diff --git a/.travis.yml b/.travis.yml
index 83ba25a3..5f20cb78 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -17,5 +17,9 @@ matrix:
     python: "3.6"
     install: pip install --quiet -r requirements_tests.txt
     env: TEST_SUITE="unittest"
+  - name: "Unit tests"
+    python: "3.6"
+    install: pip install --quiet -r requirements_tests.txt
+    env: TEST_SUITE="testscripts"
 
 script: ./.travis/script
diff --git a/.travis/script b/.travis/script
index 76d74aea..c793d9e7 100755
--- a/.travis/script
+++ b/.travis/script
@@ -14,9 +14,15 @@ if [[ "$TEST_SUITE" == "unittest" ]]; then
   pushd tts_namespace
   nosetests TTS.speaker_encoder.tests --nocapture
   nosetests TTS.vocoder.tests --nocapture
-  nosetests TTS.tests --nocapture
-  nosetests TTS.tf.tests --nocapture
+  nosetests TTS.tts.tests --nocapture
+  nosetests TTS.tts.tf.tests --nocapture
   popd
-  # Test server package
-  ./tests/test_server_package.sh
+fi
+
+if [[ "$TEST_SUITE" == "testscripts" ]]; then
+  # Test server package
+  ./tts/tests/test_server_package.sh
+  # test model training scripts
+  ./tts/tests/test_tts_train.sh
+  ./vocoder/tests/test_vocoder_train.sh
 fi
diff --git a/compute_statistics.py b/compute_statistics.py
deleted file mode 100755
index 399ae512..00000000
--- a/compute_statistics.py
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import os
-import argparse
-
-import numpy as np
-from tqdm import tqdm
-
-from TTS.datasets.preprocess import load_meta_data
-from TTS.utils.io import load_config
-from TTS.utils.audio import AudioProcessor
-
-def main():
-    """Run preprocessing process."""
-    parser = argparse.ArgumentParser(
-        description="Compute mean and variance of spectrogtram features.")
-    parser.add_argument("--config_path", type=str, required=True,
-                        help="TTS config file path to define audio processin parameters.")
-    parser.add_argument("--out_path", default=None, type=str,
-                        help="directory to save the output file.")
-    args = parser.parse_args()
-
-    # load config
-    CONFIG = load_config(args.config_path)
-    CONFIG.audio['signal_norm'] = False  # do not apply earlier normalization
-    CONFIG.audio['stats_path'] = None  # discard pre-defined stats
-
-    # load audio processor
-    ap = AudioProcessor(**CONFIG.audio)
-
-    # load the meta data of target dataset
-    dataset_items = load_meta_data(CONFIG.datasets)[0]  # take only train data
-    print(f" > There are {len(dataset_items)} files.")
-
-    mel_sum = 0
-    mel_square_sum = 0
-    linear_sum = 0
-    linear_square_sum = 0
-    N = 0
-    for item in tqdm(dataset_items):
-        # compute features
-        wav = ap.load_wav(item[1])
-        linear = ap.spectrogram(wav)
-        mel = ap.melspectrogram(wav)
-
-        # compute stats
-        N += mel.shape[1]
-        mel_sum += mel.sum(1)
-        linear_sum += linear.sum(1)
-        mel_square_sum += (mel ** 2).sum(axis=1)
-        linear_square_sum += (linear ** 2).sum(axis=1)
-
-    mel_mean = mel_sum / N
-    mel_scale = np.sqrt(mel_square_sum / N - mel_mean ** 2)
-    linear_mean = linear_sum / N
-    linear_scale = np.sqrt(linear_square_sum / N - linear_mean ** 2)
-
-    output_file_path = os.path.join(args.out_path, "scale_stats.npy")
-    stats = {}
-    stats['mel_mean'] = mel_mean
-    stats['mel_std'] = mel_scale
-    stats['linear_mean'] = linear_mean
-    stats['linear_std'] = linear_scale
-
-    print(f' > Avg mel spec mean: {mel_mean.mean()}')
-    print(f' > Avg mel spec scale: {mel_scale.mean()}')
-    print(f' > Avg linear spec mean: {linear_mean.mean()}')
-    print(f' > Avg lienar spec scale: {linear_scale.mean()}')
-
-    # set default config values for mean-var scaling
-    CONFIG.audio['stats_path'] = output_file_path
-    CONFIG.audio['signal_norm'] = True
-    # remove redundant values
-    del CONFIG.audio['max_norm']
-    del CONFIG.audio['min_level_db']
-    del CONFIG.audio['symmetric_norm']
-    del CONFIG.audio['clip_norm']
-    stats['audio_config'] = CONFIG.audio
-    np.save(output_file_path, stats, allow_pickle=True)
-    print(f' > scale_stats.npy is saved to {output_file_path}')
-
-
-if __name__ == "__main__":
-    main()
diff --git a/datasets/TTSDataset.py b/datasets/TTSDataset.py
deleted file mode 100644
index 7fe966d7..00000000
--- a/datasets/TTSDataset.py
+++ /dev/null
@@ -1,240 +0,0 @@
-import os
-import numpy as np
-import collections
-import torch
-import random
-from torch.utils.data import Dataset
-
-from TTS.utils.text import text_to_sequence, phoneme_to_sequence, pad_with_eos_bos
-from TTS.utils.data import prepare_data, prepare_tensor, prepare_stop_target
-
-
-class MyDataset(Dataset):
-    def __init__(self,
-                 outputs_per_step,
-                 text_cleaner,
-                 compute_linear_spec,
-                 ap,
-                 meta_data,
-                 tp=None,
-                 batch_group_size=0,
-                 min_seq_len=0,
-                 max_seq_len=float("inf"),
-                 use_phonemes=True,
-                 phoneme_cache_path=None,
-                 phoneme_language="en-us",
-                 enable_eos_bos=False,
-                 verbose=False):
-        """
-        Args:
-            outputs_per_step (int): number of time frames predicted per step.
-            text_cleaner (str): text cleaner used for the dataset.
-            compute_linear_spec (bool): compute linear spectrogram if True.
-            ap (TTS.utils.AudioProcessor): audio processor object.
-            meta_data (list): list of dataset instances.
-            batch_group_size (int): (0) range of batch randomization after sorting
-                sequences by length.
-            min_seq_len (int): (0) minimum sequence length to be processed
-                by the loader.
-            max_seq_len (int): (float("inf")) maximum sequence length.
-            use_phonemes (bool): (true) if true, text converted to phonemes.
-            phoneme_cache_path (str): path to cache phoneme features.
-            phoneme_language (str): one the languages from
-                https://github.com/bootphon/phonemizer#languages
-            enable_eos_bos (bool): enable end of sentence and beginning of sentences characters.
-            verbose (bool): print diagnostic information.
-        """
-        self.batch_group_size = batch_group_size
-        self.items = meta_data
-        self.outputs_per_step = outputs_per_step
-        self.sample_rate = ap.sample_rate
-        self.cleaners = text_cleaner
-        self.compute_linear_spec = compute_linear_spec
-        self.min_seq_len = min_seq_len
-        self.max_seq_len = max_seq_len
-        self.ap = ap
-        self.tp = tp
-        self.use_phonemes = use_phonemes
-        self.phoneme_cache_path = phoneme_cache_path
-        self.phoneme_language = phoneme_language
-        self.enable_eos_bos = enable_eos_bos
-        self.verbose = verbose
-        if use_phonemes and not os.path.isdir(phoneme_cache_path):
-            os.makedirs(phoneme_cache_path, exist_ok=True)
-        if self.verbose:
-            print("\n > DataLoader initialization")
-            print(" | > Use phonemes: {}".format(self.use_phonemes))
-            if use_phonemes:
-                print("   | > phoneme language: {}".format(phoneme_language))
-            print(" | > Number of instances : {}".format(len(self.items)))
-        self.sort_items()
-
-    def load_wav(self, filename):
-        audio = self.ap.load_wav(filename)
-        return audio
-
-    @staticmethod
-    def load_np(filename):
-        data = np.load(filename).astype('float32')
-        return data
-
-    def _generate_and_cache_phoneme_sequence(self, text, cache_path):
-        """generate a phoneme sequence from text.
-        since the usage is for subsequent caching, we never add bos and
-        eos chars here. Instead we add those dynamically later; based on the
-        config option."""
-        phonemes = phoneme_to_sequence(text, [self.cleaners],
-                                       language=self.phoneme_language,
-                                       enable_eos_bos=False,
-                                       tp=self.tp)
-        phonemes = np.asarray(phonemes, dtype=np.int32)
-        np.save(cache_path, phonemes)
-        return phonemes
-
-    def _load_or_generate_phoneme_sequence(self, wav_file, text):
-        file_name = os.path.splitext(os.path.basename(wav_file))[0]
-        cache_path = os.path.join(self.phoneme_cache_path,
-                                  file_name + '_phoneme.npy')
-        try:
-            phonemes = np.load(cache_path)
-        except FileNotFoundError:
-            phonemes = self._generate_and_cache_phoneme_sequence(text,
-                                                                 cache_path)
-        except (ValueError, IOError):
-            print(" > ERROR: failed loading phonemes for {}. "
-                  "Recomputing.".format(wav_file))
-            phonemes = self._generate_and_cache_phoneme_sequence(text,
-                                                                 cache_path)
-        if self.enable_eos_bos:
-            phonemes = pad_with_eos_bos(phonemes, tp=self.tp)
-            phonemes = np.asarray(phonemes, dtype=np.int32)
-        return phonemes
-
-    def load_data(self, idx):
-        text, wav_file, speaker_name = self.items[idx]
-        wav = np.asarray(self.load_wav(wav_file), dtype=np.float32)
-
-        if self.use_phonemes:
-            text = self._load_or_generate_phoneme_sequence(wav_file, text)
-        else:
-            text = np.asarray(
-                text_to_sequence(text, [self.cleaners], tp=self.tp), dtype=np.int32)
-
-        assert text.size > 0, self.items[idx][1]
-        assert wav.size > 0, self.items[idx][1]
-
-        sample = {
-            'text': text,
-            'wav': wav,
-            'item_idx': self.items[idx][1],
-            'speaker_name': speaker_name
-        }
-        return sample
-
-    def sort_items(self):
-        r"""Sort instances based on text length in ascending order"""
-        lengths = np.array([len(ins[0]) for ins in self.items])
-
-        idxs = np.argsort(lengths)
-        new_items = []
-        ignored = []
-        for i, idx in enumerate(idxs):
-            length = lengths[idx]
-            if length < self.min_seq_len or length > self.max_seq_len:
-                ignored.append(idx)
-            else:
-                new_items.append(self.items[idx])
-        # shuffle batch groups
-        if self.batch_group_size > 0:
-            for i in range(len(new_items) // self.batch_group_size):
-                offset = i * self.batch_group_size
-                end_offset = offset + self.batch_group_size
-                temp_items = new_items[offset:end_offset]
-                random.shuffle(temp_items)
-                new_items[offset:end_offset] = temp_items
-        self.items = new_items
-
-        if self.verbose:
-            print(" | > Max length sequence: {}".format(np.max(lengths)))
-            print(" | > Min length sequence: {}".format(np.min(lengths)))
-            print(" | > Avg length sequence: {}".format(np.mean(lengths)))
-            print(" | > Num. instances discarded by max-min (max={}, min={}) seq limits: {}".format(
-                self.max_seq_len, self.min_seq_len, len(ignored)))
-            print(" | > Batch group size: {}.".format(self.batch_group_size))
-
-    def __len__(self):
-        return len(self.items)
-
-    def __getitem__(self, idx):
-        return self.load_data(idx)
-
-    def collate_fn(self, batch):
-        r"""
-            Perform preprocessing and create a final data batch:
-            1. Sort batch instances by text-length
-            2. Convert Audio signal to Spectrograms.
-            3. PAD sequences wrt r.
-            4. Load to Torch.
-        """
-
-        # Puts each data field into a tensor with outer dimension batch size
-        if isinstance(batch[0], collections.Mapping):
-
-            text_lenghts = np.array([len(d["text"]) for d in batch])
-
-            # sort items with text input length for RNN efficiency
-            text_lenghts, ids_sorted_decreasing = torch.sort(
-                torch.LongTensor(text_lenghts), dim=0, descending=True)
-
-            wav = [batch[idx]['wav'] for idx in ids_sorted_decreasing]
-            item_idxs = [
-                batch[idx]['item_idx'] for idx in ids_sorted_decreasing
-            ]
-            text = [batch[idx]['text'] for idx in ids_sorted_decreasing]
-            speaker_name = [batch[idx]['speaker_name']
-                            for idx in ids_sorted_decreasing]
-
-            # compute features
-            mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
-
-            mel_lengths = [m.shape[1] for m in mel]
-
-            # compute 'stop token' targets
-            stop_targets = [
-                np.array([0.] * (mel_len - 1) + [1.]) for mel_len in mel_lengths
-            ]
-
-            # PAD stop targets
-            stop_targets = prepare_stop_target(stop_targets,
-                                               self.outputs_per_step)
-
-            # PAD sequences with longest instance in the batch
-            text = prepare_data(text).astype(np.int32)
-
-            # PAD features with longest instance
-            mel = prepare_tensor(mel, self.outputs_per_step)
-
-            # B x D x T --> B x T x D
-            mel = mel.transpose(0, 2, 1)
-
-            # convert things to pytorch
-            text_lenghts = torch.LongTensor(text_lenghts)
-            text = torch.LongTensor(text)
-            mel = torch.FloatTensor(mel).contiguous()
-            mel_lengths = torch.LongTensor(mel_lengths)
-            stop_targets = torch.FloatTensor(stop_targets)
-
-            # compute linear spectrogram
-            if self.compute_linear_spec:
-                linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
-                linear = prepare_tensor(linear, self.outputs_per_step)
-                linear = linear.transpose(0, 2, 1)
-                assert mel.shape[1] == linear.shape[1]
-                linear = torch.FloatTensor(linear).contiguous()
-            else:
-                linear = None
-            return text, text_lenghts, speaker_name, linear, mel, mel_lengths, \
-                   stop_targets, item_idxs
-
-        raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
-                         found {}".format(type(batch[0]))))
diff --git a/datasets/preprocess.py b/datasets/preprocess.py
deleted file mode 100644
index e8700c6b..00000000
--- a/datasets/preprocess.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import os
-from glob import glob
-import re
-import sys
-from TTS.utils.generic_utils import split_dataset
-
-
-def load_meta_data(datasets):
-    meta_data_train_all = []
-    meta_data_eval_all = []
-    for dataset in datasets:
-        name = dataset['name']
-        root_path = dataset['path']
-        meta_file_train = dataset['meta_file_train']
-        meta_file_val = dataset['meta_file_val']
-        preprocessor = get_preprocessor_by_name(name)
-
-        meta_data_train = preprocessor(root_path, meta_file_train)
-        if meta_file_val is None:
-            meta_data_eval, meta_data_train = split_dataset(meta_data_train)
-        else:
-            meta_data_eval = preprocessor(root_path, meta_file_val)
-        meta_data_train_all += meta_data_train
-        meta_data_eval_all += meta_data_eval
-    return meta_data_train_all, meta_data_eval_all
-
-
-def get_preprocessor_by_name(name):
-    """Returns the respective preprocessing function."""
-    thismodule = sys.modules[__name__]
-    return getattr(thismodule, name.lower())
-
-
-def tweb(root_path, meta_file):
-    """Normalize TWEB dataset.
-    https://www.kaggle.com/bryanpark/the-world-english-bible-speech-dataset
-    """
-    txt_file = os.path.join(root_path, meta_file)
-    items = []
-    speaker_name = "tweb"
-    with open(txt_file, 'r') as ttf:
-        for line in ttf:
-            cols = line.split('\t')
-            wav_file = os.path.join(root_path, cols[0] + '.wav')
-            text = cols[1]
-            items.append([text, wav_file, speaker_name])
-    return items
-
-
-# def kusal(root_path, meta_file):
-#     txt_file = os.path.join(root_path, meta_file)
-#     texts = []
-#     wavs = []
-#     with open(txt_file, "r", encoding="utf8") as f:
-#         frames = [
-#             line.split('\t') for line in f
-#             if line.split('\t')[0] in self.wav_files_dict.keys()
-#         ]
-#     # TODO: code the rest
-#     return  {'text': texts, 'wavs': wavs}
-
-
-def mozilla(root_path, meta_file):
-    """Normalizes Mozilla meta data files to TTS format"""
-    txt_file = os.path.join(root_path, meta_file)
-    items = []
-    speaker_name = "mozilla"
-    with open(txt_file, 'r') as ttf:
-        for line in ttf:
-            cols = line.split('|')
-            wav_file = cols[1].strip()
-            text = cols[0].strip()
-            wav_file = os.path.join(root_path, "wavs", wav_file)
-            items.append([text, wav_file, speaker_name])
-    return items
-
-
-def mozilla_de(root_path, meta_file):
-    """Normalizes Mozilla meta data files to TTS format"""
-    txt_file = os.path.join(root_path, meta_file)
-    items = []
-    speaker_name = "mozilla"
-    with open(txt_file, 'r', encoding="ISO 8859-1") as ttf:
-        for line in ttf:
-            cols = line.strip().split('|')
-            wav_file = cols[0].strip()
-            text = cols[1].strip()
-            folder_name = f"BATCH_{wav_file.split('_')[0]}_FINAL"
-            wav_file = os.path.join(root_path, folder_name, wav_file)
-            items.append([text, wav_file, speaker_name])
-    return items
-
-
-def mailabs(root_path, meta_files=None):
-    """Normalizes M-AI-Labs meta data files to TTS format"""
-    speaker_regex = re.compile("by_book/(male|female)/(?P<speaker_name>[^/]+)/")
-    if meta_files is None:
-        csv_files = glob(root_path+"/**/metadata.csv", recursive=True)
-    else:
-        csv_files = meta_files
-    # meta_files = [f.strip() for f in meta_files.split(",")]
-    items = []
-    for csv_file in csv_files:
-        txt_file = os.path.join(root_path, csv_file)
-        folder = os.path.dirname(txt_file)
-        # determine speaker based on folder structure...
-        speaker_name_match = speaker_regex.search(txt_file)
-        if speaker_name_match is None:
-            continue
-        speaker_name = speaker_name_match.group("speaker_name")
-        print(" | > {}".format(csv_file))
-        with open(txt_file, 'r') as ttf:
-            for line in ttf:
-                cols = line.split('|')
-                if meta_files is None:
-                    wav_file = os.path.join(folder, 'wavs', cols[0] + '.wav')
-                else:
-                    wav_file = os.path.join(root_path, folder.replace("metadata.csv", ""), 'wavs', cols[0] + '.wav')
-                if os.path.isfile(wav_file):
-                    text = cols[1].strip()
-                    items.append([text, wav_file, speaker_name])
-                else:
-                    raise RuntimeError("> File %s does not exist!"%(wav_file))
-    return items
-
-
-def ljspeech(root_path, meta_file):
-    """Normalizes the Nancy meta data file to TTS format"""
-    txt_file = os.path.join(root_path, meta_file)
-    items = []
-    speaker_name = "ljspeech"
-    with open(txt_file, 'r') as ttf:
-        for line in ttf:
-            cols = line.split('|')
-            wav_file = os.path.join(root_path, 'wavs', cols[0] + '.wav')
-            text = cols[1]
-            items.append([text, wav_file, speaker_name])
-    return items
-
-
-def nancy(root_path, meta_file):
-    """Normalizes the Nancy meta data file to TTS format"""
-    txt_file = os.path.join(root_path, meta_file)
-    items = []
-    speaker_name = "nancy"
-    with open(txt_file, 'r') as ttf:
-        for line in ttf:
-            utt_id = line.split()[1]
-            text = line[line.find('"') + 1:line.rfind('"') - 1]
-            wav_file = os.path.join(root_path, "wavn", utt_id + ".wav")
-            items.append([text, wav_file, speaker_name])
-    return items
-
-
-def common_voice(root_path, meta_file):
-    """Normalize the common voice meta data file to TTS format."""
-    txt_file = os.path.join(root_path, meta_file)
-    items = []
-    with open(txt_file, 'r') as ttf:
-        for line in ttf:
-            if line.startswith("client_id"):
-                continue
-            cols = line.split("\t")
-            text = cols[2]
-            speaker_name = cols[0]
-            wav_file = os.path.join(root_path, "clips", cols[1] + ".wav")
-            items.append([text, wav_file, speaker_name])
-    return items
-
-
-def libri_tts(root_path, meta_files=None):
-    """https://ai.google/tools/datasets/libri-tts/"""
-    items = []
-    if meta_files is None:
-        meta_files = glob(f"{root_path}/**/*trans.tsv", recursive=True)
-    for meta_file in meta_files:
-        _meta_file = os.path.basename(meta_file).split('.')[0]
-        speaker_name = _meta_file.split('_')[0]
-        chapter_id = _meta_file.split('_')[1]
-        _root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
-        with open(meta_file, 'r') as ttf:
-            for line in ttf:
-                cols = line.split('\t')
-                wav_file = os.path.join(_root_path, cols[0] + '.wav')
-                text = cols[1]
-                items.append([text, wav_file, speaker_name])
-    for item in items:
-        assert os.path.exists(item[1]), f" [!] wav files don't exist - {item[1]}"
-    return items
-
-
-def custom_turkish(root_path, meta_file):
-    txt_file = os.path.join(root_path, meta_file)
-    items = []
-    speaker_name = "turkish-female"
-    skipped_files = []
-    with open(txt_file, 'r', encoding='utf-8') as ttf:
-        for line in ttf:
-            cols = line.split('|')
-            wav_file = os.path.join(root_path, 'wavs', cols[0].strip() + '.wav')
-            if not os.path.exists(wav_file):
-                skipped_files.append(wav_file)
-                continue
-            text = cols[1].strip()
-            items.append([text, wav_file, speaker_name])
-    print(f" [!] {len(skipped_files)} files skipped. They don't exist...")
-    return items
diff --git a/distribute.py b/distribute.py
deleted file mode 100644
index b0fc8b07..00000000
--- a/distribute.py
+++ /dev/null
@@ -1,178 +0,0 @@
-# edited from https://github.com/fastai/imagenet-fast/blob/master/imagenet_nv/distributed.py
-import os, sys
-import math
-import time
-import subprocess
-import argparse
-import torch
-import torch.distributed as dist
-from torch.utils.data.sampler import Sampler
-from torch.autograd import Variable
-from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
-from TTS.utils.generic_utils import create_experiment_folder
-
-
-class DistributedSampler(Sampler):
-    """
-    Non shuffling Distributed Sampler
-    """
-
-    def __init__(self, dataset, num_replicas=None, rank=None):
-        super(DistributedSampler, self).__init__(dataset)
-        if num_replicas is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            num_replicas = dist.get_world_size()
-        if rank is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            rank = dist.get_rank()
-        self.dataset = dataset
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.epoch = 0
-        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
-        self.total_size = self.num_samples * self.num_replicas
-
-    def __iter__(self):
-        indices = torch.arange(len(self.dataset)).tolist()
-
-        # add extra samples to make it evenly divisible
-        indices += indices[:(self.total_size - len(indices))]
-        assert len(indices) == self.total_size
-
-        # subsample
-        indices = indices[self.rank:self.total_size:self.num_replicas]
-        assert len(indices) == self.num_samples
-
-        return iter(indices)
-
-    def __len__(self):
-        return self.num_samples
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch
-
-
-def reduce_tensor(tensor, num_gpus):
-    rt = tensor.clone()
-    dist.all_reduce(rt, op=dist.reduce_op.SUM)
-    rt /= num_gpus
-    return rt
-
-
-def init_distributed(rank, num_gpus, group_name, dist_backend, dist_url):
-    assert torch.cuda.is_available(), "Distributed mode requires CUDA."
-
-    # Set cuda device so everything is done on the right GPU.
-    torch.cuda.set_device(rank % torch.cuda.device_count())
-
-    # Initialize distributed communication
-    dist.init_process_group(
-        dist_backend,
-        init_method=dist_url,
-        world_size=num_gpus,
-        rank=rank,
-        group_name=group_name)
-
-
-def apply_gradient_allreduce(module):
-
-    # sync model parameters
-    for p in module.state_dict().values():
-        if not torch.is_tensor(p):
-            continue
-        dist.broadcast(p, 0)
-
-    def allreduce_params():
-        if module.needs_reduction:
-            module.needs_reduction = False
-            # bucketing params based on value types
-            buckets = {}
-            for param in module.parameters():
-                if param.requires_grad and param.grad is not None:
-                    tp = type(param.data)
-                    if tp not in buckets:
-                        buckets[tp] = []
-                    buckets[tp].append(param)
-            for tp in buckets:
-                bucket = buckets[tp]
-                grads = [param.grad.data for param in bucket]
-                coalesced = _flatten_dense_tensors(grads)
-                dist.all_reduce(coalesced, op=dist.reduce_op.SUM)
-                coalesced /= dist.get_world_size()
-                for buf, synced in zip(
-                        grads, _unflatten_dense_tensors(coalesced, grads)):
-                    buf.copy_(synced)
-
-    for param in list(module.parameters()):
-
-        def allreduce_hook(*_):
-            Variable._execution_engine.queue_callback(allreduce_params)
-
-        if param.requires_grad:
-            param.register_hook(allreduce_hook)
-
-    def set_needs_reduction(self, *_):
-        self.needs_reduction = True
-
-    module.register_forward_hook(set_needs_reduction)
-    return module
-
-
-def main():
-    """
-    Call train.py as a new process and pass command arguments
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--continue_path',
-        type=str,
-        help='Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
-        default='',
-        required='--config_path' not in sys.argv)
-    parser.add_argument(
-        '--restore_path',
-        type=str,
-        help='Model file to be restored. Use to finetune a model.',
-        default='')
-    parser.add_argument(
-        '--config_path',
-        type=str,
-        help='Path to config file for training.',
-        required='--continue_path' not in sys.argv
-    )
-    args = parser.parse_args()
-
-    # OUT_PATH = create_experiment_folder(CONFIG.output_path, CONFIG.run_name,
-                                        # True)
-    # stdout_path = os.path.join(OUT_PATH, "process_stdout/")
-
-    num_gpus = torch.cuda.device_count()
-    group_id = time.strftime("%Y_%m_%d-%H%M%S")
-
-    # set arguments for train.py
-    command = ['train.py']
-    command.append('--continue_path={}'.format(args.continue_path))
-    command.append('--restore_path={}'.format(args.restore_path))
-    command.append('--config_path={}'.format(args.config_path))
-    command.append('--group_id=group_{}'.format(group_id))
-    command.append('')
-
-    # run processes
-    processes = []
-    for i in range(num_gpus):
-        my_env = os.environ.copy()
-        my_env["PYTHON_EGG_CACHE"] = "/tmp/tmp{}".format(i)
-        command[-1] = '--rank={}'.format(i)
-        stdout = None if i == 0 else open(os.devnull, 'w')
-        p = subprocess.Popen(['python3'] + command, stdout=stdout, env=my_env)
-        processes.append(p)
-        print(command)
-
-    for p in processes:
-        p.wait()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/layers/__init__.py b/layers/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/layers/common_layers.py b/layers/common_layers.py
deleted file mode 100644
index b7d02c2d..00000000
--- a/layers/common_layers.py
+++ /dev/null
@@ -1,389 +0,0 @@
-import torch
-from torch import nn
-from torch.autograd import Variable
-from torch.nn import functional as F
-
-
-class Linear(nn.Module):
-    def __init__(self,
-                 in_features,
-                 out_features,
-                 bias=True,
-                 init_gain='linear'):
-        super(Linear, self).__init__()
-        self.linear_layer = torch.nn.Linear(
-            in_features, out_features, bias=bias)
-        self._init_w(init_gain)
-
-    def _init_w(self, init_gain):
-        torch.nn.init.xavier_uniform_(
-            self.linear_layer.weight,
-            gain=torch.nn.init.calculate_gain(init_gain))
-
-    def forward(self, x):
-        return self.linear_layer(x)
-
-
-class LinearBN(nn.Module):
-    def __init__(self,
-                 in_features,
-                 out_features,
-                 bias=True,
-                 init_gain='linear'):
-        super(LinearBN, self).__init__()
-        self.linear_layer = torch.nn.Linear(
-            in_features, out_features, bias=bias)
-        self.batch_normalization = nn.BatchNorm1d(out_features, momentum=0.1, eps=1e-5)
-        self._init_w(init_gain)
-
-    def _init_w(self, init_gain):
-        torch.nn.init.xavier_uniform_(
-            self.linear_layer.weight,
-            gain=torch.nn.init.calculate_gain(init_gain))
-
-    def forward(self, x):
-        out = self.linear_layer(x)
-        if len(out.shape) == 3:
-            out = out.permute(1, 2, 0)
-        out = self.batch_normalization(out)
-        if len(out.shape) == 3:
-            out = out.permute(2, 0, 1)
-        return out
-
-
-class Prenet(nn.Module):
-    def __init__(self,
-                 in_features,
-                 prenet_type="original",
-                 prenet_dropout=True,
-                 out_features=[256, 256],
-                 bias=True):
-        super(Prenet, self).__init__()
-        self.prenet_type = prenet_type
-        self.prenet_dropout = prenet_dropout
-        in_features = [in_features] + out_features[:-1]
-        if prenet_type == "bn":
-            self.linear_layers = nn.ModuleList([
-                LinearBN(in_size, out_size, bias=bias)
-                for (in_size, out_size) in zip(in_features, out_features)
-            ])
-        elif prenet_type == "original":
-            self.linear_layers = nn.ModuleList([
-                Linear(in_size, out_size, bias=bias)
-                for (in_size, out_size) in zip(in_features, out_features)
-            ])
-
-    def forward(self, x):
-        for linear in self.linear_layers:
-            if self.prenet_dropout:
-                x = F.dropout(F.relu(linear(x)), p=0.5, training=self.training)
-            else:
-                x = F.relu(linear(x))
-        return x
-
-
-####################
-# ATTENTION MODULES
-####################
-
-
-class LocationLayer(nn.Module):
-    def __init__(self,
-                 attention_dim,
-                 attention_n_filters=32,
-                 attention_kernel_size=31):
-        super(LocationLayer, self).__init__()
-        self.location_conv1d = nn.Conv1d(
-            in_channels=2,
-            out_channels=attention_n_filters,
-            kernel_size=attention_kernel_size,
-            stride=1,
-            padding=(attention_kernel_size - 1) // 2,
-            bias=False)
-        self.location_dense = Linear(
-            attention_n_filters, attention_dim, bias=False, init_gain='tanh')
-
-    def forward(self, attention_cat):
-        processed_attention = self.location_conv1d(attention_cat)
-        processed_attention = self.location_dense(
-            processed_attention.transpose(1, 2))
-        return processed_attention
-
-
-class GravesAttention(nn.Module):
-    """ Discretized Graves attention:
-        - https://arxiv.org/abs/1910.10288
-        - https://arxiv.org/pdf/1906.01083.pdf
-    """
-    COEF = 0.3989422917366028  # numpy.sqrt(1/(2*numpy.pi))
-
-    def __init__(self, query_dim, K):
-        super(GravesAttention, self).__init__()
-        self._mask_value = 1e-8
-        self.K = K
-        # self.attention_alignment = 0.05
-        self.eps = 1e-5
-        self.J = None
-        self.N_a = nn.Sequential(
-            nn.Linear(query_dim, query_dim, bias=True),
-            nn.ReLU(),
-            nn.Linear(query_dim, 3*K, bias=True))
-        self.attention_weights = None
-        self.mu_prev = None
-        self.init_layers()
-
-    def init_layers(self):
-        torch.nn.init.constant_(self.N_a[2].bias[(2*self.K):(3*self.K)], 1.)  # bias mean
-        torch.nn.init.constant_(self.N_a[2].bias[self.K:(2*self.K)], 10)  # bias std
-
-    def init_states(self, inputs):
-        if self.J is None or inputs.shape[1]+1 > self.J.shape[-1]:
-            self.J = torch.arange(0, inputs.shape[1]+2.0).to(inputs.device) + 0.5
-        self.attention_weights = torch.zeros(inputs.shape[0], inputs.shape[1]).to(inputs.device)
-        self.mu_prev = torch.zeros(inputs.shape[0], self.K).to(inputs.device)
-
-    # pylint: disable=R0201
-    # pylint: disable=unused-argument
-    def preprocess_inputs(self, inputs):
-        return None
-
-    def forward(self, query, inputs, processed_inputs, mask):
-        """
-        shapes:
-            query: B x D_attention_rnn
-            inputs: B x T_in x D_encoder
-            processed_inputs: place_holder
-            mask: B x T_in
-        """
-        gbk_t = self.N_a(query)
-        gbk_t = gbk_t.view(gbk_t.size(0), -1, self.K)
-
-        # attention model parameters
-        # each B x K
-        g_t = gbk_t[:, 0, :]
-        b_t = gbk_t[:, 1, :]
-        k_t = gbk_t[:, 2, :]
-
-        # dropout to decorrelate attention heads
-        g_t = torch.nn.functional.dropout(g_t, p=0.5, training=self.training)
-
-        # attention GMM parameters
-        sig_t = torch.nn.functional.softplus(b_t) + self.eps
-
-        mu_t = self.mu_prev + torch.nn.functional.softplus(k_t)
-        g_t = torch.softmax(g_t, dim=-1) + self.eps
-
-        j = self.J[:inputs.size(1)+1]
-
-        # attention weights
-        phi_t = g_t.unsqueeze(-1) * (1 / (1 + torch.sigmoid((mu_t.unsqueeze(-1) - j) / sig_t.unsqueeze(-1))))
-
-        # discritize attention weights
-        alpha_t = torch.sum(phi_t, 1)
-        alpha_t = alpha_t[:, 1:] - alpha_t[:, :-1]
-        alpha_t[alpha_t == 0] = 1e-8
-
-        # apply masking
-        if mask is not None:
-            alpha_t.data.masked_fill_(~mask, self._mask_value)
-
-        context = torch.bmm(alpha_t.unsqueeze(1), inputs).squeeze(1)
-        self.attention_weights = alpha_t
-        self.mu_prev = mu_t
-        return context
-
-
-class OriginalAttention(nn.Module):
-    """Following the methods proposed here:
-        - https://arxiv.org/abs/1712.05884
-        - https://arxiv.org/abs/1807.06736 + state masking at inference
-        - Using sigmoid instead of softmax normalization
-        - Attention windowing at inference time
-    """
-    # Pylint gets confused by PyTorch conventions here
-    #pylint: disable=attribute-defined-outside-init
-    def __init__(self, query_dim, embedding_dim, attention_dim,
-                 location_attention, attention_location_n_filters,
-                 attention_location_kernel_size, windowing, norm, forward_attn,
-                 trans_agent, forward_attn_mask):
-        super(OriginalAttention, self).__init__()
-        self.query_layer = Linear(
-            query_dim, attention_dim, bias=False, init_gain='tanh')
-        self.inputs_layer = Linear(
-            embedding_dim, attention_dim, bias=False, init_gain='tanh')
-        self.v = Linear(attention_dim, 1, bias=True)
-        if trans_agent:
-            self.ta = nn.Linear(
-                query_dim + embedding_dim, 1, bias=True)
-        if location_attention:
-            self.location_layer = LocationLayer(
-                attention_dim,
-                attention_location_n_filters,
-                attention_location_kernel_size,
-            )
-        self._mask_value = -float("inf")
-        self.windowing = windowing
-        self.win_idx = None
-        self.norm = norm
-        self.forward_attn = forward_attn
-        self.trans_agent = trans_agent
-        self.forward_attn_mask = forward_attn_mask
-        self.location_attention = location_attention
-
-    def init_win_idx(self):
-        self.win_idx = -1
-        self.win_back = 2
-        self.win_front = 6
-
-    def init_forward_attn(self, inputs):
-        B = inputs.shape[0]
-        T = inputs.shape[1]
-        self.alpha = torch.cat(
-            [torch.ones([B, 1]),
-             torch.zeros([B, T])[:, :-1] + 1e-7], dim=1).to(inputs.device)
-        self.u = (0.5 * torch.ones([B, 1])).to(inputs.device)
-
-    def init_location_attention(self, inputs):
-        B = inputs.shape[0]
-        T = inputs.shape[1]
-        self.attention_weights_cum = Variable(inputs.data.new(B, T).zero_())
-
-    def init_states(self, inputs):
-        B = inputs.shape[0]
-        T = inputs.shape[1]
-        self.attention_weights = Variable(inputs.data.new(B, T).zero_())
-        if self.location_attention:
-            self.init_location_attention(inputs)
-        if self.forward_attn:
-            self.init_forward_attn(inputs)
-        if self.windowing:
-            self.init_win_idx()
-
-    def preprocess_inputs(self, inputs):
-        return self.inputs_layer(inputs)
-
-    def update_location_attention(self, alignments):
-        self.attention_weights_cum += alignments
-
-    def get_location_attention(self, query, processed_inputs):
-        attention_cat = torch.cat((self.attention_weights.unsqueeze(1),
-                                   self.attention_weights_cum.unsqueeze(1)),
-                                  dim=1)
-        processed_query = self.query_layer(query.unsqueeze(1))
-        processed_attention_weights = self.location_layer(attention_cat)
-        energies = self.v(
-            torch.tanh(processed_query + processed_attention_weights +
-                       processed_inputs))
-        energies = energies.squeeze(-1)
-        return energies, processed_query
-
-    def get_attention(self, query, processed_inputs):
-        processed_query = self.query_layer(query.unsqueeze(1))
-        energies = self.v(torch.tanh(processed_query + processed_inputs))
-        energies = energies.squeeze(-1)
-        return energies, processed_query
-
-    def apply_windowing(self, attention, inputs):
-        back_win = self.win_idx - self.win_back
-        front_win = self.win_idx + self.win_front
-        if back_win > 0:
-            attention[:, :back_win] = -float("inf")
-        if front_win < inputs.shape[1]:
-            attention[:, front_win:] = -float("inf")
-        # this is a trick to solve a special problem.
-        # but it does not hurt.
-        if self.win_idx == -1:
-            attention[:, 0] = attention.max()
-        # Update the window
-        self.win_idx = torch.argmax(attention, 1).long()[0].item()
-        return attention
-
-    def apply_forward_attention(self, alignment):
-        # forward attention
-        fwd_shifted_alpha = F.pad(self.alpha[:, :-1].clone().to(alignment.device),
-                            (1, 0, 0, 0))
-        # compute transition potentials
-        alpha = ((1 - self.u) * self.alpha
-                 + self.u * fwd_shifted_alpha
-                 + 1e-8) * alignment
-        # force incremental alignment
-        if not self.training and self.forward_attn_mask:
-            _, n = fwd_shifted_alpha.max(1)
-            val, n2 = alpha.max(1)
-            for b in range(alignment.shape[0]):
-                alpha[b, n[b] + 3:] = 0
-                alpha[b, :(
-                    n[b] - 1
-                )] = 0  # ignore all previous states to prevent repetition.
-                alpha[b,
-                      (n[b] - 2
-                       )] = 0.01 * val[b]  # smoothing factor for the prev step
-        # renormalize attention weights
-        alpha = alpha / alpha.sum(dim=1, keepdim=True)
-        return alpha
-
-    def forward(self, query, inputs, processed_inputs, mask):
-        """
-        shapes:
-            query: B x D_attn_rnn
-            inputs: B x T_en x D_en
-            processed_inputs:: B x T_en x D_attn
-            mask: B x T_en
-        """
-        if self.location_attention:
-            attention, _ = self.get_location_attention(
-                query, processed_inputs)
-        else:
-            attention, _ = self.get_attention(
-                query, processed_inputs)
-        # apply masking
-        if mask is not None:
-            attention.data.masked_fill_(~mask, self._mask_value)
-        # apply windowing - only in eval mode
-        if not self.training and self.windowing:
-            attention = self.apply_windowing(attention, inputs)
-
-        # normalize attention values
-        if self.norm == "softmax":
-            alignment = torch.softmax(attention, dim=-1)
-        elif self.norm == "sigmoid":
-            alignment = torch.sigmoid(attention) / torch.sigmoid(
-                attention).sum(
-                    dim=1, keepdim=True)
-        else:
-            raise ValueError("Unknown value for attention norm type")
-
-        if self.location_attention:
-            self.update_location_attention(alignment)
-
-        # apply forward attention if enabled
-        if self.forward_attn:
-            alignment = self.apply_forward_attention(alignment)
-            self.alpha = alignment
-
-        context = torch.bmm(alignment.unsqueeze(1), inputs)
-        context = context.squeeze(1)
-        self.attention_weights = alignment
-
-        # compute transition agent
-        if self.forward_attn and self.trans_agent:
-            ta_input = torch.cat([context, query.squeeze(1)], dim=-1)
-            self.u = torch.sigmoid(self.ta(ta_input))
-        return context
-
-
-def init_attn(attn_type, query_dim, embedding_dim, attention_dim,
-              location_attention, attention_location_n_filters,
-              attention_location_kernel_size, windowing, norm, forward_attn,
-              trans_agent, forward_attn_mask, attn_K):
-    if attn_type == "original":
-        return OriginalAttention(query_dim, embedding_dim, attention_dim,
-                                 location_attention,
-                                 attention_location_n_filters,
-                                 attention_location_kernel_size, windowing,
-                                 norm, forward_attn, trans_agent,
-                                 forward_attn_mask)
-    if attn_type == "graves":
-        return GravesAttention(query_dim, attn_K)
-    raise RuntimeError(
-        " [!] Given Attention Type '{attn_type}' is not exist.")
diff --git a/layers/gst_layers.py b/layers/gst_layers.py
deleted file mode 100644
index 8058d5ed..00000000
--- a/layers/gst_layers.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class GST(nn.Module):
-    """Global Style Token Module for factorizing prosody in speech.
-
-    See https://arxiv.org/pdf/1803.09017"""
-
-    def __init__(self, num_mel, num_heads, num_style_tokens, embedding_dim):
-        super().__init__()
-        self.encoder = ReferenceEncoder(num_mel, embedding_dim)
-        self.style_token_layer = StyleTokenLayer(num_heads, num_style_tokens,
-                                                 embedding_dim)
-
-    def forward(self, inputs):
-        enc_out = self.encoder(inputs)
-        style_embed = self.style_token_layer(enc_out)
-
-        return style_embed
-
-
-class ReferenceEncoder(nn.Module):
-    """NN module creating a fixed size prosody embedding from a spectrogram.
-
-    inputs: mel spectrograms [batch_size, num_spec_frames, num_mel]
-    outputs: [batch_size, embedding_dim]
-    """
-
-    def __init__(self, num_mel, embedding_dim):
-
-        super().__init__()
-        self.num_mel = num_mel
-        filters = [1] + [32, 32, 64, 64, 128, 128]
-        num_layers = len(filters) - 1
-        convs = [
-            nn.Conv2d(
-                in_channels=filters[i],
-                out_channels=filters[i + 1],
-                kernel_size=(3, 3),
-                stride=(2, 2),
-                padding=(1, 1)) for i in range(num_layers)
-        ]
-        self.convs = nn.ModuleList(convs)
-        self.bns = nn.ModuleList([
-            nn.BatchNorm2d(num_features=filter_size)
-            for filter_size in filters[1:]
-        ])
-
-        post_conv_height = self.calculate_post_conv_height(
-            num_mel, 3, 2, 1, num_layers)
-        self.recurrence = nn.GRU(
-            input_size=filters[-1] * post_conv_height,
-            hidden_size=embedding_dim // 2,
-            batch_first=True)
-
-    def forward(self, inputs):
-        batch_size = inputs.size(0)
-        x = inputs.view(batch_size, 1, -1, self.num_mel)
-        # x: 4D tensor [batch_size, num_channels==1, num_frames, num_mel]
-        for conv, bn in zip(self.convs, self.bns):
-            x = conv(x)
-            x = bn(x)
-            x = F.relu(x)
-
-        x = x.transpose(1, 2)
-        # x: 4D tensor [batch_size, post_conv_width,
-        #               num_channels==128, post_conv_height]
-        post_conv_width = x.size(1)
-        x = x.contiguous().view(batch_size, post_conv_width, -1)
-        # x: 3D tensor [batch_size, post_conv_width,
-        #               num_channels*post_conv_height]
-        self.recurrence.flatten_parameters()
-        memory, out = self.recurrence(x)
-        # out: 3D tensor [seq_len==1, batch_size, encoding_size=128]
-
-        return out.squeeze(0)
-
-    @staticmethod
-    def calculate_post_conv_height(height, kernel_size, stride, pad,
-                                   n_convs):
-        """Height of spec after n convolutions with fixed kernel/stride/pad."""
-        for _ in range(n_convs):
-            height = (height - kernel_size + 2 * pad) // stride + 1
-        return height
-
-
-class StyleTokenLayer(nn.Module):
-    """NN Module attending to style tokens based on prosody encodings."""
-
-    def __init__(self, num_heads, num_style_tokens,
-                 embedding_dim):
-        super().__init__()
-        self.query_dim = embedding_dim // 2
-        self.key_dim = embedding_dim // num_heads
-        self.style_tokens = nn.Parameter(
-            torch.FloatTensor(num_style_tokens, self.key_dim))
-        nn.init.orthogonal_(self.style_tokens)
-        self.attention = MultiHeadAttention(
-            query_dim=self.query_dim,
-            key_dim=self.key_dim,
-            num_units=embedding_dim,
-            num_heads=num_heads)
-
-    def forward(self, inputs):
-        batch_size = inputs.size(0)
-        prosody_encoding = inputs.unsqueeze(1)
-        # prosody_encoding: 3D tensor [batch_size, 1, encoding_size==128]
-        tokens = torch.tanh(self.style_tokens) \
-            .unsqueeze(0) \
-            .expand(batch_size, -1, -1)
-        # tokens: 3D tensor [batch_size, num tokens, token embedding size]
-        style_embed = self.attention(prosody_encoding, tokens)
-
-        return style_embed
-
-
-class MultiHeadAttention(nn.Module):
-    '''
-    input:
-        query --- [N, T_q, query_dim]
-        key --- [N, T_k, key_dim]
-    output:
-        out --- [N, T_q, num_units]
-    '''
-
-    def __init__(self, query_dim, key_dim, num_units, num_heads):
-
-        super().__init__()
-        self.num_units = num_units
-        self.num_heads = num_heads
-        self.key_dim = key_dim
-
-        self.W_query = nn.Linear(
-            in_features=query_dim, out_features=num_units, bias=False)
-        self.W_key = nn.Linear(
-            in_features=key_dim, out_features=num_units, bias=False)
-        self.W_value = nn.Linear(
-            in_features=key_dim, out_features=num_units, bias=False)
-
-    def forward(self, query, key):
-        queries = self.W_query(query)  # [N, T_q, num_units]
-        keys = self.W_key(key)  # [N, T_k, num_units]
-        values = self.W_value(key)
-
-        split_size = self.num_units // self.num_heads
-        queries = torch.stack(
-            torch.split(queries, split_size, dim=2),
-            dim=0)  # [h, N, T_q, num_units/h]
-        keys = torch.stack(
-            torch.split(keys, split_size, dim=2),
-            dim=0)  # [h, N, T_k, num_units/h]
-        values = torch.stack(
-            torch.split(values, split_size, dim=2),
-            dim=0)  # [h, N, T_k, num_units/h]
-
-        # score = softmax(QK^T / (d_k ** 0.5))
-        scores = torch.matmul(queries, keys.transpose(2, 3))  # [h, N, T_q, T_k]
-        scores = scores / (self.key_dim**0.5)
-        scores = F.softmax(scores, dim=3)
-
-        # out = score * V
-        out = torch.matmul(scores, values)  # [h, N, T_q, num_units/h]
-        out = torch.cat(
-            torch.split(out, 1, dim=0),
-            dim=3).squeeze(0)  # [N, T_q, num_units]
-
-        return out
diff --git a/layers/losses.py b/layers/losses.py
deleted file mode 100644
index f7745b6e..00000000
--- a/layers/losses.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional
-from TTS.utils.generic_utils import sequence_mask
-
-
-class L1LossMasked(nn.Module):
-
-    def __init__(self, seq_len_norm):
-        super(L1LossMasked, self).__init__()
-        self.seq_len_norm = seq_len_norm
-
-    def forward(self, x, target, length):
-        """
-        Args:
-            x: A Variable containing a FloatTensor of size
-                (batch, max_len, dim) which contains the
-                unnormalized probability for each class.
-            target: A Variable containing a LongTensor of size
-                (batch, max_len, dim) which contains the index of the true
-                class for each corresponding step.
-            length: A Variable containing a LongTensor of size (batch,)
-                which contains the length of each data in a batch.
-        Returns:
-            loss: An average loss value in range [0, 1] masked by the length.
-        """
-        # mask: (batch, max_len, 1)
-        target.requires_grad = False
-        mask = sequence_mask(
-            sequence_length=length, max_len=target.size(1)).unsqueeze(2).float()
-        if self.seq_len_norm:
-            norm_w = mask / mask.sum(dim=1, keepdim=True)
-            out_weights = norm_w.div(target.shape[0] * target.shape[2])
-            mask = mask.expand_as(x)
-            loss = functional.l1_loss(
-                x * mask, target * mask, reduction='none')
-            loss = loss.mul(out_weights.to(loss.device)).sum()
-        else:
-            mask = mask.expand_as(x)
-            loss = functional.l1_loss(
-                x * mask, target * mask, reduction='sum')
-            loss = loss / mask.sum()
-        return loss
-
-
-class MSELossMasked(nn.Module):
-
-    def __init__(self, seq_len_norm):
-        super(MSELossMasked, self).__init__()
-        self.seq_len_norm = seq_len_norm
-
-    def forward(self, x, target, length):
-        """
-        Args:
-            x: A Variable containing a FloatTensor of size
-                (batch, max_len, dim) which contains the
-                unnormalized probability for each class.
-            target: A Variable containing a LongTensor of size
-                (batch, max_len, dim) which contains the index of the true
-                class for each corresponding step.
-            length: A Variable containing a LongTensor of size (batch,)
-                which contains the length of each data in a batch.
-        Returns:
-            loss: An average loss value in range [0, 1] masked by the length.
-        """
-        # mask: (batch, max_len, 1)
-        target.requires_grad = False
-        mask = sequence_mask(
-            sequence_length=length, max_len=target.size(1)).unsqueeze(2).float()
-        if self.seq_len_norm:
-            norm_w = mask / mask.sum(dim=1, keepdim=True)
-            out_weights = norm_w.div(target.shape[0] * target.shape[2])
-            mask = mask.expand_as(x)
-            loss = functional.mse_loss(
-                x * mask, target * mask, reduction='none')
-            loss = loss.mul(out_weights.to(loss.device)).sum()
-        else:
-            mask = mask.expand_as(x)
-            loss = functional.mse_loss(
-                x * mask, target * mask, reduction='sum')
-            loss = loss / mask.sum()
-        return loss
-
-
-class AttentionEntropyLoss(nn.Module):
-    # pylint: disable=R0201
-    def forward(self, align):
-        """
-        Forces attention to be more decisive by penalizing
-        soft attention weights
-
-        TODO: arguments
-        TODO: unit_test
-        """
-        entropy = torch.distributions.Categorical(probs=align).entropy()
-        loss = (entropy / np.log(align.shape[1])).mean()
-        return loss
-
-
-class BCELossMasked(nn.Module):
-
-    def __init__(self, pos_weight):
-        super(BCELossMasked, self).__init__()
-        self.pos_weight = pos_weight
-
-    def forward(self, x, target, length):
-        """
-        Args:
-            x: A Variable containing a FloatTensor of size
-                (batch, max_len) which contains the
-                unnormalized probability for each class.
-            target: A Variable containing a LongTensor of size
-                (batch, max_len) which contains the index of the true
-                class for each corresponding step.
-            length: A Variable containing a LongTensor of size (batch,)
-                which contains the length of each data in a batch.
-        Returns:
-            loss: An average loss value in range [0, 1] masked by the length.
-        """
-        # mask: (batch, max_len, 1)
-        target.requires_grad = False
-        mask = sequence_mask(sequence_length=length, max_len=target.size(1)).float()
-        loss = functional.binary_cross_entropy_with_logits(
-            x * mask, target * mask, pos_weight=self.pos_weight, reduction='sum')
-        loss = loss / mask.sum()
-        return loss
-
-
-class GuidedAttentionLoss(torch.nn.Module):
-    def __init__(self, sigma=0.4):
-        super(GuidedAttentionLoss, self).__init__()
-        self.sigma = sigma
-
-    def _make_ga_masks(self, ilens, olens):
-        B = len(ilens)
-        max_ilen = max(ilens)
-        max_olen = max(olens)
-        ga_masks = torch.zeros((B, max_olen, max_ilen))
-        for idx, (ilen, olen) in enumerate(zip(ilens, olens)):
-            ga_masks[idx, :olen, :ilen] = self._make_ga_mask(ilen, olen, self.sigma)
-        return ga_masks
-
-    def forward(self, att_ws, ilens, olens):
-        ga_masks = self._make_ga_masks(ilens, olens).to(att_ws.device)
-        seq_masks = self._make_masks(ilens, olens).to(att_ws.device)
-        losses = ga_masks * att_ws
-        loss = torch.mean(losses.masked_select(seq_masks))
-        return loss
-
-    @staticmethod
-    def _make_ga_mask(ilen, olen, sigma):
-        grid_x, grid_y = torch.meshgrid(torch.arange(olen), torch.arange(ilen))
-        grid_x, grid_y = grid_x.float(), grid_y.float()
-        return 1.0 - torch.exp(-(grid_y / ilen - grid_x / olen) ** 2 / (2 * (sigma ** 2)))
-
-    @staticmethod
-    def _make_masks(ilens, olens):
-        in_masks = sequence_mask(ilens)
-        out_masks = sequence_mask(olens)
-        return out_masks.unsqueeze(-1) & in_masks.unsqueeze(-2)
-
-
-class TacotronLoss(torch.nn.Module):
-    def __init__(self, c, stopnet_pos_weight=10, ga_sigma=0.4):
-        super(TacotronLoss, self).__init__()
-        self.stopnet_pos_weight = stopnet_pos_weight
-        self.ga_alpha = c.ga_alpha
-        self.config = c
-        # postnet decoder loss
-        if c.loss_masking:
-            self.criterion = L1LossMasked(c.seq_len_norm) if c.model in [
-                "Tacotron"
-            ] else MSELossMasked(c.seq_len_norm)
-        else:
-            self.criterion = nn.L1Loss() if c.model in ["Tacotron"
-                                                        ] else nn.MSELoss()
-        # guided attention loss
-        if c.ga_alpha > 0:
-            self.criterion_ga = GuidedAttentionLoss(sigma=ga_sigma)
-        # stopnet loss
-        # pylint: disable=not-callable
-        self.criterion_st = BCELossMasked(pos_weight=torch.tensor(stopnet_pos_weight)) if c.stopnet else None
-
-    def forward(self, postnet_output, decoder_output, mel_input, linear_input,
-                stopnet_output, stopnet_target, output_lens, decoder_b_output,
-                alignments, alignment_lens, alignments_backwards, input_lens):
-
-        return_dict = {}
-        # decoder and postnet losses
-        if self.config.loss_masking:
-            decoder_loss = self.criterion(decoder_output, mel_input,
-                                          output_lens)
-            if self.config.model in ["Tacotron", "TacotronGST"]:
-                postnet_loss = self.criterion(postnet_output, linear_input,
-                                              output_lens)
-            else:
-                postnet_loss = self.criterion(postnet_output, mel_input,
-                                              output_lens)
-        else:
-            decoder_loss = self.criterion(decoder_output, mel_input)
-            if self.config.model in ["Tacotron", "TacotronGST"]:
-                postnet_loss = self.criterion(postnet_output, linear_input)
-            else:
-                postnet_loss = self.criterion(postnet_output, mel_input)
-        loss = decoder_loss + postnet_loss
-        return_dict['decoder_loss'] = decoder_loss
-        return_dict['postnet_loss'] = postnet_loss
-
-        # stopnet loss
-        stop_loss = self.criterion_st(
-            stopnet_output, stopnet_target,
-            output_lens) if self.config.stopnet else torch.zeros(1)
-        if not self.config.separate_stopnet and self.config.stopnet:
-            loss += stop_loss
-        return_dict['stopnet_loss'] = stop_loss
-
-        # backward decoder loss (if enabled)
-        if self.config.bidirectional_decoder:
-            if self.config.loss_masking:
-                decoder_b_loss = self.criterion(torch.flip(decoder_b_output, dims=(1, )), mel_input, output_lens)
-            else:
-                decoder_b_loss = self.criterion(torch.flip(decoder_b_output, dims=(1, )), mel_input)
-            decoder_c_loss = torch.nn.functional.l1_loss(torch.flip(decoder_b_output, dims=(1, )), decoder_output)
-            loss += decoder_b_loss + decoder_c_loss
-            return_dict['decoder_b_loss'] = decoder_b_loss
-            return_dict['decoder_c_loss'] = decoder_c_loss
-
-        # double decoder consistency loss (if enabled)
-        if self.config.double_decoder_consistency:
-            decoder_b_loss = self.criterion(decoder_b_output, mel_input, output_lens)
-            # decoder_c_loss = torch.nn.functional.l1_loss(decoder_b_output, decoder_output)
-            attention_c_loss = torch.nn.functional.l1_loss(alignments, alignments_backwards)
-            loss += decoder_b_loss + attention_c_loss
-            return_dict['decoder_coarse_loss'] = decoder_b_loss
-            return_dict['decoder_ddc_loss'] = attention_c_loss
-
-        # guided attention loss (if enabled)
-        if self.config.ga_alpha > 0:
-            ga_loss = self.criterion_ga(alignments, input_lens, alignment_lens)
-            loss += ga_loss * self.ga_alpha
-            return_dict['ga_loss'] = ga_loss * self.ga_alpha
-
-        return_dict['loss'] = loss
-        return return_dict
-
diff --git a/layers/tacotron.py b/layers/tacotron.py
deleted file mode 100644
index 20fd1e52..00000000
--- a/layers/tacotron.py
+++ /dev/null
@@ -1,496 +0,0 @@
-# coding: utf-8
-import torch
-from torch import nn
-from .common_layers import Prenet, init_attn, Linear
-
-
-class BatchNormConv1d(nn.Module):
-    r"""A wrapper for Conv1d with BatchNorm. It sets the activation
-    function between Conv and BatchNorm layers. BatchNorm layer
-    is initialized with the TF default values for momentum and eps.
-
-    Args:
-        in_channels: size of each input sample
-        out_channels: size of each output samples
-        kernel_size: kernel size of conv filters
-        stride: stride of conv filters
-        padding: padding of conv filters
-        activation: activation function set b/w Conv1d and BatchNorm
-
-    Shapes:
-        - input: batch x dims
-        - output: batch x dims
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride,
-                 padding,
-                 activation=None):
-
-        super(BatchNormConv1d, self).__init__()
-        self.padding = padding
-        self.padder = nn.ConstantPad1d(padding, 0)
-        self.conv1d = nn.Conv1d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=0,
-            bias=False)
-        # Following tensorflow's default parameters
-        self.bn = nn.BatchNorm1d(out_channels, momentum=0.99, eps=1e-3)
-        self.activation = activation
-        # self.init_layers()
-
-    def init_layers(self):
-        if type(self.activation) == torch.nn.ReLU:
-            w_gain = 'relu'
-        elif type(self.activation) == torch.nn.Tanh:
-            w_gain = 'tanh'
-        elif self.activation is None:
-            w_gain = 'linear'
-        else:
-            raise RuntimeError('Unknown activation function')
-        torch.nn.init.xavier_uniform_(
-            self.conv1d.weight, gain=torch.nn.init.calculate_gain(w_gain))
-
-    def forward(self, x):
-        x = self.padder(x)
-        x = self.conv1d(x)
-        x = self.bn(x)
-        if self.activation is not None:
-            x = self.activation(x)
-        return x
-
-
-class Highway(nn.Module):
-    # TODO: Try GLU layer
-    def __init__(self, in_size, out_size):
-        super(Highway, self).__init__()
-        self.H = nn.Linear(in_size, out_size)
-        self.H.bias.data.zero_()
-        self.T = nn.Linear(in_size, out_size)
-        self.T.bias.data.fill_(-1)
-        self.relu = nn.ReLU()
-        self.sigmoid = nn.Sigmoid()
-        # self.init_layers()
-
-    def init_layers(self):
-        torch.nn.init.xavier_uniform_(
-            self.H.weight, gain=torch.nn.init.calculate_gain('relu'))
-        torch.nn.init.xavier_uniform_(
-            self.T.weight, gain=torch.nn.init.calculate_gain('sigmoid'))
-
-    def forward(self, inputs):
-        H = self.relu(self.H(inputs))
-        T = self.sigmoid(self.T(inputs))
-        return H * T + inputs * (1.0 - T)
-
-
-class CBHG(nn.Module):
-    """CBHG module: a recurrent neural network composed of:
-        - 1-d convolution banks
-        - Highway networks + residual connections
-        - Bidirectional gated recurrent units
-
-        Args:
-            in_features (int): sample size
-            K (int): max filter size in conv bank
-            projections (list): conv channel sizes for conv projections
-            num_highways (int): number of highways layers
-
-        Shapes:
-            - input: B x D x T_in
-            - output: B x T_in x D*2
-    """
-
-    def __init__(self,
-                 in_features,
-                 K=16,
-                 conv_bank_features=128,
-                 conv_projections=[128, 128],
-                 highway_features=128,
-                 gru_features=128,
-                 num_highways=4):
-        super(CBHG, self).__init__()
-        self.in_features = in_features
-        self.conv_bank_features = conv_bank_features
-        self.highway_features = highway_features
-        self.gru_features = gru_features
-        self.conv_projections = conv_projections
-        self.relu = nn.ReLU()
-        # list of conv1d bank with filter size k=1...K
-        # TODO: try dilational layers instead
-        self.conv1d_banks = nn.ModuleList([
-            BatchNormConv1d(in_features,
-                            conv_bank_features,
-                            kernel_size=k,
-                            stride=1,
-                            padding=[(k - 1) // 2, k // 2],
-                            activation=self.relu) for k in range(1, K + 1)
-        ])
-        # max pooling of conv bank, with padding
-        # TODO: try average pooling OR larger kernel size
-        out_features = [K * conv_bank_features] + conv_projections[:-1]
-        activations = [self.relu] * (len(conv_projections) - 1)
-        activations += [None]
-        # setup conv1d projection layers
-        layer_set = []
-        for (in_size, out_size, ac) in zip(out_features, conv_projections,
-                                           activations):
-            layer = BatchNormConv1d(in_size,
-                                    out_size,
-                                    kernel_size=3,
-                                    stride=1,
-                                    padding=[1, 1],
-                                    activation=ac)
-            layer_set.append(layer)
-        self.conv1d_projections = nn.ModuleList(layer_set)
-        # setup Highway layers
-        if self.highway_features != conv_projections[-1]:
-            self.pre_highway = nn.Linear(conv_projections[-1],
-                                         highway_features,
-                                         bias=False)
-        self.highways = nn.ModuleList([
-            Highway(highway_features, highway_features)
-            for _ in range(num_highways)
-        ])
-        # bi-directional GPU layer
-        self.gru = nn.GRU(gru_features,
-                          gru_features,
-                          1,
-                          batch_first=True,
-                          bidirectional=True)
-
-    def forward(self, inputs):
-        # (B, in_features, T_in)
-        x = inputs
-        # (B, hid_features*K, T_in)
-        # Concat conv1d bank outputs
-        outs = []
-        for conv1d in self.conv1d_banks:
-            out = conv1d(x)
-            outs.append(out)
-        x = torch.cat(outs, dim=1)
-        assert x.size(1) == self.conv_bank_features * len(self.conv1d_banks)
-        for conv1d in self.conv1d_projections:
-            x = conv1d(x)
-        x += inputs
-        x = x.transpose(1, 2)
-        if self.highway_features != self.conv_projections[-1]:
-            x = self.pre_highway(x)
-        # Residual connection
-        # TODO: try residual scaling as in Deep Voice 3
-        # TODO: try plain residual layers
-        for highway in self.highways:
-            x = highway(x)
-        # (B, T_in, hid_features*2)
-        # TODO: replace GRU with convolution as in Deep Voice 3
-        self.gru.flatten_parameters()
-        outputs, _ = self.gru(x)
-        return outputs
-
-
-class EncoderCBHG(nn.Module):
-    def __init__(self):
-        super(EncoderCBHG, self).__init__()
-        self.cbhg = CBHG(
-            128,
-            K=16,
-            conv_bank_features=128,
-            conv_projections=[128, 128],
-            highway_features=128,
-            gru_features=128,
-            num_highways=4)
-
-    def forward(self, x):
-        return self.cbhg(x)
-
-
-class Encoder(nn.Module):
-    r"""Encapsulate Prenet and CBHG modules for encoder"""
-
-    def __init__(self, in_features):
-        super(Encoder, self).__init__()
-        self.prenet = Prenet(in_features, out_features=[256, 128])
-        self.cbhg = EncoderCBHG()
-
-    def forward(self, inputs):
-        r"""
-        Args:
-            inputs (FloatTensor): embedding features
-
-        Shapes:
-            - inputs: batch x time x in_features
-            - outputs: batch x time x 128*2
-        """
-        # B x T x prenet_dim
-        outputs = self.prenet(inputs)
-        outputs = self.cbhg(outputs.transpose(1, 2))
-        return outputs
-
-
-class PostCBHG(nn.Module):
-    def __init__(self, mel_dim):
-        super(PostCBHG, self).__init__()
-        self.cbhg = CBHG(
-            mel_dim,
-            K=8,
-            conv_bank_features=128,
-            conv_projections=[256, mel_dim],
-            highway_features=128,
-            gru_features=128,
-            num_highways=4)
-
-    def forward(self, x):
-        return self.cbhg(x)
-
-
-class Decoder(nn.Module):
-    """Decoder module.
-
-    Args:
-        in_features (int): input vector (encoder output) sample size.
-        memory_dim (int): memory vector (prev. time-step output) sample size.
-        r (int): number of outputs per time step.
-        memory_size (int): size of the past window. if <= 0 memory_size = r
-        TODO: arguments
-    """
-
-    # Pylint gets confused by PyTorch conventions here
-    #pylint: disable=attribute-defined-outside-init
-
-    def __init__(self, in_features, memory_dim, r, memory_size, attn_type, attn_windowing,
-                 attn_norm, prenet_type, prenet_dropout, forward_attn,
-                 trans_agent, forward_attn_mask, location_attn, attn_K,
-                 separate_stopnet, speaker_embedding_dim):
-        super(Decoder, self).__init__()
-        self.r_init = r
-        self.r = r
-        self.in_features = in_features
-        self.max_decoder_steps = 500
-        self.use_memory_queue = memory_size > 0
-        self.memory_size = memory_size if memory_size > 0 else r
-        self.memory_dim = memory_dim
-        self.separate_stopnet = separate_stopnet
-        self.query_dim = 256
-        # memory -> |Prenet| -> processed_memory
-        prenet_dim = memory_dim * self.memory_size + speaker_embedding_dim if self.use_memory_queue else memory_dim + speaker_embedding_dim
-        self.prenet = Prenet(
-            prenet_dim,
-            prenet_type,
-            prenet_dropout,
-            out_features=[256, 128])
-        # processed_inputs, processed_memory -> |Attention| -> Attention, attention, RNN_State
-        # attention_rnn generates queries for the attention mechanism
-        self.attention_rnn = nn.GRUCell(in_features + 128, self.query_dim)
-
-        self.attention = init_attn(attn_type=attn_type,
-                                   query_dim=self.query_dim,
-                                   embedding_dim=in_features,
-                                   attention_dim=128,
-                                   location_attention=location_attn,
-                                   attention_location_n_filters=32,
-                                   attention_location_kernel_size=31,
-                                   windowing=attn_windowing,
-                                   norm=attn_norm,
-                                   forward_attn=forward_attn,
-                                   trans_agent=trans_agent,
-                                   forward_attn_mask=forward_attn_mask,
-                                   attn_K=attn_K)
-        # (processed_memory | attention context) -> |Linear| -> decoder_RNN_input
-        self.project_to_decoder_in = nn.Linear(256 + in_features, 256)
-        # decoder_RNN_input -> |RNN| -> RNN_state
-        self.decoder_rnns = nn.ModuleList(
-            [nn.GRUCell(256, 256) for _ in range(2)])
-        # RNN_state -> |Linear| -> mel_spec
-        self.proj_to_mel = nn.Linear(256, memory_dim * self.r_init)
-        # learn init values instead of zero init.
-        self.stopnet = StopNet(256 + memory_dim * self.r_init)
-
-    def set_r(self, new_r):
-        self.r = new_r
-
-    def _reshape_memory(self, memory):
-        """
-        Reshape the spectrograms for given 'r'
-        """
-        # Grouping multiple frames if necessary
-        if memory.size(-1) == self.memory_dim:
-            memory = memory.view(memory.shape[0], memory.size(1) // self.r, -1)
-        # Time first (T_decoder, B, memory_dim)
-        memory = memory.transpose(0, 1)
-        return memory
-
-    def _init_states(self, inputs):
-        """
-        Initialization of decoder states
-        """
-        B = inputs.size(0)
-        T = inputs.size(1)
-        # go frame as zeros matrix
-        if self.use_memory_queue:
-            self.memory_input = torch.zeros(1, device=inputs.device).repeat(B, self.memory_dim * self.memory_size)
-        else:
-            self.memory_input = torch.zeros(1, device=inputs.device).repeat(B, self.memory_dim)
-        # decoder states
-        self.attention_rnn_hidden = torch.zeros(1, device=inputs.device).repeat(B, 256)
-        self.decoder_rnn_hiddens = [
-            torch.zeros(1, device=inputs.device).repeat(B, 256)
-            for idx in range(len(self.decoder_rnns))
-        ]
-        self.context_vec = inputs.data.new(B, self.in_features).zero_()
-        # cache attention inputs
-        self.processed_inputs = self.attention.preprocess_inputs(inputs)
-
-    def _parse_outputs(self, outputs, attentions, stop_tokens):
-        # Back to batch first
-        attentions = torch.stack(attentions).transpose(0, 1)
-        stop_tokens = torch.stack(stop_tokens).transpose(0, 1)
-        outputs = torch.stack(outputs).transpose(0, 1).contiguous()
-        outputs = outputs.view(
-            outputs.size(0), -1, self.memory_dim)
-        outputs = outputs.transpose(1, 2)
-        return outputs, attentions, stop_tokens
-
-    def decode(self, inputs, mask=None):
-        # Prenet
-        processed_memory = self.prenet(self.memory_input)
-        # Attention RNN
-        self.attention_rnn_hidden = self.attention_rnn(
-            torch.cat((processed_memory, self.context_vec), -1),
-            self.attention_rnn_hidden)
-        self.context_vec = self.attention(
-            self.attention_rnn_hidden, inputs, self.processed_inputs, mask)
-        # Concat RNN output and attention context vector
-        decoder_input = self.project_to_decoder_in(
-            torch.cat((self.attention_rnn_hidden, self.context_vec), -1))
-
-        # Pass through the decoder RNNs
-        for idx in range(len(self.decoder_rnns)):
-            self.decoder_rnn_hiddens[idx] = self.decoder_rnns[idx](
-                decoder_input, self.decoder_rnn_hiddens[idx])
-            # Residual connection
-            decoder_input = self.decoder_rnn_hiddens[idx] + decoder_input
-        decoder_output = decoder_input
-
-        # predict mel vectors from decoder vectors
-        output = self.proj_to_mel(decoder_output)
-        # output = torch.sigmoid(output)
-        # predict stop token
-        stopnet_input = torch.cat([decoder_output, output], -1)
-        if self.separate_stopnet:
-            stop_token = self.stopnet(stopnet_input.detach())
-        else:
-            stop_token = self.stopnet(stopnet_input)
-        output = output[:, : self.r * self.memory_dim]
-        return output, stop_token, self.attention.attention_weights
-
-    def _update_memory_input(self, new_memory):
-        if self.use_memory_queue:
-            if self.memory_size > self.r:
-                # memory queue size is larger than number of frames per decoder iter
-                self.memory_input = torch.cat([
-                    new_memory, self.memory_input[:, :(
-                        self.memory_size - self.r) * self.memory_dim].clone()
-                ], dim=-1)
-            else:
-                # memory queue size smaller than number of frames per decoder iter
-                self.memory_input = new_memory[:, :self.memory_size * self.memory_dim]
-        else:
-            # use only the last frame prediction
-            # assert new_memory.shape[-1] == self.r * self.memory_dim
-            self.memory_input = new_memory[:, self.memory_dim * (self.r - 1):]
-
-    def forward(self, inputs, memory, mask, speaker_embeddings=None):
-        """
-        Args:
-            inputs: Encoder outputs.
-            memory: Decoder memory (autoregression. If None (at eval-time),
-              decoder outputs are used as decoder inputs. If None, it uses the last
-              output as the input.
-            mask: Attention mask for sequence padding.
-
-        Shapes:
-            - inputs: batch x time x encoder_out_dim
-            - memory: batch x #mel_specs x mel_spec_dim
-        """
-        # Run greedy decoding if memory is None
-        memory = self._reshape_memory(memory)
-        outputs = []
-        attentions = []
-        stop_tokens = []
-        t = 0
-        self._init_states(inputs)
-        self.attention.init_states(inputs)
-        while len(outputs) < memory.size(0):
-            if t > 0:
-                new_memory = memory[t - 1]
-                self._update_memory_input(new_memory)
-            if speaker_embeddings is not None:
-                self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
-            output, stop_token, attention = self.decode(inputs, mask)
-            outputs += [output]
-            attentions += [attention]
-            stop_tokens += [stop_token.squeeze(1)]
-            t += 1
-        return self._parse_outputs(outputs, attentions, stop_tokens)
-
-    def inference(self, inputs, speaker_embeddings=None):
-        """
-        Args:
-            inputs: encoder outputs.
-            speaker_embeddings: speaker vectors.
-
-        Shapes:
-            - inputs: batch x time x encoder_out_dim
-            - speaker_embeddings: batch x embed_dim
-        """
-        outputs = []
-        attentions = []
-        stop_tokens = []
-        t = 0
-        self._init_states(inputs)
-        self.attention.init_win_idx()
-        self.attention.init_states(inputs)
-        while True:
-            if t > 0:
-                new_memory = outputs[-1]
-                self._update_memory_input(new_memory)
-            if speaker_embeddings is not None:
-                self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
-            output, stop_token, attention = self.decode(inputs, None)
-            stop_token = torch.sigmoid(stop_token.data)
-            outputs += [output]
-            attentions += [attention]
-            stop_tokens += [stop_token]
-            t += 1
-            if t > inputs.shape[1] / 4 and (stop_token > 0.6
-                                            or attention[:, -1].item() > 0.6):
-                break
-            elif t > self.max_decoder_steps:
-                print("   | > Decoder stopped with 'max_decoder_steps")
-                break
-        return self._parse_outputs(outputs, attentions, stop_tokens)
-
-
-class StopNet(nn.Module):
-    r"""
-    Args:
-        in_features (int): feature dimension of input.
-    """
-
-    def __init__(self, in_features):
-        super(StopNet, self).__init__()
-        self.dropout = nn.Dropout(0.1)
-        self.linear = nn.Linear(in_features, 1)
-        torch.nn.init.xavier_uniform_(
-            self.linear.weight, gain=torch.nn.init.calculate_gain('linear'))
-
-    def forward(self, inputs):
-        outputs = self.dropout(inputs)
-        outputs = self.linear(outputs)
-        return outputs
diff --git a/layers/tacotron2.py b/layers/tacotron2.py
deleted file mode 100644
index f11aee65..00000000
--- a/layers/tacotron2.py
+++ /dev/null
@@ -1,353 +0,0 @@
-import torch
-from torch.autograd import Variable
-from torch import nn
-from torch.nn import functional as F
-from .common_layers import init_attn, Prenet, Linear
-
-
-class ConvBNBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, activation=None):
-        super(ConvBNBlock, self).__init__()
-        assert (kernel_size - 1) % 2 == 0
-        padding = (kernel_size - 1) // 2
-        self.convolution1d = nn.Conv1d(in_channels,
-                                       out_channels,
-                                       kernel_size,
-                                       padding=padding)
-        self.batch_normalization = nn.BatchNorm1d(out_channels, momentum=0.1, eps=1e-5)
-        self.dropout = nn.Dropout(p=0.5)
-        if activation == 'relu':
-            self.activation = nn.ReLU()
-        elif activation == 'tanh':
-            self.activation = nn.Tanh()
-        else:
-            self.activation = nn.Identity()
-
-    def forward(self, x):
-        o = self.convolution1d(x)
-        o = self.batch_normalization(o)
-        o = self.activation(o)
-        o = self.dropout(o)
-        return o
-
-
-class Postnet(nn.Module):
-    def __init__(self, output_dim, num_convs=5):
-        super(Postnet, self).__init__()
-        self.convolutions = nn.ModuleList()
-        self.convolutions.append(
-            ConvBNBlock(output_dim, 512, kernel_size=5, activation='tanh'))
-        for _ in range(1, num_convs - 1):
-            self.convolutions.append(
-                ConvBNBlock(512, 512, kernel_size=5, activation='tanh'))
-        self.convolutions.append(
-            ConvBNBlock(512, output_dim, kernel_size=5, activation=None))
-
-    def forward(self, x):
-        o = x
-        for layer in self.convolutions:
-            o = layer(o)
-        return o
-
-
-class Encoder(nn.Module):
-    def __init__(self, output_input_dim=512):
-        super(Encoder, self).__init__()
-        self.convolutions = nn.ModuleList()
-        for _ in range(3):
-            self.convolutions.append(
-                ConvBNBlock(output_input_dim, output_input_dim, 5, 'relu'))
-        self.lstm = nn.LSTM(output_input_dim,
-                            int(output_input_dim / 2),
-                            num_layers=1,
-                            batch_first=True,
-                            bias=True,
-                            bidirectional=True)
-        self.rnn_state = None
-
-    def forward(self, x, input_lengths):
-        o = x
-        for layer in self.convolutions:
-            o = layer(o)
-        o = o.transpose(1, 2)
-        o = nn.utils.rnn.pack_padded_sequence(o,
-                                              input_lengths,
-                                              batch_first=True)
-        self.lstm.flatten_parameters()
-        o, _ = self.lstm(o)
-        o, _ = nn.utils.rnn.pad_packed_sequence(o, batch_first=True)
-        return o
-
-    def inference(self, x):
-        o = x
-        for layer in self.convolutions:
-            o = layer(o)
-        o = o.transpose(1, 2)
-        # self.lstm.flatten_parameters()
-        o, _ = self.lstm(o)
-        return o
-
-
-# adapted from https://github.com/NVIDIA/tacotron2/
-class Decoder(nn.Module):
-    # Pylint gets confused by PyTorch conventions here
-    #pylint: disable=attribute-defined-outside-init
-    def __init__(self, input_dim, frame_dim, r, attn_type, attn_win, attn_norm,
-                 prenet_type, prenet_dropout, forward_attn, trans_agent,
-                 forward_attn_mask, location_attn, attn_K, separate_stopnet,
-                 speaker_embedding_dim):
-        super(Decoder, self).__init__()
-        self.frame_dim = frame_dim
-        self.r_init = r
-        self.r = r
-        self.encoder_embedding_dim = input_dim
-        self.separate_stopnet = separate_stopnet
-        self.max_decoder_steps = 1000
-        self.gate_threshold = 0.5
-
-        # model dimensions
-        self.query_dim = 1024
-        self.decoder_rnn_dim = 1024
-        self.prenet_dim = 256
-        self.attn_dim = 128
-        self.p_attention_dropout = 0.1
-        self.p_decoder_dropout = 0.1
-
-        # memory -> |Prenet| -> processed_memory
-        prenet_dim = self.frame_dim
-        self.prenet = Prenet(prenet_dim,
-                             prenet_type,
-                             prenet_dropout,
-                             out_features=[self.prenet_dim, self.prenet_dim],
-                             bias=False)
-
-        self.attention_rnn = nn.LSTMCell(self.prenet_dim + input_dim,
-                                         self.query_dim,
-                                         bias=True)
-
-        self.attention = init_attn(attn_type=attn_type,
-                                   query_dim=self.query_dim,
-                                   embedding_dim=input_dim,
-                                   attention_dim=128,
-                                   location_attention=location_attn,
-                                   attention_location_n_filters=32,
-                                   attention_location_kernel_size=31,
-                                   windowing=attn_win,
-                                   norm=attn_norm,
-                                   forward_attn=forward_attn,
-                                   trans_agent=trans_agent,
-                                   forward_attn_mask=forward_attn_mask,
-                                   attn_K=attn_K)
-
-        self.decoder_rnn = nn.LSTMCell(self.query_dim + input_dim,
-                                       self.decoder_rnn_dim,
-                                       bias=True)
-
-        self.linear_projection = Linear(self.decoder_rnn_dim + input_dim,
-                                        self.frame_dim * self.r_init)
-
-        self.stopnet = nn.Sequential(
-            nn.Dropout(0.1),
-            Linear(self.decoder_rnn_dim + self.frame_dim * self.r_init,
-                   1,
-                   bias=True,
-                   init_gain='sigmoid'))
-        self.memory_truncated = None
-
-    def set_r(self, new_r):
-        self.r = new_r
-
-    def get_go_frame(self, inputs):
-        B = inputs.size(0)
-        memory = torch.zeros(1, device=inputs.device).repeat(B,
-                             self.frame_dim * self.r)
-        return memory
-
-    def _init_states(self, inputs, mask, keep_states=False):
-        B = inputs.size(0)
-        # T = inputs.size(1)
-        if not keep_states:
-            self.query = torch.zeros(1, device=inputs.device).repeat(
-                B, self.query_dim)
-            self.attention_rnn_cell_state = torch.zeros(
-                1, device=inputs.device).repeat(B, self.query_dim)
-            self.decoder_hidden = torch.zeros(1, device=inputs.device).repeat(
-                B, self.decoder_rnn_dim)
-            self.decoder_cell = torch.zeros(1, device=inputs.device).repeat(
-                B, self.decoder_rnn_dim)
-            self.context = torch.zeros(1, device=inputs.device).repeat(
-                B, self.encoder_embedding_dim)
-        self.inputs = inputs
-        self.processed_inputs = self.attention.preprocess_inputs(inputs)
-        self.mask = mask
-
-    def _reshape_memory(self, memory):
-        """
-        Reshape the spectrograms for given 'r'
-        """
-        # Grouping multiple frames if necessary
-        if memory.size(-1) == self.frame_dim:
-            memory = memory.view(memory.shape[0], memory.size(1) // self.r, -1)
-        # Time first (T_decoder, B, frame_dim)
-        memory = memory.transpose(0, 1)
-        return memory
-
-    def _parse_outputs(self, outputs, stop_tokens, alignments):
-        alignments = torch.stack(alignments).transpose(0, 1)
-        stop_tokens = torch.stack(stop_tokens).transpose(0, 1)
-        outputs = torch.stack(outputs).transpose(0, 1).contiguous()
-        outputs = outputs.view(outputs.size(0), -1, self.frame_dim)
-        outputs = outputs.transpose(1, 2)
-        return outputs, stop_tokens, alignments
-
-    def _update_memory(self, memory):
-        if len(memory.shape) == 2:
-            return memory[:, self.frame_dim * (self.r - 1):]
-        return memory[:, :, self.frame_dim * (self.r - 1):]
-
-    def decode(self, memory):
-        '''
-         shapes:
-            - memory: B x r * self.frame_dim
-        '''
-        # self.context: B x D_en
-        # query_input: B x D_en + (r * self.frame_dim)
-        query_input = torch.cat((memory, self.context), -1)
-        # self.query and self.attention_rnn_cell_state : B x D_attn_rnn
-        self.query, self.attention_rnn_cell_state = self.attention_rnn(
-            query_input, (self.query, self.attention_rnn_cell_state))
-        self.query = F.dropout(self.query, self.p_attention_dropout,
-                               self.training)
-        self.attention_rnn_cell_state = F.dropout(
-            self.attention_rnn_cell_state, self.p_attention_dropout,
-            self.training)
-        # B x D_en
-        self.context = self.attention(self.query, self.inputs,
-                                      self.processed_inputs, self.mask)
-        # B x (D_en + D_attn_rnn)
-        decoder_rnn_input = torch.cat((self.query, self.context), -1)
-        # self.decoder_hidden and self.decoder_cell: B x D_decoder_rnn
-        self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
-            decoder_rnn_input, (self.decoder_hidden, self.decoder_cell))
-        self.decoder_hidden = F.dropout(self.decoder_hidden,
-                                        self.p_decoder_dropout, self.training)
-        # B x (D_decoder_rnn + D_en)
-        decoder_hidden_context = torch.cat((self.decoder_hidden, self.context),
-                                           dim=1)
-        # B x (self.r * self.frame_dim)
-        decoder_output = self.linear_projection(decoder_hidden_context)
-        # B x (D_decoder_rnn + (self.r * self.frame_dim))
-        stopnet_input = torch.cat((self.decoder_hidden, decoder_output), dim=1)
-        if self.separate_stopnet:
-            stop_token = self.stopnet(stopnet_input.detach())
-        else:
-            stop_token = self.stopnet(stopnet_input)
-        # select outputs for the reduction rate self.r
-        decoder_output = decoder_output[:, :self.r * self.frame_dim]
-        return decoder_output, self.attention.attention_weights, stop_token
-
-    def forward(self, inputs, memories, mask, speaker_embeddings=None):
-        memory = self.get_go_frame(inputs).unsqueeze(0)
-        memories = self._reshape_memory(memories)
-        memories = torch.cat((memory, memories), dim=0)
-        memories = self._update_memory(memories)
-        if speaker_embeddings is not None:
-            memories = torch.cat([memories, speaker_embeddings], dim=-1)
-        memories = self.prenet(memories)
-
-        self._init_states(inputs, mask=mask)
-        self.attention.init_states(inputs)
-
-        outputs, stop_tokens, alignments = [], [], []
-        while len(outputs) < memories.size(0) - 1:
-            memory = memories[len(outputs)]
-            decoder_output, attention_weights, stop_token = self.decode(memory)
-            outputs += [decoder_output.squeeze(1)]
-            stop_tokens += [stop_token.squeeze(1)]
-            alignments += [attention_weights]
-
-        outputs, stop_tokens, alignments = self._parse_outputs(
-            outputs, stop_tokens, alignments)
-        return outputs, alignments, stop_tokens
-
-    def inference(self, inputs, speaker_embeddings=None):
-        memory = self.get_go_frame(inputs)
-        memory = self._update_memory(memory)
-
-        self._init_states(inputs, mask=None)
-        self.attention.init_states(inputs)
-
-        outputs, stop_tokens, alignments, t = [], [], [], 0
-        while True:
-            memory = self.prenet(memory)
-            if speaker_embeddings is not None:
-                memory = torch.cat([memory, speaker_embeddings], dim=-1)
-            decoder_output, alignment, stop_token = self.decode(memory)
-            stop_token = torch.sigmoid(stop_token.data)
-            outputs += [decoder_output.squeeze(1)]
-            stop_tokens += [stop_token]
-            alignments += [alignment]
-
-            if stop_token > 0.7 and t > inputs.shape[0] / 2:
-                break
-            if len(outputs) == self.max_decoder_steps:
-                print("   | > Decoder stopped with 'max_decoder_steps")
-                break
-
-            memory = self._update_memory(decoder_output)
-            t += 1
-
-        outputs, stop_tokens, alignments = self._parse_outputs(
-            outputs, stop_tokens, alignments)
-
-        return outputs, alignments, stop_tokens
-
-    def inference_truncated(self, inputs):
-        """
-        Preserve decoder states for continuous inference
-        """
-        if self.memory_truncated is None:
-            self.memory_truncated = self.get_go_frame(inputs)
-            self._init_states(inputs, mask=None, keep_states=False)
-        else:
-            self._init_states(inputs, mask=None, keep_states=True)
-
-        self.attention.init_win_idx()
-        self.attention.init_states(inputs)
-        outputs, stop_tokens, alignments, t = [], [], [], 0
-        stop_flags = [True, False, False]
-        while True:
-            memory = self.prenet(self.memory_truncated)
-            decoder_output, alignment, stop_token = self.decode(memory)
-            stop_token = torch.sigmoid(stop_token.data)
-            outputs += [decoder_output.squeeze(1)]
-            stop_tokens += [stop_token]
-            alignments += [alignment]
-
-            if stop_token > 0.7:
-                break
-            if len(outputs) == self.max_decoder_steps:
-                print("   | > Decoder stopped with 'max_decoder_steps")
-                break
-
-            self.memory_truncated = decoder_output
-            t += 1
-
-        outputs, stop_tokens, alignments = self._parse_outputs(
-            outputs, stop_tokens, alignments)
-
-        return outputs, alignments, stop_tokens
-
-    def inference_step(self, inputs, t, memory=None):
-        """
-        For debug purposes
-        """
-        if t == 0:
-            memory = self.get_go_frame(inputs)
-            self._init_states(inputs, mask=None)
-
-        memory = self.prenet(memory)
-        decoder_output, stop_token, alignment = self.decode(memory)
-        stop_token = torch.sigmoid(stop_token.data)
-        memory = decoder_output
-        return decoder_output, stop_token, alignment
diff --git a/models/__init__.py b/models/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/models/tacotron.py b/models/tacotron.py
deleted file mode 100644
index ba42610c..00000000
--- a/models/tacotron.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# coding: utf-8
-import torch
-from torch import nn
-
-from TTS.layers.gst_layers import GST
-from TTS.layers.tacotron import Decoder, Encoder, PostCBHG
-from TTS.models.tacotron_abstract import TacotronAbstract
-
-
-class Tacotron(TacotronAbstract):
-    def __init__(self,
-                 num_chars,
-                 num_speakers,
-                 r=5,
-                 postnet_output_dim=1025,
-                 decoder_output_dim=80,
-                 attn_type='original',
-                 attn_win=False,
-                 attn_norm="sigmoid",
-                 prenet_type="original",
-                 prenet_dropout=True,
-                 forward_attn=False,
-                 trans_agent=False,
-                 forward_attn_mask=False,
-                 location_attn=True,
-                 attn_K=5,
-                 separate_stopnet=True,
-                 bidirectional_decoder=False,
-                 double_decoder_consistency=False,
-                 ddc_r=None,
-                 gst=False,
-                 memory_size=5):
-        super(Tacotron,
-              self).__init__(num_chars, num_speakers, r, postnet_output_dim,
-                             decoder_output_dim, attn_type, attn_win,
-                             attn_norm, prenet_type, prenet_dropout,
-                             forward_attn, trans_agent, forward_attn_mask,
-                             location_attn, attn_K, separate_stopnet,
-                             bidirectional_decoder, double_decoder_consistency,
-                             ddc_r, gst)
-        decoder_in_features = 512 if num_speakers > 1 else 256
-        encoder_in_features = 512 if num_speakers > 1 else 256
-        speaker_embedding_dim = 256
-        proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # base model layers
-        self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
-        self.embedding.weight.data.normal_(0, 0.3)
-        self.encoder = Encoder(encoder_in_features)
-        self.decoder = Decoder(decoder_in_features, decoder_output_dim, r,
-                               memory_size, attn_type, attn_win, attn_norm,
-                               prenet_type, prenet_dropout, forward_attn,
-                               trans_agent, forward_attn_mask, location_attn,
-                               attn_K, separate_stopnet, proj_speaker_dim)
-        self.postnet = PostCBHG(decoder_output_dim)
-        self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2,
-                                     postnet_output_dim)
-        # speaker embedding layers
-        if num_speakers > 1:
-            self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
-            self.speaker_embedding.weight.data.normal_(0, 0.3)
-            self.speaker_project_mel = nn.Sequential(
-                nn.Linear(speaker_embedding_dim, proj_speaker_dim), nn.Tanh())
-            self.speaker_embeddings = None
-            self.speaker_embeddings_projected = None
-        # global style token layers
-        if self.gst:
-            gst_embedding_dim = 256
-            self.gst_layer = GST(num_mel=80,
-                                 num_heads=4,
-                                 num_style_tokens=10,
-                                 embedding_dim=gst_embedding_dim)
-        # backward pass decoder
-        if self.bidirectional_decoder:
-            self._init_backward_decoder()
-        # setup DDC
-        if self.double_decoder_consistency:
-            self.coarse_decoder = Decoder(
-                decoder_in_features, decoder_output_dim, ddc_r, memory_size,
-                attn_type, attn_win, attn_norm, prenet_type, prenet_dropout,
-                forward_attn, trans_agent, forward_attn_mask, location_attn,
-                attn_K, separate_stopnet, proj_speaker_dim)
-
-
-    def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None):
-        """
-        Shapes:
-            - characters: B x T_in
-            - text_lengths: B
-            - mel_specs: B x T_out x D
-            - speaker_ids: B x 1
-        """
-        self._init_states()
-        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
-        # B x T_in x embed_dim
-        inputs = self.embedding(characters)
-        # B x speaker_embed_dim
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
-        if self.num_speakers > 1:
-            # B x T_in x embed_dim + speaker_embed_dim
-            inputs = self._concat_speaker_embedding(inputs,
-                                                    self.speaker_embeddings)
-        # B x T_in x encoder_in_features
-        encoder_outputs = self.encoder(inputs)
-        # sequence masking
-        encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
-        # global style token
-        if self.gst:
-            # B x gst_dim
-            encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
-        if self.num_speakers > 1:
-            encoder_outputs = self._concat_speaker_embedding(
-                encoder_outputs, self.speaker_embeddings)
-        # decoder_outputs: B x decoder_in_features x T_out
-        # alignments: B x T_in x encoder_in_features
-        # stop_tokens: B x T_in
-        decoder_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, input_mask,
-            self.speaker_embeddings_projected)
-        # sequence masking
-        if output_mask is not None:
-            decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
-        # B x T_out x decoder_in_features
-        postnet_outputs = self.postnet(decoder_outputs)
-        # sequence masking
-        if output_mask is not None:
-            postnet_outputs = postnet_outputs * output_mask.unsqueeze(2).expand_as(postnet_outputs)
-        # B x T_out x posnet_dim
-        postnet_outputs = self.last_linear(postnet_outputs)
-        # B x T_out x decoder_in_features
-        decoder_outputs = decoder_outputs.transpose(1, 2).contiguous()
-        if self.bidirectional_decoder:
-            decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
-            return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
-        if self.double_decoder_consistency:
-            decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
-            return  decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
-        return decoder_outputs, postnet_outputs, alignments, stop_tokens
-
-    @torch.no_grad()
-    def inference(self, characters, speaker_ids=None, style_mel=None):
-        inputs = self.embedding(characters)
-        self._init_states()
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
-        if self.num_speakers > 1:
-            inputs = self._concat_speaker_embedding(inputs,
-                                                    self.speaker_embeddings)
-        encoder_outputs = self.encoder(inputs)
-        if self.gst and style_mel is not None:
-            encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
-        if self.num_speakers > 1:
-            encoder_outputs = self._concat_speaker_embedding(
-                encoder_outputs, self.speaker_embeddings)
-        decoder_outputs, alignments, stop_tokens = self.decoder.inference(
-            encoder_outputs, self.speaker_embeddings_projected)
-        postnet_outputs = self.postnet(decoder_outputs)
-        postnet_outputs = self.last_linear(postnet_outputs)
-        decoder_outputs = decoder_outputs.transpose(1, 2)
-        return decoder_outputs, postnet_outputs, alignments, stop_tokens
diff --git a/models/tacotron2.py b/models/tacotron2.py
deleted file mode 100644
index 4a22b7fa..00000000
--- a/models/tacotron2.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import torch
-from torch import nn
-
-from TTS.layers.gst_layers import GST
-from TTS.layers.tacotron2 import Decoder, Encoder, Postnet
-from TTS.models.tacotron_abstract import TacotronAbstract
-
-
-# TODO: match function arguments with tacotron
-class Tacotron2(TacotronAbstract):
-    def __init__(self,
-                 num_chars,
-                 num_speakers,
-                 r,
-                 postnet_output_dim=80,
-                 decoder_output_dim=80,
-                 attn_type='original',
-                 attn_win=False,
-                 attn_norm="softmax",
-                 prenet_type="original",
-                 prenet_dropout=True,
-                 forward_attn=False,
-                 trans_agent=False,
-                 forward_attn_mask=False,
-                 location_attn=True,
-                 attn_K=5,
-                 separate_stopnet=True,
-                 bidirectional_decoder=False,
-                 double_decoder_consistency=False,
-                 ddc_r=None,
-                 gst=False):
-        super(Tacotron2,
-              self).__init__(num_chars, num_speakers, r, postnet_output_dim,
-                             decoder_output_dim, attn_type, attn_win,
-                             attn_norm, prenet_type, prenet_dropout,
-                             forward_attn, trans_agent, forward_attn_mask,
-                             location_attn, attn_K, separate_stopnet,
-                             bidirectional_decoder, double_decoder_consistency,
-                             ddc_r, gst)
-        decoder_in_features = 512 if num_speakers > 1 else 512
-        encoder_in_features = 512 if num_speakers > 1 else 512
-        proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # base layers
-        self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
-        if num_speakers > 1:
-            self.speaker_embedding = nn.Embedding(num_speakers, 512)
-            self.speaker_embedding.weight.data.normal_(0, 0.3)
-        self.encoder = Encoder(encoder_in_features)
-        self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
-                               attn_norm, prenet_type, prenet_dropout,
-                               forward_attn, trans_agent, forward_attn_mask,
-                               location_attn, attn_K, separate_stopnet, proj_speaker_dim)
-        self.postnet = Postnet(self.postnet_output_dim)
-        # global style token layers
-        if self.gst:
-            gst_embedding_dim = encoder_in_features
-            self.gst_layer = GST(num_mel=80,
-                                 num_heads=4,
-                                 num_style_tokens=10,
-                                 embedding_dim=gst_embedding_dim)
-        # backward pass decoder
-        if self.bidirectional_decoder:
-            self._init_backward_decoder()
-        # setup DDC
-        if self.double_decoder_consistency:
-            self.coarse_decoder = Decoder(
-                decoder_in_features, self.decoder_output_dim, ddc_r, attn_type,
-                attn_win, attn_norm, prenet_type, prenet_dropout, forward_attn,
-                trans_agent, forward_attn_mask, location_attn, attn_K,
-                separate_stopnet, proj_speaker_dim)
-
-    @staticmethod
-    def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
-        mel_outputs = mel_outputs.transpose(1, 2)
-        mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
-        return mel_outputs, mel_outputs_postnet, alignments
-
-    def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
-        self._init_states()
-        # compute mask for padding
-        # B x T_in_max (boolean)
-        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
-        # B x D_embed x T_in_max
-        embedded_inputs = self.embedding(text).transpose(1, 2)
-        # B x T_in_max x D_en
-        encoder_outputs = self.encoder(embedded_inputs, text_lengths)
-        # adding speaker embeddding to encoder output
-        # TODO: multi-speaker
-        # B x speaker_embed_dim
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
-        if self.num_speakers > 1:
-            # B x T_in x embed_dim + speaker_embed_dim
-            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                          self.speaker_embeddings)
-        encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
-        # global style token
-        if self.gst:
-            # B x gst_dim
-            encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
-        # B x mel_dim x T_out -- B x T_out//r x T_in -- B x T_out//r
-        decoder_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, input_mask)
-        # sequence masking
-        if mel_lengths is not None:
-            decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
-        # B x mel_dim x T_out
-        postnet_outputs = self.postnet(decoder_outputs)
-        postnet_outputs = decoder_outputs + postnet_outputs
-        # sequence masking
-        if output_mask is not None:
-            postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs)
-        # B x T_out x mel_dim -- B x T_out x mel_dim -- B x T_out//r x T_in
-        decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
-            decoder_outputs, postnet_outputs, alignments)
-        if self.bidirectional_decoder:
-            decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
-            return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
-        if self.double_decoder_consistency:
-            decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
-            return  decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
-        return decoder_outputs, postnet_outputs, alignments, stop_tokens
-
-    @torch.no_grad()
-    def inference(self, text, speaker_ids=None):
-        embedded_inputs = self.embedding(text).transpose(1, 2)
-        encoder_outputs = self.encoder.inference(embedded_inputs)
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
-        if self.num_speakers > 1:
-            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                          self.speaker_embeddings)
-        decoder_outputs, alignments, stop_tokens = self.decoder.inference(
-            encoder_outputs)
-        postnet_outputs = self.postnet(decoder_outputs)
-        postnet_outputs = decoder_outputs + postnet_outputs
-        decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
-            decoder_outputs, postnet_outputs, alignments)
-        return decoder_outputs, postnet_outputs, alignments, stop_tokens
-
-    def inference_truncated(self, text, speaker_ids=None):
-        """
-        Preserve model states for continuous inference
-        """
-        embedded_inputs = self.embedding(text).transpose(1, 2)
-        encoder_outputs = self.encoder.inference_truncated(embedded_inputs)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                      speaker_ids)
-        mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(
-            encoder_outputs)
-        mel_outputs_postnet = self.postnet(mel_outputs)
-        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
-        mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
-            mel_outputs, mel_outputs_postnet, alignments)
-        return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
-
-
-    def _speaker_embedding_pass(self, encoder_outputs, speaker_ids):
-        # TODO: multi-speaker
-        # if hasattr(self, "speaker_embedding") and speaker_ids is None:
-        #     raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
-        # if hasattr(self, "speaker_embedding") and speaker_ids is not None:
-
-        #     speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
-        #                                                    encoder_outputs.size(1),
-        #                                                    -1)
-        #     encoder_outputs = encoder_outputs + speaker_embeddings
-        # return encoder_outputs
-        pass
diff --git a/models/tacotron_abstract.py b/models/tacotron_abstract.py
deleted file mode 100644
index 75a1a5cd..00000000
--- a/models/tacotron_abstract.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import copy
-from abc import ABC, abstractmethod
-
-import torch
-from torch import nn
-
-from TTS.utils.generic_utils import sequence_mask
-
-
-class TacotronAbstract(ABC, nn.Module):
-    def __init__(self,
-                 num_chars,
-                 num_speakers,
-                 r,
-                 postnet_output_dim=80,
-                 decoder_output_dim=80,
-                 attn_type='original',
-                 attn_win=False,
-                 attn_norm="softmax",
-                 prenet_type="original",
-                 prenet_dropout=True,
-                 forward_attn=False,
-                 trans_agent=False,
-                 forward_attn_mask=False,
-                 location_attn=True,
-                 attn_K=5,
-                 separate_stopnet=True,
-                 bidirectional_decoder=False,
-                 double_decoder_consistency=False,
-                 ddc_r=None,
-                 gst=False):
-        """ Abstract Tacotron class """
-        super().__init__()
-        self.num_chars = num_chars
-        self.r = r
-        self.decoder_output_dim = decoder_output_dim
-        self.postnet_output_dim = postnet_output_dim
-        self.gst = gst
-        self.num_speakers = num_speakers
-        self.bidirectional_decoder = bidirectional_decoder
-        self.double_decoder_consistency = double_decoder_consistency
-        self.ddc_r = ddc_r
-        self.attn_type = attn_type
-        self.attn_win = attn_win
-        self.attn_norm = attn_norm
-        self.prenet_type = prenet_type
-        self.prenet_dropout = prenet_dropout
-        self.forward_attn = forward_attn
-        self.trans_agent = trans_agent
-        self.forward_attn_mask = forward_attn_mask
-        self.location_attn = location_attn
-        self.attn_K = attn_K
-        self.separate_stopnet = separate_stopnet
-
-        # layers
-        self.embedding = None
-        self.encoder = None
-        self.decoder = None
-        self.postnet = None
-
-        # global style token
-        if self.gst:
-            self.gst_layer = None
-
-        # model states
-        self.speaker_embeddings = None
-        self.speaker_embeddings_projected = None
-
-        # additional layers
-        self.decoder_backward = None
-        self.coarse_decoder = None
-
-    #############################
-    # INIT FUNCTIONS
-    #############################
-
-    def _init_states(self):
-        self.speaker_embeddings = None
-        self.speaker_embeddings_projected = None
-
-    def _init_backward_decoder(self):
-        self.decoder_backward = copy.deepcopy(self.decoder)
-
-    def _init_coarse_decoder(self):
-        self.coarse_decoder = copy.deepcopy(self.decoder)
-        self.coarse_decoder.r_init = self.ddc_r
-        self.coarse_decoder.set_r(self.ddc_r)
-
-    #############################
-    # CORE FUNCTIONS
-    #############################
-
-    @abstractmethod
-    def forward(self):
-        pass
-
-    @abstractmethod
-    def inference(self):
-        pass
-
-    #############################
-    # COMMON COMPUTE FUNCTIONS
-    #############################
-
-    def compute_masks(self, text_lengths, mel_lengths):
-        """Compute masks  against sequence paddings."""
-        # B x T_in_max (boolean)
-        device = text_lengths.device
-        input_mask = sequence_mask(text_lengths).to(device)
-        output_mask = None
-        if mel_lengths is not None:
-            max_len = mel_lengths.max()
-            r = self.decoder.r
-            max_len = max_len + (r - (max_len % r)) if max_len % r > 0 else max_len
-            output_mask = sequence_mask(mel_lengths, max_len=max_len).to(device)
-        return input_mask, output_mask
-
-    def _backward_pass(self, mel_specs, encoder_outputs, mask):
-        """ Run backwards decoder """
-        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
-            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
-            self.speaker_embeddings_projected)
-        decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
-        return decoder_outputs_b, alignments_b
-
-    def _coarse_decoder_pass(self, mel_specs, encoder_outputs, alignments,
-                             input_mask):
-        """ Double Decoder Consistency """
-        T = mel_specs.shape[1]
-        if T % self.coarse_decoder.r > 0:
-            padding_size = self.coarse_decoder.r - (T % self.coarse_decoder.r)
-            mel_specs = torch.nn.functional.pad(mel_specs,
-                                                (0, 0, 0, padding_size, 0, 0))
-        decoder_outputs_backward, alignments_backward, _ = self.coarse_decoder(
-            encoder_outputs.detach(), mel_specs, input_mask)
-        # scale_factor = self.decoder.r_init / self.decoder.r
-        alignments_backward = torch.nn.functional.interpolate(
-            alignments_backward.transpose(1, 2),
-            size=alignments.shape[1],
-            mode='nearest').transpose(1, 2)
-        decoder_outputs_backward = decoder_outputs_backward.transpose(1, 2)
-        decoder_outputs_backward = decoder_outputs_backward[:, :T, :]
-        return decoder_outputs_backward, alignments_backward
-
-    #############################
-    # EMBEDDING FUNCTIONS
-    #############################
-
-    def compute_speaker_embedding(self, speaker_ids):
-        """ Compute speaker embedding vectors """
-        if hasattr(self, "speaker_embedding") and speaker_ids is None:
-            raise RuntimeError(
-                " [!] Model has speaker embedding layer but speaker_id is not provided"
-            )
-        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
-            self.speaker_embeddings = self.speaker_embedding(speaker_ids).unsqueeze(1)
-        if hasattr(self, "speaker_project_mel") and speaker_ids is not None:
-            self.speaker_embeddings_projected = self.speaker_project_mel(
-                self.speaker_embeddings).squeeze(1)
-
-    def compute_gst(self, inputs, mel_specs):
-        """ Compute global style token """
-        # pylint: disable=not-callable
-        gst_outputs = self.gst_layer(mel_specs)
-        inputs = self._add_speaker_embedding(inputs, gst_outputs)
-        return inputs
-
-    @staticmethod
-    def _add_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = outputs + speaker_embeddings_
-        return outputs
-
-    @staticmethod
-    def _concat_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
-        return outputs
diff --git a/notebooks/CheckSpectrograms.ipynb b/notebooks/CheckSpectrograms.ipynb
index 66c3c7cc..dbb7a1be 100644
--- a/notebooks/CheckSpectrograms.ipynb
+++ b/notebooks/CheckSpectrograms.ipynb
@@ -16,9 +16,9 @@
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "from TTS.utils.audio import AudioProcessor\n",
-    "from TTS.utils.visual import plot_spectrogram\n",
-    "from TTS.utils.generic_utils import load_config\n",
+    "from TTS.tts.utils.audio import AudioProcessor\n",
+    "from TTS.tts.utils.visual import plot_spectrogram\n",
+    "from TTS.tts.utils.generic_utils import load_config\n",
     "import glob \n",
     "import IPython.display as ipd"
    ]
diff --git a/notebooks/ExtractTTSpectrogram.ipynb b/notebooks/ExtractTTSpectrogram.ipynb
index c747c764..b28489e0 100644
--- a/notebooks/ExtractTTSpectrogram.ipynb
+++ b/notebooks/ExtractTTSpectrogram.ipynb
@@ -22,12 +22,12 @@
     "import numpy as np\n",
     "from tqdm import tqdm as tqdm\n",
     "from torch.utils.data import DataLoader\n",
-    "from TTS.datasets.TTSDataset import MyDataset\n",
-    "from TTS.layers.losses import L1LossMasked\n",
-    "from TTS.utils.audio import AudioProcessor\n",
-    "from TTS.utils.visual import plot_spectrogram\n",
-    "from TTS.utils.generic_utils import load_config, setup_model, sequence_mask\n",
-    "from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
+    "from TTS.tts.datasets.TTSDataset import MyDataset\n",
+    "from TTS.tts.layers.losses import L1LossMasked\n",
+    "from TTS.tts.utils.audio import AudioProcessor\n",
+    "from TTS.tts.utils.visual import plot_spectrogram\n",
+    "from TTS.tts.utils.generic_utils import load_config, setup_model, sequence_mask\n",
+    "from TTS.tts.utils.text.symbols import make_symbols, symbols, phonemes\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
@@ -108,7 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "preprocessor = importlib.import_module('TTS.datasets.preprocess')\n",
+    "preprocessor = importlib.import_module('TTS.tts.datasets.preprocess')\n",
     "preprocessor = getattr(preprocessor, DATASET.lower())\n",
     "meta_data = preprocessor(DATA_PATH,METADATA_FILE)\n",
     "dataset = MyDataset(checkpoint['r'], C.text_cleaner, False, ap, meta_data,tp=C.characters if 'characters' in C.keys() else None, use_phonemes=C.use_phonemes,  phoneme_cache_path=C.phoneme_cache_path, enable_eos_bos=C.enable_eos_bos_chars)\n",
diff --git a/notebooks/TestAttention.ipynb b/notebooks/TestAttention.ipynb
index 92b1d6c4..51413099 100644
--- a/notebooks/TestAttention.ipynb
+++ b/notebooks/TestAttention.ipynb
@@ -36,14 +36,14 @@
     "import librosa\n",
     "import librosa.display\n",
     "\n",
-    "from TTS.layers import *\n",
-    "from TTS.utils.audio import AudioProcessor\n",
-    "from TTS.utils.generic_utils import setup_model\n",
-    "from TTS.utils.io import load_config\n",
-    "from TTS.utils.text import text_to_sequence\n",
-    "from TTS.utils.synthesis import synthesis\n",
-    "from TTS.utils.visual import plot_alignment\n",
-    "from TTS.utils.measures import alignment_diagonal_score\n",
+    "from TTS.tts.layers import *\n",
+    "from TTS.tts.utils.audio import AudioProcessor\n",
+    "from TTS.tts.utils.generic_utils import setup_model\n",
+    "from TTS.tts.utils.io import load_config\n",
+    "from TTS.tts.utils.text import text_to_sequence\n",
+    "from TTS.tts.utils.synthesis import synthesis\n",
+    "from TTS.tts.utils.visual import plot_alignment\n",
+    "from TTS.tts.utils.measures import alignment_diagonal_score\n",
     "\n",
     "import IPython\n",
     "from IPython.display import Audio\n",
@@ -96,7 +96,7 @@
    "outputs": [],
    "source": [
     "# LOAD TTS MODEL\n",
-    "from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
+    "from TTS.tts.utils.text.symbols import make_symbols, symbols, phonemes\n",
     "\n",
     "# multi speaker \n",
     "if CONFIG.use_speaker_embedding:\n",
diff --git a/notebooks/dataset_analysis/AnalyzeDataset-Copy1.ipynb b/notebooks/dataset_analysis/AnalyzeDataset-Copy1.ipynb
new file mode 100644
index 00000000..390b20e2
--- /dev/null
+++ b/notebooks/dataset_analysis/AnalyzeDataset-Copy1.ipynb
@@ -0,0 +1,3406 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "TTS_PATH = \"/home/erogol/projects/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.append(TTS_PATH) # set this if TTS is not installed globally\n",
+    "import glob\n",
+    "import librosa\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from scipy.stats import norm\n",
+    "from tqdm import tqdm_notebook as tqdm\n",
+    "from multiprocessing import Pool\n",
+    "from matplotlib import pylab as plt\n",
+    "from collections import Counter\n",
+    "from TTS.tts.datasets.preprocess import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "DATA_PATH = \"/home/erogol/Data/Spectie/audio/output/\"\n",
+    "META_DATA = \"metadata.txt\"\n",
+    "NUM_PROC = 8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/home/erogol/Data/Spectie/audio/output/metadata.txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-ca0237d02d4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# use your own preprocessor at this stage - TTS/datasets/proprocess.py\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmozilla_de\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATA_PATH\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMETA_DATA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Number of audio files: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/TTS/tts_namespace/TTS/datasets/preprocess.py\u001b[0m in \u001b[0;36mmozilla_de\u001b[0;34m(root_path, meta_file)\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0mspeaker_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"mozilla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtxt_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ISO 8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mttf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mttf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m             \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'|'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/erogol/Data/Spectie/audio/output/metadata.txt'"
+     ]
+    }
+   ],
+   "source": [
+    "# use your own preprocessor at this stage - TTS/datasets/proprocess.py\n",
+    "items = mozilla_de(DATA_PATH, META_DATA)\n",
+    "print(\" > Number of audio files: {}\".format(len(items)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "# check wavs if exist\n",
+    "wav_files = []\n",
+    "for item in items:\n",
+    "    wav_file = item[1].strip()\n",
+    "    wav_files.append(wav_file)\n",
+    "    if not os.path.exists(wav_file):\n",
+    "        print(wav_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_119.wav']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# show duplicate items\n",
+    "c = Counter(wav_files)\n",
+    "duplicates = [item for item, count in c.items() if count > 1]\n",
+    "print(duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "folders = [w.split('/')[5] for w in wav_files]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'BATCH_10_FINAL',\n",
+       " 'BATCH_11_FINAL',\n",
+       " 'BATCH_12_FINAL',\n",
+       " 'BATCH_13_FINAL',\n",
+       " 'BATCH_14_FINAL',\n",
+       " 'BATCH_15_FINAL',\n",
+       " 'BATCH_16_FINAL',\n",
+       " 'BATCH_17_FINAL',\n",
+       " 'BATCH_18_FINAL',\n",
+       " 'BATCH_19_FINAL',\n",
+       " 'BATCH_1_FINAL',\n",
+       " 'BATCH_20_FINAL',\n",
+       " 'BATCH_2_FINAL',\n",
+       " 'BATCH_3_FINAL',\n",
+       " 'BATCH_4_FINAL',\n",
+       " 'BATCH_5_FINAL',\n",
+       " 'BATCH_6_FINAL',\n",
+       " 'BATCH_7_FINAL',\n",
+       " 'BATCH_8_FINAL',\n",
+       " 'BATCH_9_FINAL'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(folders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/erogol/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
+      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afbb94c274fe4913b256a8756584c0f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=14610.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def load_item(item):\n",
+    "    file_name = item[1].strip()\n",
+    "    text = item[0].strip()\n",
+    "    audio = librosa.load(file_name, sr=None)\n",
+    "    sr = audio[1]\n",
+    "    audio = audio[0]\n",
+    "    audio_len = len(audio) / sr\n",
+    "    text_len = len(text)\n",
+    "    return file_name, text, text_len, audio, audio_len\n",
+    "\n",
+    "# This will take a while depending on size of dataset\n",
+    "if NUM_PROC == 1:\n",
+    "    data = []\n",
+    "    for m in tqdm(items):\n",
+    "        data += [load_item(m)]\n",
+    "else:\n",
+    "    with Pool(8) as p:\n",
+    "        data = list(tqdm(p.imap(load_item, items), total=len(items)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/erogol/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
+      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53b7f6adb4db47279927ec064addb3c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=14610.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " > Number of words: 27102\n"
+     ]
+    }
+   ],
+   "source": [
+    "# count words in the dataset\n",
+    "w_count = Counter()\n",
+    "for item in tqdm(data):\n",
+    "    text = item[1].lower().strip()\n",
+    "    for word in text.split():\n",
+    "        w_count[word] += 1\n",
+    "print(\" > Number of words: {}\".format(len(w_count)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/erogol/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
+      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b48c3415e2a4ac1a174502c2308501d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=14610.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "text_vs_durs = {}  # text length vs audio duration\n",
+    "text_len_counter = Counter()  # number of sentences with the keyed length\n",
+    "for item in tqdm(data):\n",
+    "    text = item[1].lower().strip()\n",
+    "    text_len = len(text)\n",
+    "    text_len_counter[text_len] += 1\n",
+    "    audio_len = item[-1]\n",
+    "    try:\n",
+    "        text_vs_durs[text_len] += [audio_len]\n",
+    "    except:\n",
+    "        text_vs_durs[text_len] = [audio_len]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "# text_len vs avg_audio_len, median_audio_len, std_audio_len\n",
+    "text_vs_avg = {}\n",
+    "text_vs_median = {}\n",
+    "text_vs_std = {}\n",
+    "for key, durs in text_vs_durs.items():\n",
+    "    text_vs_avg[key] = np.mean(durs)\n",
+    "    text_vs_median[key] = np.median(durs)\n",
+    "    text_vs_std[key] = np.std(durs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "### Avg audio length per char"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "Collapsed": "false",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_7.wav', 'Schickes Heimkino!', 18, array([1.28518932e-05, 1.68334354e-05, 1.03571265e-05, ...,\n",
+      "       2.77877753e-05, 1.10460878e-05, 2.05760971e-05], dtype=float32), 1.5862083333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_12.wav', 'Das sieht ihm ähnlich.', 23, array([7.6380376e-05, 9.3327515e-05, 6.1386294e-05, ..., 3.4380835e-05,\n",
+      "       2.6692895e-05, 2.2882025e-06], dtype=float32), 1.6567083333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_20.wav', 'Oh, das Programm ist mir neu.', 29, array([-3.6327918e-05, -5.8332487e-05, -5.0294046e-05, ...,\n",
+      "       -3.2606560e-05, -5.3037817e-05, -3.6754736e-05], dtype=float32), 1.8241458333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_21.wav', 'Niemand ist ein Alleskönner.', 29, array([2.5469655e-05, 1.5675920e-05, 2.6378759e-05, ..., 3.4840865e-05,\n",
+      "       3.4687979e-05, 2.3448023e-05], dtype=float32), 1.9034583333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_25.wav', 'Dagegen ist kein Kraut gewachsen.', 33, array([8.6409571e-05, 1.6211446e-04, 1.2149933e-04, ..., 1.4264301e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_39.wav', 'Seid gegrüÃ\\x9ft!', 15, array([-4.95165441e-05, -9.18527076e-05, -1.06668835e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_43.wav', 'Nicht mit dem FuÃ\\x9f!', 19, array([-2.4153460e-05, -9.5195399e-05, -1.8093537e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_44.wav', 'Wissen ist Macht.', 17, array([-1.9221216e-05, -2.1811753e-05, -4.0165878e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_45.wav', 'Guck mal, ein Eichhörnchen!', 28, array([-8.8387278e-05, -7.1484370e-05, -9.1183894e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_58.wav', 'Ich bin mein eigener Hund.', 26, array([-1.3441265e-05, -1.3771249e-05,  2.1415319e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_68.wav', 'Lach ich, oder was?', 19, array([1.20631594e-04, 2.69133277e-04, 3.61918297e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_76.wav', 'Moment mal, das ist neu.', 24, array([-4.0444505e-05, -5.6087447e-05, -7.0869857e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_79.wav', 'Wie lange zieht der Tee schon?', 30, array([ 1.3359761e-05,  1.4845427e-06, -8.4266394e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_95.wav', 'Schlaf gut!', 11, array([-8.3705861e-05, -1.3769916e-04, -1.0772650e-04, ...,\n",
+      "       -1.2876300e-05, -3.5042558e-05, -1.5538299e-05], dtype=float32), 1.0839166666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_97.wav', 'Entschuldigen Sie die Verwechslung!', 35, array([-4.3585667e-05, -4.9360351e-05, -2.4610319e-05, ...,\n",
+      "       -1.4282005e-05, -7.0760620e-07, -2.8634834e-06], dtype=float32), 1.9210833333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_99.wav', 'Schönes Ding!', 14, array([-4.9598326e-05, -4.2029962e-05, -2.2566113e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_101.wav', 'Dann nichts wie weg hier!', 25, array([ 1.2582598e-05,  1.4227808e-05,  1.0588883e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_120.wav', \"Wie geht's?\", 11, array([ 3.6131805e-05,  2.3445213e-05,  4.7948160e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_179.wav', 'Das ganze Haus hat gewackelt.', 29, array([ 1.31893430e-05, -2.02163919e-05, -5.92077959e-06, ...,\n",
+      "       -8.03239527e-06, -1.91841791e-05, -1.46886205e-05], dtype=float32), 1.9034583333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_186.wav', 'Woher kommt all der Hass?', 25, array([-1.0393358e-05, -4.2540119e-05, -1.8952907e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_189.wav', 'Stillgestanden!', 15, array([ 4.4343769e-06,  1.3210945e-05,  1.7683087e-05, ...,\n",
+      "        2.6131744e-05, -5.4923967e-06,  9.4311863e-06], dtype=float32), 1.2689791666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_199.wav', 'Eine Sache zur Zeit.', 20, array([5.1501018e-05, 6.3279913e-05, 7.3763011e-05, ..., 1.0348874e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_218.wav', 'Nichts für ungut!', 18, array([-4.0355466e-05, -4.5107645e-05, -7.7510209e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_220.wav', 'Sieh genau hin!', 15, array([-1.2045763e-02, -1.6849384e-02, -1.4799301e-02, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_224.wav', 'Und welches Baujahr?', 20, array([-3.5566740e-05, -2.3342436e-05, -2.8526230e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_226.wav', 'Sofort umkehren!', 16, array([ 1.2734158e-04,  1.4998924e-04,  1.2418727e-04, ...,\n",
+      "       -6.3872926e-06, -5.1714401e-06, -1.2052229e-05], dtype=float32), 1.3923541666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_232.wav', 'Da muss man locker bleiben.', 27, array([-3.2585725e-05, -3.3840271e-05,  1.3126293e-05, ...,\n",
+      "       -1.8632261e-05, -6.3017387e-06, -5.6675367e-06], dtype=float32), 1.6567083333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_237.wav', 'Probier es mal mit Aceton.', 26, array([ 7.5771743e-05,  1.0223542e-04,  1.0343192e-04, ...,\n",
+      "       -2.1570906e-05, -3.1918564e-05, -1.1135696e-05], dtype=float32), 1.8858125)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_244.wav', 'Kommt drauf an.', 15, array([ 2.7207607e-05,  1.8057373e-05,  1.2512723e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_246.wav', 'Man darf gespannt sein.', 23, array([-2.3668355e-03, -3.7321844e-03, -3.6732492e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_251.wav', 'Daran scheiden sich die Geister.', 32, array([-2.39492147e-05, -4.70898958e-05, -2.53186899e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_258.wav', 'Was habt ihr heute erlebt?', 26, array([ 3.5868085e-05,  8.2530729e-05,  4.6677309e-05, ...,\n",
+      "       -8.4167405e-06, -2.0942105e-05, -6.2113932e-06], dtype=float32), 1.7888958333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_265.wav', 'Lass das sein!', 14, array([2.4356419e-05, 5.5347311e-05, 5.1189338e-05, ..., 2.7182332e-05,\n",
+      "       1.6106302e-05, 2.1714099e-05], dtype=float32), 1.2425208333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_267.wav', 'Auch heute noch.', 16, array([ 1.6202603e-05,  1.8275598e-05,  1.5345126e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_270.wav', 'Wir sehen uns in Bielefeld.', 27, array([5.0975410e-05, 4.6619494e-05, 5.2299667e-05, ..., 2.4641362e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_274.wav', 'Gerald muss Dampf ablassen.', 27, array([-1.4112990e-04, -2.2197423e-04, -2.2060136e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_277.wav', 'Sehen Sie selbst!', 17, array([-3.6524234e-05, -2.8097162e-05,  4.4066533e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_282.wav', 'Haben wir jemanden vergessen?', 29, array([-2.1900923e-05, -8.0311016e-05, -4.5058856e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_300.wav', 'Oh, der Besuch ist da!', 22, array([-1.1763951e-06, -6.4509544e-07, -2.1343028e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_303.wav', 'Kannst du das bitte übernehmen?', 32, array([1.9790201e-05, 2.5795589e-05, 2.3016226e-05, ..., 4.4700668e-05,\n",
+      "       2.9440445e-05, 4.1151830e-05], dtype=float32), 1.965125)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_309.wav', 'Ich muss verrückt sein.', 24, array([-3.7773843e-05, -2.5238944e-05, -4.5549310e-05, ...,\n",
+      "       -1.4228171e-05, -1.3738420e-05, -2.5079733e-05], dtype=float32), 1.4099583333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_312.wav', 'Gestrichen!', 11, array([4.6765574e-05, 8.2428312e-05, 6.1315681e-05, ..., 1.7959255e-06,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_321.wav', 'Gott atmet nicht.', 17, array([3.9337472e-05, 4.7041980e-05, 5.6819965e-05, ..., 1.6601467e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_327.wav', 'Das ist mir auch klar.', 22, array([ 6.4578126e-05,  9.0902526e-05,  7.7864941e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_329.wav', 'Es sieht nach Unsinn aus.', 25, array([ 1.1480927e-06,  7.0667493e-06, -3.8140864e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_333.wav', 'Das ist nur von auÃ\\x9fen.', 23, array([-3.8521201e-05, -4.7468315e-05, -3.4236415e-05, ...,\n",
+      "        5.2493826e-05,  3.7984686e-05,  3.3584591e-05], dtype=float32), 1.9915625)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_334.wav', 'Ich habe gerade ein DéjÃ\\xa0-vu.', 30, array([ 4.4728897e-04,  3.7400136e-04, -4.0894563e-04, ...,\n",
+      "        2.4757979e-05,  1.1479871e-05,  2.5551706e-05], dtype=float32), 1.9387083333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_336.wav', 'Ich muss mich verzählt haben.', 30, array([-3.9173494e-05, -2.9986420e-05, -1.9012801e-05, ...,\n",
+      "       -6.0724019e-06,  2.7600961e-05, -3.4350986e-05], dtype=float32), 1.6831458333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_342.wav', 'So kann man sich täuschen.', 27, array([-3.5296402e-05, -6.0332448e-05, -5.2051670e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_345.wav', 'Ich weiÃ\\x9f nicht woher.', 22, array([-2.05518299e-05, -1.30783865e-05, -1.48754107e-05, ...,\n",
+      "       -5.49699544e-05, -3.01012133e-05, -1.70801268e-05], dtype=float32), 1.4980833333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_352.wav', 'Bist du jetzt beleidigt?', 24, array([-1.0385954e-05,  1.1672010e-05, -2.3844843e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_357.wav', 'Gib mir zwei Minuten, ja?', 25, array([-1.8705783e-05, -3.0273133e-05, -2.4814160e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_360.wav', 'Voll der Psycho-Blick!', 22, array([ 5.0691519e-06,  1.2665058e-05,  1.4902340e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_365.wav', 'Mein Freund ist Musiker.', 24, array([ 4.2413834e-05,  2.3999601e-05,  1.0646096e-05, ...,\n",
+      "       -1.9632445e-05, -2.5183452e-05, -1.8877656e-05], dtype=float32), 1.7272083333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_386.wav', 'Hast du Knoblauch gegessen?', 27, array([ 4.2124993e-06,  1.6061234e-05,  1.6008022e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_391.wav', 'Ist mir gar nicht aufgefallen.', 30, array([-1.2801524e-04, -1.8332504e-04, -1.6864720e-04, ...,\n",
+      "       -1.7935792e-05,  1.3743926e-05,  4.5144670e-06], dtype=float32), 1.6390833333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_396.wav', 'Verdammt noch mal!', 18, array([-1.9188805e-05,  2.9282862e-06,  3.1274089e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_403.wav', 'Klingt moralisch einwandfrei.', 29, array([-1.5154625e-06, -1.1907745e-05, -3.7140951e-06, ...,\n",
+      "        1.4816231e-06, -1.0694354e-05, -2.7909247e-05], dtype=float32), 1.8770208333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_412.wav', 'Wie wunderschön du bist.', 25, array([ 8.1452117e-06,  1.2316134e-05,  1.2410718e-05, ...,\n",
+      "       -2.5919973e-05, -1.5394140e-05, -1.6787388e-05], dtype=float32), 1.7800833333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_419.wav', 'Ich kann nichts erkennen.', 25, array([-2.1261691e-05, -2.6662590e-05, -3.2895186e-05, ...,\n",
+      "       -8.6166056e-06,  1.0871788e-06, -5.8716050e-06], dtype=float32), 1.4363958333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_423.wav', 'Jetzt aber zackig!', 18, array([ 2.4374567e-06,  2.0842881e-05, -1.5250983e-05, ...,\n",
+      "       -1.6002667e-05, -4.2002972e-05, -2.0723968e-05], dtype=float32), 1.2953958333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_425.wav', 'Ich bin schon ganz wirr im Kopf.', 32, array([2.9025901e-05, 3.5920395e-05, 4.5607205e-05, ..., 1.6718976e-05,\n",
+      "       2.1111184e-05, 3.3797973e-05], dtype=float32), 1.98275)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_430.wav', 'Ihr gefällt die Kulisse.', 25, array([ 2.0069625e-05,  6.2984320e-05,  4.6121866e-05, ...,\n",
+      "       -3.1357740e-05, -2.2353357e-05, -2.2545100e-05], dtype=float32), 1.6919583333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_445.wav', 'GrüÃ\\x9f dich!', 12, array([-1.0602423e-05, -7.0546007e-06,  1.1231577e-05, ...,\n",
+      "       -4.8423290e-06, -2.5039872e-05, -2.4532073e-05], dtype=float32), 0.7842916666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_456.wav', 'Nach mir die Sintflut!', 22, array([ 2.0728099e-05, -9.0359263e-06, -4.4944873e-06, ...,\n",
+      "        6.8659042e-06, -1.2404760e-05, -2.2153192e-06], dtype=float32), 1.5862083333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_460.wav', 'Was soll das denn bringen?', 26, array([ 3.9292016e-05,  5.6996982e-05,  6.4746971e-05, ...,\n",
+      "       -3.1001658e-05, -9.7075417e-06, -1.9902369e-05], dtype=float32), 1.7888958333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_461.wav', 'Er lädt immer noch.', 20, array([-1.6651324e-05, -5.8167420e-06,  5.8412393e-06, ...,\n",
+      "       -5.8599158e-05, -5.3942535e-05, -2.6054968e-05], dtype=float32), 1.2337291666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_479.wav', 'Was sollen wir nur tun?', 23, array([-4.4440752e-05, -5.3991145e-05, -4.1732972e-05, ...,\n",
+      "       -5.2980035e-06,  1.0908753e-05,  1.9730707e-05], dtype=float32), 1.8329583333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_481.wav', 'Schluss damit!', 14, array([-2.9023191e-05, -4.2109135e-05, -3.8624265e-05, ...,\n",
+      "       -1.9805097e-05, -6.0203884e-06,  1.1789062e-05], dtype=float32), 0.9605416666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_483.wav', 'Können sie mir ihr Passwort geben?', 35, array([ 2.5537942e-05,  5.2574283e-05,  5.7736743e-05, ...,\n",
+      "       -5.4731267e-06, -2.9014491e-05,  3.6238887e-06], dtype=float32), 1.7624583333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_491.wav', 'Sie muss zum BogenschieÃ\\x9fen.', 28, array([-3.1108371e-05, -5.1357423e-05, -7.0860064e-05, ...,\n",
+      "       -4.0438888e-05, -2.6810346e-06, -1.3582417e-05], dtype=float32), 1.9387083333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_513.wav', 'Gib ihm die Schaufel wieder!', 28, array([-2.5840678e-05, -2.4174828e-05, -1.2895588e-05, ...,\n",
+      "        3.6998503e-05,  3.0887943e-05,  1.9229607e-05], dtype=float32), 1.7448333333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_514.wav', 'Ich will mich kurzfassen.', 25, array([-5.4538796e-06,  1.6863480e-05, -2.4184583e-05, ...,\n",
+      "       -7.9238208e-07,  9.8597202e-06,  2.5041477e-06], dtype=float32), 1.7448333333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_515.wav', 'Die ist hart im Nehmen.', 23, array([ 3.2496322e-05,  3.8166479e-05,  3.2249674e-05, ...,\n",
+      "       -1.0363748e-05,  1.9095280e-05,  9.2708688e-06], dtype=float32), 1.7360208333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_516.wav', 'Oh mein Gott!', 13, array([ 1.0293347e-05,  2.3256578e-05, -2.6419082e-06, ...,\n",
+      "       -1.2127157e-05,  1.4263560e-06,  3.2800324e-06], dtype=float32), 0.8812291666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_517.wav', 'Einer noch!', 11, array([ 1.8490386e-05,  9.7866017e-05,  1.1555837e-04, ...,\n",
+      "       -5.3282761e-08, -1.5481584e-05,  1.1070631e-06], dtype=float32), 0.7578541666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_536.wav', 'Da hat er sich verhaspelt.', 26, array([-1.2101016e-05, -4.1350278e-05, -2.5068364e-05, ...,\n",
+      "       -9.8568984e-05,  1.2527088e-04,  2.5078503e-04], dtype=float32), 1.6390833333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_538.wav', 'Kann ich mir nicht vorstellen.', 30, array([-7.1259085e-05, -6.6917557e-05, -7.5606287e-05, ...,\n",
+      "       -1.7281625e-05,  1.9208239e-06,  9.8984492e-06], dtype=float32), 1.5950208333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_541.wav', 'Kannst du sie mal anstupsen?', 28, array([-3.0119493e-06,  3.5770699e-06,  8.4955855e-06, ...,\n",
+      "        1.3389642e-05,  2.2122082e-05,  1.8456800e-05], dtype=float32), 1.67875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_554.wav', 'Das wird nicht billig.', 22, array([-1.2833251e-05, -2.6942225e-05, -1.1592191e-05, ...,\n",
+      "       -1.1226616e-05,  2.4460544e-05,  4.6120007e-05], dtype=float32), 1.3570833333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_555.wav', 'Ã\\x9cberall wird hier gebaut.', 26, array([ 3.0397489e-06,  1.6576083e-05,  1.7184460e-05, ...,\n",
+      "       -4.7443868e-06,  1.7984281e-07,  1.7898132e-05], dtype=float32), 1.5950208333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_556.wav', 'Was möchten Sie zu trinken?', 28, array([3.6597925e-05, 3.9522194e-05, 3.4265908e-05, ..., 4.9602304e-04,\n",
+      "       4.0240673e-04, 2.1699475e-04], dtype=float32), 1.7888958333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_559.wav', 'Waren Sie schon einmal bei uns?', 31, array([ 2.5204083e-06, -9.7146321e-06,  1.0508998e-05, ...,\n",
+      "        1.6337053e-05,  4.2958636e-05,  3.6466561e-05], dtype=float32), 1.8858333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_579.wav', 'Traut sich sonst noch jemand?', 29, array([-3.4311914e-05, -1.9934920e-05, -3.6420348e-05, ...,\n",
+      "       -8.5477677e-06, -8.7745884e-06, -2.7311040e-05], dtype=float32), 1.9739583333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_587.wav', 'Hier noch mal die Kurzform.', 27, array([ 4.8683055e-06, -9.0082349e-06, -6.4492651e-06, ...,\n",
+      "        1.2890940e-05,  1.4272653e-05,  9.0988487e-06], dtype=float32), 1.9475)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_594.wav', 'Haste mal nen Euro?', 19, array([-8.6395357e-06, -1.0812845e-05, -3.0906973e-05, ...,\n",
+      "        9.5510404e-06,  1.9230547e-05,  3.1346096e-06], dtype=float32), 1.4011458333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_599.wav', 'Wie schreibt man das?', 21, array([-3.6024519e-06, -2.5525418e-05, -2.9170100e-05, ...,\n",
+      "       -1.0803048e-05,  3.5519159e-05,  6.3340508e-06], dtype=float32), 1.6831458333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_600.wav', 'Er kann es nicht mehr hören.', 29, array([-3.8066657e-05, -3.2469205e-05, -5.3206204e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_601.wav', 'Bleib einfach cool.', 19, array([-4.1984731e-05, -2.3916245e-05, -3.1576215e-05, ...,\n",
+      "       -1.8820670e-05,  6.2404342e-07, -9.7557686e-06], dtype=float32), 1.7712708333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_603.wav', 'Davon können Sie ausgehen.', 27, array([ 1.0824577e-05, -1.7968627e-05, -1.6179658e-05, ...,\n",
+      "       -5.5361601e-05, -4.2508735e-05, -3.1106232e-05], dtype=float32), 1.8153333333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_606.wav', 'So ist das im Leben.', 20, array([ 1.0786475e-05, -1.3495748e-05,  6.5641157e-06, ...,\n",
+      "       -3.1349493e-05, -2.5596510e-05, -2.9100025e-05], dtype=float32), 1.6655208333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_625.wav', 'Du musst anders fragen.', 23, array([ 4.8367940e-03,  6.8724523e-03,  6.1804145e-03, ...,\n",
+      "       -7.8923513e-06,  1.7550767e-06,  7.2876783e-06], dtype=float32), 1.7360208333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_628.wav', 'Es war nicht alles schlecht.', 28, array([ 1.08825125e-05,  1.04639130e-05,  8.46001694e-06, ...,\n",
+      "       -2.05042506e-05,  7.06381434e-06,  2.37766089e-05], dtype=float32), 1.7977083333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_643.wav', 'Das lasse ich mir nicht bieten!', 31, array([-8.2775728e-07, -4.0987805e-05, -1.7558119e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_665.wav', 'Hallo, ich bin der Neue!', 24, array([-2.4004371e-04, -3.8098267e-04, -3.8909691e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_667.wav', 'Fastest du?', 11, array([-6.0218765e-05, -8.1393919e-05, -8.6645297e-05, ...,\n",
+      "        6.8678496e-06, -8.2385115e-05, -5.4868913e-05], dtype=float32), 1.2072708333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_675.wav', 'Nur um das klarzustellen.', 25, array([ 2.7598284e-05,  4.3499585e-05, -7.3542742e-06, ...,\n",
+      "        4.4517365e-06, -9.3571025e-06,  3.8795395e-05], dtype=float32), 1.8681875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_699.wav', 'Jetzt wird es gemein.', 21, array([ 2.8973442e-05,  5.4584369e-05,  2.5356880e-05, ...,\n",
+      "        7.6631528e-05,  5.6628844e-05, -4.1394928e-06], dtype=float32), 1.8681875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_704.wav', 'So sieht das aus.', 17, array([7.2620540e-05, 1.0683333e-04, 1.9689680e-04, ..., 2.9477818e-05,\n",
+      "       1.5229379e-05, 4.7805424e-05], dtype=float32), 1.7448333333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_710.wav', 'Gute Nacht ihr Lausbuben!', 25, array([-3.4681521e-04, -4.7425818e-04, -4.6133957e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_727.wav', 'Tschüss, Mädels!', 18, array([ 5.8768086e-07, -7.6773445e-05, -4.4017674e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_750.wav', 'Geh mir nicht auf den Keks.', 27, array([ 3.7033031e-05, -1.8765691e-05,  3.5605895e-05, ...,\n",
+      "       -4.1894207e-05, -5.0918239e-05, -8.2971856e-05], dtype=float32), 1.8505833333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_758.wav', \"Dir werd ich's zeigen.\", 22, array([ 5.9986287e-05,  3.1676023e-05,  9.2681257e-05, ...,\n",
+      "       -2.7595996e-05, -4.2494954e-05, -1.1851616e-06], dtype=float32), 1.8505833333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_764.wav', 'Macht euch bereit!', 18, array([1.5598367e-04, 1.9868747e-04, 1.1692408e-04, ..., 8.2378487e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_766.wav', 'Da kiekste wa?', 14, array([ 5.4184136e-07, -6.1094812e-05, -6.1461476e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_778.wav', 'Das gibt es ja nicht!', 21, array([ 2.0350570e-04,  3.1676778e-04,  2.1080665e-04, ...,\n",
+      "       -6.1200735e-05,  1.1813832e-05, -2.1792879e-05], dtype=float32), 1.3570833333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_10_FINAL/10_789.wav', 'Das ist nicht mein Problem.', 27, array([-5.5885310e-05, -6.4690561e-05, -3.0270432e-05, ...,\n",
+      "       -7.1330876e-05, -1.6931441e-05, -1.1536635e-05], dtype=float32), 1.8858333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_23.wav', 'Finde dich damit ab.', 20, array([ 7.2009592e-05, -2.1050539e-05, -8.4551131e-05, ...,\n",
+      "        5.7306173e-05,  9.7603959e-05,  1.5820342e-04], dtype=float32), 1.3394583333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_41.wav', 'Wie im Wilden Westen!', 21, array([ 1.4756477e-05,  3.1426986e-05,  9.2355578e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_63.wav', 'Da gehe ich mit.', 16, array([-1.10742374e-04, -1.88132090e-05,  1.54691588e-05, ...,\n",
+      "        2.89936361e-06, -3.01086147e-05,  3.05973408e-05], dtype=float32), 1.7183958333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_75.wav', 'Warum nur werktags?', 19, array([-0.00052728, -0.00052381, -0.00042873, ..., -0.00014365,\n",
+      "       -0.00010449, -0.00010741], dtype=float32), 1.7183958333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_76.wav', 'Geht ihr zur Kommunion?', 23, array([-1.0898075e-04, -9.7388023e-05, -6.8978305e-05, ...,\n",
+      "       -5.0831288e-05, -1.5921889e-05,  6.4072694e-05], dtype=float32), 1.7271979166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_80.wav', 'Ihr Blick spricht Bände.', 25, array([-4.6483423e-05, -1.6536529e-04, -9.5357966e-05, ...,\n",
+      "       -8.0715154e-06, -4.8390953e-05, -5.0536739e-05], dtype=float32), 1.6655104166666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_85.wav', 'Ruhe in Frieden.', 16, array([ 1.12481954e-04,  1.02392871e-04,  1.89193961e-05, ...,\n",
+      "       -1.02047234e-05, -6.91346722e-05, -7.76782108e-05], dtype=float32), 1.7095729166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_97.wav', 'Es wird hart gekämpft.', 23, array([-0.0001628 , -0.00018412, -0.00010292, ...,  0.0001769 ,\n",
+      "        0.00018152,  0.00018817], dtype=float32), 1.8681979166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_98.wav', 'Warum das alles?', 16, array([-9.8717544e-05, -8.1991704e-05, -1.4659751e-04, ...,\n",
+      "       -6.5778313e-06, -7.7343866e-05,  1.8901783e-05], dtype=float32), 1.3218333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_109.wav', 'Und Action!', 11, array([-2.8484770e-05,  8.8463985e-06,  5.4628901e-05, ...,\n",
+      "        6.9029898e-05, -7.5049247e-06,  2.7110993e-05], dtype=float32), 1.23371875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_112.wav', 'Bist du dir sicher?', 19, array([ 1.8312603e-05, -8.6757791e-07, -5.3837293e-06, ...,\n",
+      "        1.1187289e-05, -3.2346459e-05,  9.6363983e-06], dtype=float32), 1.6302708333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_113.wav', 'Nur über meine Leiche!', 23, array([ 7.7449629e-05,  1.5036203e-04,  1.0243297e-04, ...,\n",
+      "       -9.4819125e-06, -6.9288013e-05,  2.3950559e-05], dtype=float32), 1.8858229166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_120.wav', 'Hoffentlich schafft er das.', 27, array([-1.6298418e-05,  1.6150392e-05,  2.2071041e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_147.wav', 'Komm, spiel mit mir!', 20, array([ 1.9483854e-05,  1.7799211e-06,  3.3775228e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_152.wav', 'Ui ui ui!', 9, array([5.5120941e-05, 5.6017692e-05, 4.3216096e-06, ..., 7.1505703e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_165.wav', 'Ich war nicht dabei.', 20, array([-9.2572387e-05, -7.4509240e-05, -3.5020537e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_170.wav', 'Danke für die Einladung.', 25, array([-5.4829288e-05, -5.2409945e-05, -1.6216440e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_197.wav', 'So soll es sein.', 16, array([ 6.0843304e-05,  1.4244186e-05, -1.4521269e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_200.wav', 'Erschütternd!', 14, array([-1.85466139e-04, -1.61985561e-04, -1.26282161e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_203.wav', 'Nur das Ã\\x9cbliche.', 17, array([ 7.9542246e-05,  8.5164116e-05,  5.9246326e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_206.wav', 'Die hat nämlich ein Loch.', 26, array([-1.4263311e-05,  3.4131535e-05, -3.4750206e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_230.wav', 'Hol das Stöckchen.', 19, array([-0.00064988, -0.00065917, -0.00059873, ...,  0.00020419,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_237.wav', 'Und bei dir?', 12, array([-2.9914919e-04, -2.2948935e-04, -2.3748397e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_264.wav', 'Es liegt in der Natur der Sache.', 32, array([ 3.1785059e-04,  3.4756004e-04,  3.4774767e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_268.wav', 'Mission erfolgreich!', 20, array([-5.1757845e-05, -2.9873547e-05, -5.2602922e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_274.wav', 'Kommt nicht in die Tüte!', 25, array([-2.6346192e-05, -6.4550313e-06, -4.2296477e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_284.wav', 'Ja, guten Tag!', 14, array([ 3.1975062e-05,  7.6259523e-05,  7.8669080e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_308.wav', 'Es ist noch nicht lange her.', 28, array([ 2.2859822e-06,  6.0211198e-05,  5.7821064e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_316.wav', 'Wiedersehen!', 12, array([2.8599703e-05, 6.1528997e-05, 8.9646070e-05, ..., 2.7208553e-06,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_328.wav', 'Mir ist schwindelig.', 20, array([ 2.4521294e-05,  5.4549360e-05,  2.9534258e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_329.wav', 'Sprechen sie deutsch?', 21, array([-2.4279220e-04, -2.6937225e-04, -2.3713916e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_360.wav', 'So war es nicht gemeint.', 24, array([-5.8561371e-05,  8.4504954e-06,  3.6038864e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_399.wav', 'Schluss jetzt!', 14, array([ 1.60011361e-04,  1.10784895e-04,  1.05728453e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_414.wav', 'Sehen Sie genau hin!', 20, array([ 4.0775692e-05,  7.8341225e-05,  5.9709568e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_432.wav', 'Christina Habeck?', 17, array([-7.0921145e-05, -8.7887020e-05, -1.0741340e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_491.wav', 'Olé, olé!', 11, array([-3.5300669e-05, -3.0546897e-05, -4.6127847e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_498.wav', 'Nur mal so als Anregung.', 24, array([-5.8754493e-05, -2.6690983e-05, -4.8782116e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_503.wav', 'Ich glaube ihr kein Wort.', 25, array([-1.92081643e-06, -2.77346317e-05, -5.22437476e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_537.wav', 'Wie könnt ihr es wagen?', 24, array([-1.4561453e-03, -1.4608348e-03, -1.4617005e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_540.wav', 'Nach was schmeckt das genau?', 28, array([5.2316565e-05, 4.9443977e-05, 5.7626901e-05, ..., 2.5021756e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_548.wav', 'Gänsehaut pur!', 15, array([-9.5325144e-05, -7.7983823e-05, -6.6722314e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_564.wav', 'Höret, höret!', 15, array([-6.9055131e-05, -6.1163970e-05, -7.0053116e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_574.wav', 'Das Haus ist umstellt.', 22, array([ 4.3151813e-05,  5.5632776e-05,  2.7663889e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_606.wav', 'Den versteht keiner.', 20, array([-6.2417603e-05, -8.2428480e-05, -4.4267428e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_612.wav', 'Halten Sie sich fest!', 21, array([2.8007184e-05, 3.2632157e-05, 6.2635645e-06, ..., 5.3581707e-06,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_640.wav', 'Können Sie sich ausweisen?', 27, array([-4.1133004e-05, -3.4346365e-05, -2.0997140e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_645.wav', 'Genug ist genug.', 16, array([1.4217473e-04, 1.3088981e-04, 1.2007774e-04, ..., 8.0914921e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_647.wav', 'Da bin ich ganz bei Ihnen!', 26, array([-6.2454426e-05, -7.3873220e-05, -9.7365184e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_666.wav', 'Ich hasse dich!', 15, array([-4.7738231e-06,  1.0362664e-06,  9.6731110e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_676.wav', 'Jetzt weiÃ\\x9f ich es wieder.', 26, array([-2.9731807e-05, -2.5498804e-05, -5.7221558e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_708.wav', 'Täuschkörper einsetzen!', 25, array([3.3980694e-05, 5.6047942e-05, 3.6845995e-05, ..., 2.0433601e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_711.wav', 'So sind die Regeln.', 19, array([ 1.0646171e-05,  2.1217951e-05, -8.0062582e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_712.wav', 'Es schmeckt nach Zimt.', 22, array([ 2.2929296e-05,  2.9111379e-05,  4.6064979e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_717.wav', 'Auch bei feuchtem Wetter nicht.', 31, array([1.6887316e-05, 6.2355371e-05, 7.5977659e-05, ..., 1.6490449e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_731.wav', 'Warum denn nicht?', 17, array([ 6.4304750e-06, -6.7788221e-07, -1.0204109e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_746.wav', 'Was isst du da?', 15, array([ 4.1260464e-05,  1.0193682e-05,  3.5085955e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_750.wav', 'Alle schreien hier!', 19, array([-1.3079788e-04, -1.3171590e-04, -1.1580650e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_754.wav', 'Das ist genau mein Ding.', 24, array([-1.1629934e-05, -2.1403244e-05,  1.6778110e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_757.wav', 'Wo denken Sie hin?', 18, array([ 2.1430247e-05,  2.1772265e-05,  2.0838190e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_758.wav', 'Reine Gewöhnungssache.', 23, array([-4.3785589e-05, -4.8620215e-05, -4.8604503e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_760.wav', 'Tschüss!', 9, array([1.6893557e-05, 3.7733011e-05, 4.6923491e-05, ..., 3.5450230e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_765.wav', 'Vergiss die Waschtasche nicht!', 30, array([-5.2931227e-05, -5.9350517e-05, -5.4635959e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_788.wav', 'Längs oder quer?', 17, array([-5.8456011e-05, -4.5964895e-05, -2.6546955e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_14.wav', 'Wer hat euch geschickt?', 23, array([-1.1148760e-04,  2.4612555e-05,  9.3476447e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_34.wav', 'Wo bin ich hier nur gelandet?', 29, array([-1.3307537e-05, -1.0089541e-04, -1.2360289e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_40.wav', 'Natürlich behauptet sie das.', 29, array([ 1.2778574e-04,  5.9959311e-05, -8.1008322e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_43.wav', 'Du hattest recht.', 17, array([-1.1000242e-04, -1.6242996e-04, -2.2294538e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_44.wav', 'Verklagen Sie mich doch!', 24, array([ 1.94306958e-05,  1.91541476e-04,  6.15894969e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_45.wav', 'Die Bremse schleift.', 20, array([ 1.8599353e-04,  8.8273533e-05,  1.5005667e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_51.wav', 'Hilfe!', 6, array([-1.7958642e-04, -2.2338594e-04, -2.7969983e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_57.wav', 'Jetzt liegt es an dir.', 22, array([ 2.1328227e-04,  8.1810067e-05, -1.6158322e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_58.wav', 'Wo kann ich das kaufen?', 23, array([-9.1674337e-05, -1.6169342e-04, -1.8347435e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_60.wav', 'Kann man jetzt auch nicht mehr ändern.', 39, array([-3.5826775e-04, -3.3033665e-04, -2.3628448e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_68.wav', 'Hör mir doch mal zu.', 21, array([-1.0109342e-04, -3.4855773e-06,  9.0611480e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_75.wav', 'Gibt es die Person wirklich?', 28, array([1.8891362e-04, 2.3809298e-04, 1.1160582e-04, ..., 2.3936841e-06,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_79.wav', 'Wo waren wir stehen geblieben?', 30, array([-6.7620305e-05,  3.2152042e-05,  6.8106332e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_91.wav', 'Grundgütiger!', 14, array([ 7.70497209e-05, -5.13312625e-05,  7.22193681e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_92.wav', 'Wer bist du?', 12, array([-4.3348764e-04, -4.4667200e-04, -4.2408684e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_95.wav', 'Schon gut.', 10, array([-3.07407812e-04, -4.31929773e-04, -5.19388705e-04, ...,\n",
+      "       -1.07154076e-04, -7.57433227e-05, -1.24133236e-04], dtype=float32), 0.9173333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_99.wav', 'Murat, was ist los mit dir?', 27, array([-3.84323685e-05,  6.48807691e-05, -5.84455011e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_101.wav', 'HeiÃ\\x9fe Würstchen!', 18, array([-0.00027939, -0.00039175, -0.00025548, ...,  0.00027689,\n",
+      "        0.00011903,  0.00012768], dtype=float32), 1.3866666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_106.wav', 'Ich will auch mal einer werden.', 31, array([ 1.36086979e-04, -1.76298781e-05, -4.00176577e-05, ...,\n",
+      "        1.72844579e-04,  1.29597363e-04, -1.02162725e-04], dtype=float32), 1.8986666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_112.wav', 'Ich will auch haben!', 20, array([-4.40885342e-05, -2.34828622e-04, -3.29593284e-04, ...,\n",
+      "       -3.05666414e-04, -1.31685141e-04, -1.00833015e-04], dtype=float32), 1.7173333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_116.wav', 'Setz dich bitte gerade hin!', 27, array([-2.2211492e-04, -2.0630175e-04, -1.4655131e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_122.wav', 'Findest du mich erwachsen?', 26, array([3.0208268e-04, 3.6579225e-04, 3.3154435e-04, ..., 6.2579543e-06,\n",
+      "       4.9250040e-05, 1.8107957e-04], dtype=float32), 1.696)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_123.wav', 'Schrei nicht so!', 16, array([ 8.03208750e-05,  1.33657450e-04, -1.13144284e-04, ...,\n",
+      "        4.64295183e-04,  4.82034549e-04,  2.86602415e-04], dtype=float32), 1.152)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_136.wav', 'Das kam unerwartet.', 19, array([-3.3067852e-05, -4.8878199e-05,  5.8831414e-05, ...,\n",
+      "       -3.5621467e-04, -3.7723745e-04, -2.3875662e-04], dtype=float32), 1.7386666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_149.wav', 'Das ergibt doch keinen Sinn.', 28, array([6.0471892e-05, 8.1125305e-05, 2.7437322e-04, ..., 9.1583250e-05,\n",
+      "       2.0055164e-04, 2.2477485e-04], dtype=float32), 1.9733333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_155.wav', 'Aller Abschied fällt schwer.', 29, array([-2.2813781e-04, -5.5478893e-05,  1.6814301e-04, ...,\n",
+      "        1.2765558e-04,  1.7368943e-04,  2.6105065e-04], dtype=float32), 1.6533333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_165.wav', 'Erkennst du mich nicht?', 23, array([-2.3624673e-04, -3.1934463e-04, -2.9434697e-04, ...,\n",
+      "        1.7059442e-04,  1.9742029e-06,  1.3172596e-04], dtype=float32), 1.4293333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_169.wav', 'Willst du sie mal streicheln?', 29, array([ 1.9991475e-04,  3.4090909e-04,  3.2008073e-04, ...,\n",
+      "        4.6425943e-05, -8.5656990e-05, -1.2934266e-05], dtype=float32), 1.9413333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_181.wav', 'Zur Anmeldung klicken Sie hier.', 31, array([ 5.3989668e-05, -9.8630007e-05, -1.1361165e-04, ...,\n",
+      "       -2.2555150e-05,  3.3015600e-05,  1.0129590e-04], dtype=float32), 1.92)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_194.wav', 'Elvis war nie tot.', 18, array([-6.78355209e-05, -5.90024465e-05, -1.47034181e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_196.wav', 'Irgendetwas zu verzollen?', 25, array([-1.2399687e-04, -3.0497483e-06, -1.2210968e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_216.wav', 'Du bist doch nicht aus Zucker.', 30, array([-3.7417009e-05, -2.1370529e-04, -1.0503333e-04, ...,\n",
+      "       -3.4687804e-05, -1.0006884e-04,  8.2270970e-05], dtype=float32), 1.9626666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_217.wav', 'Bald hat er sein Abi.', 21, array([-7.6955817e-05, -7.4724245e-05, -5.4779473e-05, ...,\n",
+      "       -3.2609492e-05, -1.9532166e-04, -4.0988740e-05], dtype=float32), 1.7173333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_227.wav', 'Da lacht das Herz.', 18, array([0.000232  , 0.00019664, 0.00015979, ..., 0.00012966, 0.0001156 ,\n",
+      "       0.00015061], dtype=float32), 1.664)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_238.wav', 'Steht mir die Bluse?', 20, array([ 5.00293754e-05,  1.15090246e-04, -1.61606382e-04, ...,\n",
+      "       -1.10758898e-04,  9.87306703e-05,  2.25929121e-04], dtype=float32), 1.3653333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_240.wav', 'Kommt ihr zurecht?', 18, array([-1.4166623e-04, -1.7185905e-04, -1.0146119e-04, ...,\n",
+      "       -1.9281202e-05, -4.6475827e-05, -7.9622550e-05], dtype=float32), 1.5466666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_246.wav', 'Her damit!', 10, array([-1.0743736e-04, -6.3287393e-05,  5.4618115e-05, ...,\n",
+      "        1.7166793e-04,  1.5052129e-04, -4.3305259e-05], dtype=float32), 0.9386666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_256.wav', 'Talente muss man fördern.', 26, array([ 2.9789119e-06,  2.0445570e-05,  3.6582744e-05, ...,\n",
+      "       -8.0595542e-05,  2.8049317e-06, -2.4196431e-04], dtype=float32), 1.6426666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_298.wav', 'Kein Kommentar!', 15, array([2.0757825e-04, 2.0225085e-05, 1.0584419e-04, ..., 2.2611262e-05,\n",
+      "       2.2597586e-04, 5.2457988e-05], dtype=float32), 1.1093333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_304.wav', 'Der atmet noch.', 15, array([-0.0001642 , -0.00022683, -0.00021831, ...,  0.00013961,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_308.wav', 'Das dauert aber lange!', 22, array([4.1067542e-05, 4.3461972e-05, 1.7915755e-04, ..., 1.1849359e-04,\n",
+      "       1.6261388e-04, 1.4937650e-05], dtype=float32), 1.44)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_311.wav', 'Du kennst mich, Danton.', 23, array([-5.2089547e-04, -4.7035489e-04, -5.9835758e-04, ...,\n",
+      "       -9.4374191e-05, -2.0053205e-05,  1.2992002e-06], dtype=float32), 1.8346666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_314.wav', 'Mein Gott, Walter!', 18, array([ 4.9858125e-05, -2.4514409e-05, -4.7797763e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_318.wav', 'Und was machst du sonst so?', 27, array([ 0.00041733,  0.00037329,  0.00035271, ..., -0.00016106,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_331.wav', 'Dort wird dir geholfen.', 23, array([-1.9671346e-04, -1.1574107e-04,  5.4965103e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_336.wav', 'Was ist denn hier los?', 22, array([0.00012079, 0.00029083, 0.00013022, ..., 0.00036718, 0.00031168,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_358.wav', 'Gleich sind wir dort.', 21, array([ 1.5992192e-04,  2.5509403e-04,  2.3052108e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_366.wav', 'Sind sie gut informiert?', 24, array([-1.2915327e-04,  5.4154119e-05,  9.4311297e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_376.wav', \"Was soll's, ich bin bereit.\", 27, array([-0.00025371, -0.00037118, -0.00054651, ..., -0.00013142,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_387.wav', 'Was soll das heiÃ\\x9fen?', 21, array([ 6.26799228e-05, -1.15550021e-04, -1.60253039e-04, ...,\n",
+      "       -1.14853225e-04,  3.62789683e-06, -1.25641367e-04], dtype=float32), 1.6106666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_398.wav', 'Oder so!', 8, array([-0.00011172, -0.00021632, -0.0003379 , ...,  0.00016637,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_431.wav', 'Fauche mich nicht so an!', 24, array([-1.69856430e-04, -2.14659201e-04, -1.17017007e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_439.wav', 'Genau zweihundert.', 18, array([ 4.3691549e-04,  4.2721629e-04,  2.1283170e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_446.wav', 'Ja ja, das schickt!', 19, array([-1.5079082e-05,  1.2119063e-04,  1.9518439e-04, ...,\n",
+      "       -8.6470172e-05, -3.4930470e-04, -3.7717246e-04], dtype=float32), 1.7173333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_460.wav', 'Stein schlägt Schere.', 22, array([ 5.7708825e-05,  1.6740670e-04,  1.9982990e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_468.wav', 'Simsalabim!', 11, array([-1.8192175e-05, -1.2427589e-04,  4.0916457e-05, ...,\n",
+      "       -3.6532696e-05,  2.9238325e-05,  2.0148496e-05], dtype=float32), 1.0506666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_478.wav', 'Bitte Zutreffendes ankreuzen.', 29, array([-5.4858734e-05, -6.8480607e-05, -7.1117909e-05, ...,\n",
+      "       -3.5092820e-05,  4.6205354e-05,  3.1237360e-05], dtype=float32), 1.968)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_480.wav', 'Dich kenne ich doch!', 20, array([-3.4106572e-04, -2.6489299e-04, -1.9887066e-04, ...,\n",
+      "        5.8086891e-05,  2.0823347e-04, -4.3870667e-05], dtype=float32), 1.4026666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_484.wav', 'Und los!', 8, array([ 2.0759732e-04,  2.4903464e-04, -3.9741102e-05, ...,\n",
+      "       -1.4017121e-04, -2.2582384e-04, -2.2852831e-04], dtype=float32), 0.8906666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_485.wav', 'Der Patient ist eh schon tot.', 29, array([ 2.8383749e-04,  1.6098749e-04,  5.8996215e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_487.wav', 'Und zwar nicht zu knapp!', 24, array([-4.9983555e-05,  1.0859955e-04,  1.3262806e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_488.wav', 'Was ist mit dem Co-Piloten?', 27, array([-4.6707326e-04, -3.3664281e-04, -1.6913723e-04, ...,\n",
+      "        9.7057833e-05, -3.0600113e-05, -3.3933247e-05], dtype=float32), 1.9626666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_510.wav', 'Sie würde ihr letztes Hemd geben.', 34, array([ 1.5112071e-04,  9.9046929e-06, -7.1756775e-05, ...,\n",
+      "        1.4958363e-04,  2.2523174e-04,  4.5510088e-04], dtype=float32), 1.92)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_521.wav', 'Das wird eh nur Werbung sein.', 29, array([-0.00043494, -0.00045403, -0.00052693, ..., -0.00037776,\n",
+      "       -0.00013905, -0.00029146], dtype=float32), 1.84)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_539.wav', 'Jetzt gibt es Kloppe.', 21, array([ 5.6757370e-05,  1.2752461e-05, -1.0132902e-04, ...,\n",
+      "       -2.8363563e-04, -4.8957689e-04, -4.9631519e-04], dtype=float32), 1.4666666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_549.wav', 'Nee, lieber nicht.', 18, array([-6.2041539e-03, -6.1025852e-03, -5.7721483e-03, ...,\n",
+      "       -4.7201215e-06, -8.9430447e-05, -4.9632461e-05], dtype=float32), 1.5626666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_563.wav', 'Er soll schlieÃ\\x9flich etwas lernen.', 34, array([-5.03349729e-05, -2.22053477e-05,  5.14282438e-05, ...,\n",
+      "        1.08890556e-04,  3.83222614e-05,  6.10036659e-05], dtype=float32), 1.8346666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_566.wav', 'Angeblich ja.', 13, array([ 1.7242544e-04,  1.8572621e-04,  1.3631192e-04, ...,\n",
+      "       -4.0973751e-05, -1.5965881e-04, -1.0953719e-04], dtype=float32), 1.2373333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_567.wav', 'Wie wäre es mit Wiesbaden?', 27, array([-9.5517004e-05, -2.3826263e-04, -1.0132407e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_576.wav', 'Hört mal zu, ihr Checker!', 26, array([-0.00049925, -0.00049119, -0.00044878, ...,  0.00019171,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_580.wav', \"Irgendwann wird's langweilig.\", 29, array([-0.00039041, -0.00038523, -0.00025343, ..., -0.00031044,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_586.wav', 'Spuck ihn wieder aus!', 21, array([ 0.00012375,  0.00025117,  0.0001871 , ..., -0.00021903,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_589.wav', 'Unterschätze den Knirps nicht.', 31, array([2.5606243e-04, 2.5400775e-04, 2.3841709e-04, ..., 2.1033855e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_596.wav', 'Darf ich vorkosten?', 19, array([-1.3477511e-04, -2.3315112e-04,  1.3153857e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_597.wav', 'Ich traue mich nicht!', 21, array([-2.9329595e-04, -3.9892262e-04, -2.9478277e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_602.wav', 'Warum seid ihr so leise?', 24, array([ 2.9226076e-05,  1.6949150e-04,  1.3950269e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_603.wav', 'Nun stellt euch nicht so an!', 28, array([1.4806543e-04, 1.4012858e-04, 7.7195640e-05, ..., 1.4235765e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_608.wav', 'Das Essen wird kalt.', 20, array([ 2.36780070e-05, -1.06394495e-04, -1.18256241e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_614.wav', 'Fachidioten soll es auch geben.', 31, array([ 7.9924423e-05,  2.0709680e-04, -6.6771558e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_627.wav', 'Du bist vielleicht eine Knalltüte!', 35, array([ 1.7171216e-04, -3.8676033e-05, -8.2237340e-05, ...,\n",
+      "       -1.8530877e-04, -1.3380373e-04, -1.6169780e-04], dtype=float32), 1.8773333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_629.wav', 'Natürlich nicht seine eigene.', 30, array([-2.2751655e-04, -1.5005520e-04, -9.8528086e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_630.wav', 'Halten Sie die Presse zurück!', 30, array([ 3.1129293e-06,  7.3669260e-05,  3.3459681e-05, ...,\n",
+      "       -1.5276406e-04,  2.6472675e-05, -1.9852230e-05], dtype=float32), 1.76)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_636.wav', 'Ruf schnell die Polizei!', 24, array([5.1400399e-05, 6.7014749e-05, 5.1501669e-05, ..., 1.8976731e-04,\n",
+      "       2.0147586e-04, 1.5075490e-04], dtype=float32), 1.5573333333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_637.wav', 'Dann nimmt man sie sich.', 24, array([-0.00050762, -0.00047607, -0.00053025, ...,  0.00035113,\n",
+      "        0.00017673,  0.00026363], dtype=float32), 1.856)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_674.wav', 'Gibst du mir deine Nummer?', 26, array([-1.0660516e-04, -1.8238377e-05,  9.7913333e-05, ...,\n",
+      "        3.0329258e-05,  9.0803427e-05,  2.0600615e-05], dtype=float32), 1.536)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_675.wav', 'Man kann nicht alles haben.', 27, array([ 3.6246947e-04,  3.3836463e-04,  3.9515106e-04, ...,\n",
+      "        1.9603693e-05, -1.0797187e-07,  4.7195343e-05], dtype=float32), 1.696)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_686.wav', 'Wie oft denn noch?', 18, array([-0.00025807, -0.00045327, -0.00041516, ..., -0.00053778,\n",
+      "       -0.00065512, -0.00057833], dtype=float32), 1.2906666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_703.wav', 'Der Erste in was?', 17, array([3.7513164e-05, 2.3692524e-05, 9.2795723e-05, ..., 1.8559145e-04,\n",
+      "       8.4898209e-05, 1.3820640e-05], dtype=float32), 1.4323645833333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_720.wav', 'Wie denn nun?', 13, array([-7.8975081e-06, -2.1718148e-05,  2.7641279e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_722.wav', 'Ihm wäre das zu müÃ\\x9fig.', 25, array([ 5.1333802e-05,  6.3180065e-05, -1.3820640e-05, ...,\n",
+      "       -1.9743769e-05,  3.9487541e-06, -4.7385049e-05], dtype=float32), 1.93334375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_724.wav', 'Ã\\x96l ist ausgelaufen.', 20, array([-3.7513164e-05, -7.8975081e-06, -1.5795016e-05, ...,\n",
+      "       -1.3820640e-05, -1.3820640e-05,  4.5410670e-05], dtype=float32), 1.6087708333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_730.wav', 'Willkommen im Neuland!', 22, array([-6.910320e-05, -6.515444e-05,  1.382064e-05, ..., -3.356441e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_737.wav', 'Kannst du mich mal zwicken?', 27, array([ 3.9487539e-05,  3.9487541e-06,  3.3564411e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_752.wav', 'Friede sei mit dir.', 19, array([-0.00018362, -0.00025075, -0.00027839, ..., -0.00025864,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_753.wav', 'Mit Speck fängt man Mäuse.', 28, array([-1.61898919e-04, -1.04641986e-04, -8.68725911e-05, ...,\n",
+      "       -5.92313118e-05,  6.31800649e-05,  7.70007027e-05], dtype=float32), 1.6087604166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_766.wav', 'Bin ich die Auskunft oder was?', 30, array([2.96156559e-05, 1.04641986e-04, 1.26360130e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_767.wav', 'Sesam, öffne dich!', 19, array([-3.8500351e-04, -3.3366971e-04, -3.5933661e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_772.wav', 'Er kennt seine Pappenheimer.', 28, array([-3.7513164e-05, -1.9743769e-05, -1.3820640e-05, ...,\n",
+      "       -8.6872591e-05, -1.5202703e-04, -1.7177081e-04], dtype=float32), 1.7146145833333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_792.wav', 'Da geht noch was.', 17, array([ 2.0336083e-04,  1.6979642e-04,  1.6189892e-04, ...,\n",
+      "       -4.9359427e-05, -2.9615656e-05, -7.3051953e-05], dtype=float32), 1.25596875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_803.wav', 'Er macht es eben gründlich.', 28, array([-5.5282559e-05, -8.2923834e-05,  1.9743769e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_816.wav', 'Spionierst du mich aus?', 23, array([3.5538786e-04, 4.5015797e-04, 4.8767112e-04, ..., 4.3436296e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_817.wav', 'Komm zurück!', 13, array([4.0672167e-04, 2.2902773e-04, 6.3180065e-05, ..., 3.7513164e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_819.wav', 'Sie schwebt auf Wolke sieben.', 29, array([7.5026328e-05, 1.2438576e-04, 1.5005266e-04, ..., 1.1056512e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_826.wav', 'Wehret den Anfängen!', 21, array([ 1.4610391e-04,  1.3425764e-04,  1.2636013e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_834.wav', 'Altes Haus, lass dich drücken!', 31, array([1.75719557e-04, 1.63873294e-04, 8.88469658e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_837.wav', 'Nicht nötig.', 13, array([-1.6189892e-04, -7.7000703e-05, -5.7256933e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_838.wav', 'Wir sind eine Familie.', 22, array([-1.2241138e-04, -1.5992454e-04, -2.3100211e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_842.wav', 'Was schlagen Sie vor?', 21, array([ 3.1590032e-05,  3.5538786e-05,  4.9359427e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_844.wav', 'Probier mal!', 12, array([ 1.4018077e-04,  1.6782204e-04,  2.2902773e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_855.wav', 'Der Schein trügt.', 18, array([ 1.3228325e-04,  4.3436296e-05,  9.8718847e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_861.wav', 'Du hast mich nie geliebt.', 25, array([ 1.02667604e-04,  1.57950155e-04,  1.50052656e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_872.wav', 'Chili ist scharf.', 17, array([-1.1253949e-04, -8.6872591e-05, -1.1648824e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_874.wav', 'Das lässt mich kalt.', 21, array([ 2.1718148e-05,  3.3564411e-05,  5.3308180e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_12_FINAL/12_881.wav', 'Kinder brauchen Helden.', 23, array([-1.8361707e-04, -1.4610391e-04, -1.1846262e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_3.wav', 'Voll der gute Vergleich!', 24, array([-3.94875406e-06, -1.08590735e-04, -1.40180768e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_14.wav', 'Gibt es das überhaupt?', 23, array([-1.0069323e-04, -1.5202703e-04, -1.8164268e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_35.wav', 'Bleib wachsam.', 14, array([-1.5597578e-04, -1.4807828e-04, -3.1590032e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_43.wav', 'Jeder hat das Recht auf Bildung.', 32, array([ 5.72569334e-05,  1.04641986e-04,  1.89540195e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_64.wav', 'Nur nicht politisch werden!', 27, array([-7.8975081e-06,  8.2923834e-05,  1.3425764e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_67.wav', 'Wir sprechen uns später noch mal.', 34, array([ 6.8037030e-03,  6.8649091e-03,  7.0327311e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_69.wav', 'Wem gehört welcher Becher?', 27, array([ 6.0810812e-04,  1.8756582e-04,  8.8846966e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_74.wav', 'Was kann der Arbeiter dafür?', 29, array([ 6.71288217e-05,  7.89750775e-05,  1.02667604e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_94.wav', 'Wir möchten abreisen.', 22, array([-1.2636013e-04, -7.3051953e-05, -7.7000703e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_97.wav', 'Halbe Fahrt voraus!', 19, array([ 5.3308180e-05,  2.7641279e-05, -1.1253949e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_101.wav', 'Gute Wahl!', 10, array([-5.3308180e-05, -4.1461917e-05, -4.3436296e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_111.wav', 'Ich kenne den doch gar nicht!', 29, array([ 4.9359427e-05,  3.5538786e-05,  6.9103196e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_135.wav', 'Die Hände auf den Rücken!', 27, array([-7.7000703e-05, -5.1333802e-05, -7.1077571e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_142.wav', 'Am Deal wird nichts geändert.', 30, array([1.4412952e-04, 1.6979642e-04, 1.7571956e-04, ..., 4.5410670e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_148.wav', 'Das ist eine Wucht.', 19, array([-4.93594271e-05, -1.57950155e-04, -1.08590735e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_152.wav', 'Renitent!', 9, array([2.8233591e-04, 2.6061776e-04, 2.2902773e-04, ..., 1.5795015e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_161.wav', 'Ist noch Kaffee da?', 19, array([-1.46103906e-04, -6.91031964e-05, -1.02667604e-04, ...,\n",
+      "       -7.89750775e-05, -2.17181478e-05,  7.89750811e-06], dtype=float32), 1.6369895833333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_166.wav', 'Da werden Erinnerungen wach.', 28, array([ 2.1718148e-05,  1.9743769e-05, -9.8718854e-05, ...,\n",
+      "        8.4898209e-05,  9.2795723e-05,  1.1846262e-05], dtype=float32), 1.7922291666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_175.wav', 'Suchen Sie die Herausforderung?', 31, array([-1.4215514e-04, -9.4770097e-05, -1.2833450e-04, ...,\n",
+      "       -4.5410670e-05, -8.2923834e-05, -6.9103196e-05], dtype=float32), 1.764)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_213.wav', 'Kommt ihr mit zur Demo?', 23, array([-7.3051953e-05, -3.7513164e-05, -6.3180065e-05, ...,\n",
+      "        6.1205690e-05,  1.2241138e-04,  1.4807828e-04], dtype=float32), 1.7781041666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_214.wav', 'Was sagt er?', 12, array([-2.6456651e-04, -2.2507898e-04, -2.0928397e-04, ...,\n",
+      "        4.3436296e-05,  8.0949460e-05,  1.8164268e-04], dtype=float32), 1.622875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_215.wav', 'Ich will mehr Geld!', 19, array([-8.4898209e-05, -9.4770097e-05, -1.1451387e-04, ...,\n",
+      "       -1.1056512e-04, -8.2923834e-05, -1.1846262e-04], dtype=float32), 1.5664270833333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_219.wav', 'Du bist überstimmt.', 20, array([ 1.04641986e-04,  6.91031964e-05,  2.76412793e-05, ...,\n",
+      "       -1.02667604e-04, -2.58643384e-04, -2.05335207e-04], dtype=float32), 1.52409375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_242.wav', 'Rutsch mir doch den Buckel runter.', 34, array([-5.7256933e-05, -3.9487541e-06,  4.5410670e-05, ...,\n",
+      "        1.6979642e-04,  7.5026328e-05, -1.5795016e-05], dtype=float32), 1.9615625)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_280.wav', 'Und ab dafür!', 14, array([ 7.1077571e-05,  1.1056512e-04,  2.0138646e-04, ...,\n",
+      "       -4.3436296e-05,  2.7641279e-05, -6.9103196e-05], dtype=float32), 1.2030416666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_283.wav', 'Er meint den Doppeldecker.', 26, array([-8.0949460e-05, -7.7000703e-05, -2.9615656e-05, ...,\n",
+      "       -1.2833450e-04, -8.0949460e-05, -1.8164268e-04], dtype=float32), 1.79221875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_284.wav', 'Oder spricht etwas dagegen?', 27, array([ 2.0533521e-04,  1.4215514e-04,  1.4018077e-04, ...,\n",
+      "       -1.3820639e-04, -7.8975077e-05, -1.6584767e-04], dtype=float32), 1.7851666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_288.wav', 'Auf zu neuen Ufern!', 19, array([ 3.5736224e-04,  4.6990174e-04,  6.1798003e-04, ...,\n",
+      "        9.2795723e-05,  2.1718148e-05, -4.9359427e-05], dtype=float32), 1.7216666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_291.wav', 'Kostprobe gefällig?', 20, array([-1.7571956e-04, -2.3889962e-04, -1.9348894e-04, ...,\n",
+      "       -2.5864338e-04, -1.6584767e-04, -2.9615656e-05], dtype=float32), 1.4182604166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_299.wav', 'Der Wein muss noch atmen.', 25, array([-3.5341349e-04, -2.4482276e-04, -2.2705336e-04, ...,\n",
+      "       -6.1205690e-05,  5.9231311e-06,  4.5410670e-05], dtype=float32), 1.9333333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_300.wav', 'Das ist nichts Ernstes.', 23, array([1.5597578e-04, 1.7177081e-04, 6.1205690e-05, ..., 2.7641279e-05,\n",
+      "       3.1590032e-05, 4.9359427e-05], dtype=float32), 1.9121666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_311.wav', 'Nee, lass mal stecken.', 22, array([ 2.3692524e-05,  3.1590032e-05, -4.7385049e-05, ...,\n",
+      "        3.8105476e-04,  4.1264479e-04,  6.8313448e-04], dtype=float32), 1.79221875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_321.wav', 'Ha, das war die Rache!', 22, array([-1.6979642e-04,  3.3564411e-05,  1.1056512e-04, ...,\n",
+      "        1.6387329e-04,  2.7048966e-04,  2.0533521e-04], dtype=float32), 1.7922291666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_326.wav', 'Eigentlich ist es logisch.', 26, array([ 7.3051953e-05,  3.9487541e-06,  2.5666901e-05, ...,\n",
+      "       -1.5795016e-05, -7.1077571e-05,  7.8975081e-06], dtype=float32), 1.7075520833333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_351.wav', 'Der Wein muss atmen können.', 28, array([2.9615656e-05, 4.3436296e-05, 8.0949460e-05, ..., 4.7385049e-05,\n",
+      "       1.7769393e-05, 1.9743770e-06], dtype=float32), 1.8063229166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_354.wav', 'Mieter haben Rechte.', 20, array([-1.5795016e-05, -9.8718847e-06,  3.3564411e-05, ...,\n",
+      "       -2.1520710e-04, -1.5992454e-04, -4.5410670e-05], dtype=float32), 1.7216666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_369.wav', 'Was für eine Erkenntnis!', 25, array([-1.02667604e-04, -8.68725911e-05, -4.73850487e-05, ...,\n",
+      "        3.35644108e-05,  7.70007027e-05,  8.68725911e-05], dtype=float32), 1.9192291666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_371.wav', 'Ich schieÃ\\x9fe mit rechts.', 24, array([ 1.3623202e-04,  7.8975077e-05,  4.3436296e-05, ...,\n",
+      "       -1.1056512e-04, -1.1451387e-04, -7.3051953e-05], dtype=float32), 1.86278125)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_376.wav', 'Ist Scooter nicht eine Band?', 28, array([ 3.5538786e-05,  0.0000000e+00, -5.9231311e-06, ...,\n",
+      "       -6.3180065e-05, -1.3820639e-04, -1.2043700e-04], dtype=float32), 1.9474479166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_387.wav', 'Wir sind Dickhäuter.', 21, array([1.52027031e-04, 1.12539492e-04, 1.02667604e-04, ...,\n",
+      "       1.38206397e-05, 5.92313108e-06, 8.09494595e-05], dtype=float32), 1.5946458333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_389.wav', 'Sei nicht so streng mit ihm!', 28, array([-3.5538786e-05,  1.7769393e-05,  7.1077571e-05, ...,\n",
+      "       -1.1451387e-04, -1.6189892e-04, -2.0928397e-04], dtype=float32), 1.9474479166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_390.wav', 'Na ja, was willst du machen?', 28, array([-1.3820640e-05, -4.1461917e-05, -4.5410670e-05, ...,\n",
+      "       -9.0821341e-05, -1.1846262e-05, -4.3436296e-05], dtype=float32), 1.93334375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_419.wav', 'Die Einschläge kommen näher.', 30, array([ 7.5026328e-05,  5.5282559e-05,  1.5597578e-04, ...,\n",
+      "        3.1590032e-05,  2.1718148e-05, -4.7385049e-05], dtype=float32), 1.9192291666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_420.wav', 'Willst du mit mir gehen?', 24, array([-1.2438576e-04, -1.9546332e-04, -1.6782204e-04, ...,\n",
+      "       -3.7513164e-05, -1.0661636e-04,  7.7000703e-05], dtype=float32), 1.8204479166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_426.wav', 'Hier bitte eine Unterschrift.', 29, array([1.6979642e-04, 1.8361707e-04, 1.7177081e-04, ..., 1.5202703e-04,\n",
+      "       2.1718148e-05, 0.0000000e+00], dtype=float32), 1.891)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_430.wav', 'Zum Glück nicht.', 17, array([-6.7128822e-05, -9.8718854e-05, -3.1590032e-05, ...,\n",
+      "       -7.3051953e-05, -9.4770097e-05, -1.1056512e-04], dtype=float32), 1.1571770833333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_437.wav', 'Einfach nur top!', 16, array([-3.9684979e-04, -4.2646544e-04, -4.0277292e-04, ...,\n",
+      "       -2.4087400e-04, -3.7513164e-05, -1.4412952e-04], dtype=float32), 1.35475)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_439.wav', 'Mach dir nichts daraus.', 23, array([ 4.2843982e-04,  5.0938927e-04,  4.6595297e-04, ...,\n",
+      "        7.8975081e-06, -3.1590032e-05,  2.5666901e-05], dtype=float32), 1.5523229166666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_443.wav', 'Lauf doch nicht immer durchs Bild!', 34, array([-3.5538786e-04, -1.7769393e-04, -1.1451387e-04, ...,\n",
+      "       -3.9487539e-05, -4.3436296e-05, -3.9487539e-05], dtype=float32), 1.9192291666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_464.wav', 'Hände hoch!', 12, array([-2.0138646e-04, -1.3425764e-04, -8.0949460e-05, ...,\n",
+      "        2.0138646e-04,  1.8756582e-04,  2.6061776e-04], dtype=float32), 1.04428125)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_475.wav', 'Was weiÃ\\x9f ich denn?', 19, array([-2.7641279e-05, -1.9743770e-06,  8.2923834e-05, ...,\n",
+      "        7.3051953e-05,  9.8718854e-05, -4.9359427e-05], dtype=float32), 1.52409375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_487.wav', 'Ich will noch nicht ins Bett!', 29, array([ 5.7256933e-05, -7.8975081e-06,  1.7769393e-05, ...,\n",
+      "       -2.9615656e-05, -1.1846262e-05,  2.5666901e-05], dtype=float32), 1.9615625)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_499.wav', 'Darüber kann man streiten.', 27, array([ 0.00011846,  0.00020534,  0.00027839, ..., -0.00031195,\n",
+      "       -0.00021521, -0.00017769], dtype=float32), 1.8486666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_500.wav', 'Dazu braucht man Ruhe.', 22, array([-1.1056512e-04, -1.4610391e-04, -1.3425764e-04, ...,\n",
+      "        6.9103196e-05,  1.6189892e-04,  2.2507898e-04], dtype=float32), 1.5523229166666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_524.wav', 'Das Zeug ist wirklich gut.', 26, array([-3.9487541e-06,  3.5538786e-05, -9.8718847e-06, ...,\n",
+      "        1.1846262e-05,  1.9743769e-05,  9.8718847e-06], dtype=float32), 1.891)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_539.wav', 'Betäubungsgewehr geladen!', 26, array([ 3.5538786e-05,  2.3692524e-05,  0.0000000e+00, ...,\n",
+      "       -9.2795723e-05, -1.9151457e-04, -1.8756582e-04], dtype=float32), 1.8768854166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_547.wav', 'Was wird wie geschrieben?', 25, array([-0.00012636, -0.00020336, -0.0002231 , ...,  0.00021521,\n",
+      "        0.00020336,  0.0001619 ], dtype=float32), 1.8768854166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_569.wav', 'Nicht schon wieder, bitte.', 26, array([ 0.00035736,  0.00043436,  0.00037316, ..., -0.00013821,\n",
+      "       -0.00013031, -0.0001619 ], dtype=float32), 1.5523229166666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_582.wav', 'Bist du blind?', 14, array([ 1.9743769e-05, -1.5795016e-05, -5.7256933e-05, ...,\n",
+      "        0.0000000e+00,  8.6872591e-05,  4.5410670e-05], dtype=float32), 1.2841875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_592.wav', 'Blinzeln zählt nicht.', 22, array([ 5.9231311e-06, -4.5410670e-05, -9.8718854e-05, ...,\n",
+      "        1.6387329e-04,  1.3820639e-04,  7.1077571e-05], dtype=float32), 1.8768958333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_595.wav', 'Ja, warum denn bitte schön nicht?', 34, array([-0.00036329, -0.00033959, -0.00036131, ..., -0.00016585,\n",
+      "       -0.00021521, -0.0001619 ], dtype=float32), 1.93334375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_597.wav', 'Mir wäre das peinlich.', 23, array([ 5.9231312e-05,  1.2241138e-04,  7.5026328e-05, ...,\n",
+      "       -1.5795016e-05, -8.2923834e-05, -6.7128822e-05], dtype=float32), 1.8345520833333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_637.wav', 'So kann es gehen.', 17, array([-7.8975081e-06,  3.9487541e-06,  4.1461917e-05, ...,\n",
+      "        5.1333802e-05,  1.3030888e-04,  3.9487539e-05], dtype=float32), 1.3688645833333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_642.wav', 'Es bleibt spannend.', 19, array([1.8559145e-04, 1.8559145e-04, 1.5597578e-04, ..., 7.3051953e-05,\n",
+      "       5.7256933e-05, 1.1451387e-04], dtype=float32), 1.559375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_647.wav', 'Marek will noch mal.', 20, array([-2.44822761e-04, -1.04641986e-04, -8.09494595e-05, ...,\n",
+      "        1.48078281e-04,  1.81642681e-04,  2.50745885e-04], dtype=float32), 1.79221875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_661.wav', 'Ruhig Brauner!', 14, array([-1.02667604e-04, -4.73850487e-05,  8.09494595e-05, ...,\n",
+      "       -9.87188469e-06, -8.88469658e-05, -1.12539492e-04], dtype=float32), 1.2771354166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_682.wav', 'Meine Rede!', 11, array([5.9428747e-04, 5.0544052e-04, 2.0730958e-04, ..., 7.5026328e-05,\n",
+      "       6.5154440e-05, 6.5154440e-05], dtype=float32), 1.0725104166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_685.wav', 'Was versprichst du dir davon?', 29, array([-1.1846262e-05, -9.8718847e-06,  4.3436296e-05, ...,\n",
+      "       -2.3692524e-05,  1.9743770e-06,  2.7641279e-05], dtype=float32), 1.7710520833333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_693.wav', 'Ich nehme euch alle.', 20, array([-3.1590032e-05, -5.9231311e-06, -7.5026328e-05, ...,\n",
+      "       -8.8846966e-05, -7.3051953e-05, -5.1333802e-05], dtype=float32), 1.7851666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_711.wav', 'Warum nämlich?', 15, array([ 5.9231312e-05,  5.9231312e-05,  3.1590032e-05, ...,\n",
+      "        1.1846262e-05, -5.9231311e-06, -7.5026328e-05], dtype=float32), 1.3688645833333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_712.wav', 'Das hätte ich beinahe vergessen.', 33, array([-2.1125835e-04, -2.4482276e-04, -1.4610391e-04, ...,\n",
+      "        9.0821341e-05,  1.7966831e-04,  1.0661636e-04], dtype=float32), 1.9192291666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_726.wav', 'Möchtest du auch einen Muffin?', 31, array([-3.9487539e-05, -2.7641279e-05,  6.3180065e-05, ...,\n",
+      "        1.7769393e-05,  6.7128822e-05,  7.1077571e-05], dtype=float32), 1.9545104166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_727.wav', 'Es hat nichts mit dir zu tun.', 29, array([-1.6584767e-04, -1.9348894e-04, -2.7641279e-04, ...,\n",
+      "        6.5154440e-05,  4.3436296e-05,  1.2438576e-04], dtype=float32), 1.7569479166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_732.wav', 'Vielleicht war ich etwas vorschnell.', 36, array([1.7177081e-04, 1.6584767e-04, 8.6872591e-05, ..., 1.9546332e-04,\n",
+      "       1.8954019e-04, 1.5597578e-04], dtype=float32), 1.9192291666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_735.wav', 'Hatschi!', 8, array([ 1.2043700e-04, -1.7769393e-05, -1.9743770e-06, ...,\n",
+      "       -1.1846262e-05, -4.5410670e-05, -7.7000703e-05], dtype=float32), 0.8114375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_739.wav', 'Ich bleibe dabei.', 17, array([-2.0533521e-04, -1.2438576e-04, -5.5282559e-05, ...,\n",
+      "        4.5410670e-05, -1.3820640e-05, -7.7000703e-05], dtype=float32), 1.2418541666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_762.wav', 'Nicht zu fassen!', 16, array([1.7414006e-03, 1.4353720e-03, 9.6547039e-04, ..., 6.3180065e-05,\n",
+      "       1.8164268e-04, 8.0949460e-05], dtype=float32), 1.1712916666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_773.wav', 'Gute Besserung!', 15, array([-5.1333802e-05,  0.0000000e+00,  2.1718148e-05, ...,\n",
+      "       -1.2636013e-04, -1.9546332e-04, -1.4215514e-04], dtype=float32), 1.2771354166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_775.wav', 'Ja, so ist es wohl.', 19, array([-1.61898919e-04,  1.97437694e-05,  1.02667604e-04, ...,\n",
+      "       -6.51544397e-05, -1.26360130e-04, -6.71288217e-05], dtype=float32), 1.44646875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_778.wav', 'Mich selbst hat das überrascht.', 32, array([7.7000703e-05, 1.1846262e-04, 1.2241138e-04, ..., 1.3820639e-04,\n",
+      "       9.8718847e-06, 1.3820640e-05], dtype=float32), 1.8275)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_782.wav', 'Wer kennt das nicht?', 20, array([-2.7641279e-05,  7.8975081e-06, -3.7513164e-05, ...,\n",
+      "       -2.3297648e-04, -2.2902773e-04, -2.4087400e-04], dtype=float32), 1.72871875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_787.wav', 'Ich liebe diese Musik!', 22, array([-1.8361707e-04, -6.9103196e-05, -9.0821341e-05, ...,\n",
+      "        5.6862057e-04,  6.2587752e-04,  5.3110742e-04], dtype=float32), 1.8580729166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_800.wav', 'Na endlich!', 11, array([-1.1846262e-04, -1.5202703e-04, -8.4898209e-05, ...,\n",
+      "        9.0821341e-05, -9.0821341e-05, -7.8975081e-06], dtype=float32), 0.91021875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_805.wav', 'Juliane gruselt sich.', 21, array([1.3425764e-04, 7.1077571e-05, 6.5154440e-05, ..., 9.8718854e-05,\n",
+      "       8.6872591e-05, 5.1333802e-05], dtype=float32), 1.86278125)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_808.wav', 'Der andere nimmt.', 17, array([-8.6872591e-05, -1.1451387e-04, -8.2923834e-05, ...,\n",
+      "        2.5666901e-05, -7.3051953e-05, -7.5026328e-05], dtype=float32), 1.52409375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_814.wav', 'Wieso ich?', 10, array([-1.14513867e-04, -1.02667604e-04, -1.77693932e-04, ...,\n",
+      "       -1.18462622e-05,  0.00000000e+00,  1.38206397e-05], dtype=float32), 0.9031666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_816.wav', 'Die Haare müssen ab.', 21, array([ 1.9546332e-04,  1.2636013e-04,  2.1125835e-04, ...,\n",
+      "        9.8718847e-06, -4.1461917e-05, -5.5282559e-05], dtype=float32), 1.2065729166666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_13_FINAL/13_844.wav', 'Die ganze Woche steht das schon an.', 35, array([ 1.0602404e-03,  1.1017023e-03,  9.0031594e-04, ...,\n",
+      "       -3.3564411e-05, -3.5538786e-05,  0.0000000e+00], dtype=float32), 1.8839479166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_7.wav', 'Meinen Respekt hast du.', 23, array([-8.1613541e-07,  3.6258320e-05,  5.8615900e-05, ...,\n",
+      "       -3.0361010e-05,  4.6051988e-05,  6.1613529e-05], dtype=float32), 1.568)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_12.wav', 'Mein SchweiÃ\\x9f stinkt nicht.', 27, array([1.2758464e-03, 1.4472028e-03, 1.4819785e-03, ..., 1.1448720e-05,\n",
+      "       2.5002395e-05, 5.3266147e-05], dtype=float32), 1.872)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_24.wav', 'So sieht es jedenfalls aus.', 27, array([ 3.5462443e-05, -3.6511621e-05, -2.4387444e-05, ...,\n",
+      "        7.4399744e-05,  7.2159133e-07,  2.3660252e-05], dtype=float32), 1.808)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_31.wav', 'Es brennt lichterloh.', 21, array([-7.8527468e-05, -1.9054073e-04, -1.8275550e-04, ...,\n",
+      "       -1.4771417e-05,  2.4868292e-05, -1.4910699e-05], dtype=float32), 1.8986666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_36.wav', 'Hat jemand Deo dabei?', 21, array([5.0298637e-05, 4.8803475e-05, 5.4532258e-05, ..., 3.4226623e-06,\n",
+      "       9.2322180e-06, 3.0618612e-05], dtype=float32), 1.7386666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_44.wav', 'Der Hund will raus.', 19, array([-8.2374172e-05, -8.4805586e-05, -9.4096496e-05, ...,\n",
+      "        2.0108973e-05,  3.4747383e-05, -3.9627314e-05], dtype=float32), 1.5413333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_46.wav', 'Nur Fliegen ist schöner.', 25, array([-2.5430196e-05, -6.4560918e-05, -6.8181558e-05, ...,\n",
+      "        6.0105547e-05,  9.7991426e-05,  2.9888753e-05], dtype=float32), 1.6693333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_57.wav', 'Endlich wieder Nachschub!', 25, array([-3.0662410e-05, -3.7799236e-05, -1.0512020e-04, ...,\n",
+      "       -1.2799338e-04, -3.7069469e-05,  3.4687200e-05], dtype=float32), 1.568)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_63.wav', \"Jetzt langt's dann aber.\", 24, array([ 1.3113129e-06, -5.7142366e-05,  3.9664551e-06, ...,\n",
+      "        4.8476216e-04,  4.0935431e-04,  5.0957059e-04], dtype=float32), 1.8453333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_76.wav', 'Ist hier noch ein Platz frei?', 29, array([ 4.6084756e-06,  2.1333383e-06,  1.0840034e-05, ...,\n",
+      "        4.7717163e-05, -4.3301993e-06,  5.9024904e-07], dtype=float32), 1.7653333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_91.wav', 'Möchten Sie durch?', 19, array([5.3242915e-05, 1.1775635e-04, 9.1564674e-05, ..., 6.9772730e-05,\n",
+      "       3.2825061e-05, 5.5504606e-05], dtype=float32), 1.1786666666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_94.wav', 'Du hast sie angemalt.', 21, array([-8.2009647e-06, -7.8560508e-05, -1.1781590e-04, ...,\n",
+      "        5.8809797e-05,  3.5827401e-05, -3.8682600e-05], dtype=float32), 1.5946666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_97.wav', 'Anfassen heiÃ\\x9ft kaufen.', 23, array([ 6.7132327e-04,  6.4567651e-04,  4.5344225e-04, ...,\n",
+      "       -2.1742040e-05, -1.2411790e-04, -3.8199389e-05], dtype=float32), 1.472)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_104.wav', 'Warum nicht lieber hier?', 24, array([-1.0701143e-05, -1.5738879e-06,  6.8153045e-06, ...,\n",
+      "       -6.3156702e-05, -1.6941859e-04, -6.0139148e-05], dtype=float32), 1.4986666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_112.wav', 'Das war ein Abenteuer.', 22, array([ 2.6408197e-05, -6.0915321e-05, -9.1295704e-05, ...,\n",
+      "       -5.6715970e-05, -3.1489210e-05,  1.5612791e-06], dtype=float32), 1.9466666666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_127.wav', 'Das wäre fatal.', 16, array([ 4.4660061e-05, -6.5924425e-05, -5.6830704e-05, ...,\n",
+      "       -5.5352357e-06,  3.0260082e-05,  9.7271128e-05], dtype=float32), 1.4666666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_141.wav', 'Nicht doch!', 11, array([-1.4546166e-04, -1.4626759e-04, -9.7611184e-05, ...,\n",
+      "        9.3360104e-05,  3.5025540e-05, -1.6926177e-06], dtype=float32), 0.928)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_146.wav', 'Heiliger Strohsack!', 19, array([-3.7175673e-04, -2.1206291e-04, -8.9090288e-05, ...,\n",
+      "        1.0547445e-04,  1.0614831e-04,  5.8346381e-05], dtype=float32), 1.376)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_153.wav', 'Gehen wir in die Eisdiele?', 26, array([-3.72752729e-05, -6.43968451e-05, -1.19852075e-05, ...,\n",
+      "        6.90084271e-05, -1.81738214e-05, -2.24471933e-05], dtype=float32), 1.4826666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_158.wav', 'Das ist halt so.', 16, array([ 2.1661433e-05, -9.2656213e-05, -2.0038491e-05, ...,\n",
+      "        3.4980503e-06,  8.1309692e-05, -1.6156602e-05], dtype=float32), 1.2853333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_174.wav', 'Ich habe dich noch nie gesehen.', 31, array([ 1.68298247e-05,  2.35711445e-06, -1.13152724e-04, ...,\n",
+      "       -5.31522637e-05,  5.38938584e-05,  1.89053408e-05], dtype=float32), 1.8773333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_179.wav', 'Das muss hart für dich sein.', 29, array([-9.2038817e-06, -9.7612574e-06, -6.3460277e-05, ...,\n",
+      "       -5.0950723e-05,  2.0168585e-05, -1.5738755e-05], dtype=float32), 1.5893333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_183.wav', \"Packen wir's!\", 13, array([-2.2114466e-05,  6.0876686e-05, -8.3392551e-05, ...,\n",
+      "        3.5826326e-06, -1.4385004e-05, -5.6348257e-05], dtype=float32), 0.9546666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_194.wav', 'Wir werden siegen!', 18, array([ 1.6911860e-04,  7.4598174e-05,  1.0261347e-04, ...,\n",
+      "        6.5378241e-05,  3.2076507e-06, -6.6169787e-06], dtype=float32), 1.3333333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_197.wav', 'Darf ich mal bei dir abbeiÃ\\x9fen?', 31, array([-1.0340806e-05,  7.1646286e-06,  3.3313339e-05, ...,\n",
+      "       -7.5323747e-05, -2.6892374e-07, -3.3816039e-05], dtype=float32), 1.76)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_199.wav', 'Das ging aber fix!', 18, array([-9.3143040e-05, -4.3784836e-05, -1.1206182e-04, ...,\n",
+      "        8.7669920e-05,  1.0557293e-05,  4.2041685e-07], dtype=float32), 1.328)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_211.wav', 'Ich habe nachgedacht.', 21, array([ 5.0232731e-05,  1.2072114e-04,  1.8210443e-04, ...,\n",
+      "       -6.5402834e-05, -5.1763345e-05, -6.0046054e-06], dtype=float32), 1.5093333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_229.wav', 'Wir lassen uns nicht erpressen.', 31, array([1.37981799e-04, 1.52958339e-04, 1.10953624e-04, ...,\n",
+      "       6.50644288e-05, 8.02592767e-05, 1.01248879e-04], dtype=float32), 1.7493333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_236.wav', 'Sag du es mir.', 14, array([ 7.4462928e-06, -2.0409609e-05, -3.6314952e-05, ...,\n",
+      "       -2.1986765e-05, -8.3042978e-05,  8.2145634e-06], dtype=float32), 1.216)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_240.wav', 'Ich vermisse ihn seit gestern.', 30, array([ 2.9365596e-04,  3.4678026e-04,  3.5397714e-04, ...,\n",
+      "       -1.5735781e-05, -2.9272232e-05,  4.2558597e-05], dtype=float32), 1.9893333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_257.wav', 'So kannte ich sie gar nicht.', 28, array([ 4.4733344e-05,  7.7341829e-05,  1.1480036e-04, ...,\n",
+      "       -1.8965245e-04, -1.4387793e-04, -1.2223862e-04], dtype=float32), 1.8133333333333332)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_258.wav', 'Dem Kind geht es gut.', 21, array([ 2.3389544e-05, -1.0488247e-05,  1.0429079e-05, ...,\n",
+      "       -8.0030593e-05, -9.8967379e-05, -4.5314195e-05], dtype=float32), 1.3066666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_260.wav', 'Lasst es krachen!', 17, array([-2.1083563e-04, -8.3892046e-05, -3.2037347e-05, ...,\n",
+      "       -6.8306355e-05, -1.3884228e-04, -6.5104126e-05], dtype=float32), 1.2)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_266.wav', 'Wie sehen Sie überhaupt aus?', 29, array([-1.0680479e-05, -1.9320854e-05, -7.0852952e-06, ...,\n",
+      "       -1.0408241e-05,  3.3198389e-06,  2.1512881e-06], dtype=float32), 1.8826666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_281.wav', 'Damit könnte es klappen.', 25, array([-2.3432081e-05, -2.4900844e-05, -1.3450766e-04, ...,\n",
+      "        2.1617279e-05,  3.1534404e-05, -2.2315735e-05], dtype=float32), 1.488)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_307.wav', 'Tut das Husten weh?', 19, array([ 9.1145994e-06,  1.5820089e-05,  5.0116945e-05, ...,\n",
+      "        1.9206882e-05, -2.6969181e-05, -2.7526901e-05], dtype=float32), 1.5626666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_310.wav', 'Und jetzt kräftig kurbeln!', 27, array([-8.4867512e-05, -1.3528325e-05,  6.7344299e-05, ...,\n",
+      "       -5.5355646e-05,  3.2757125e-05, -1.3706725e-05], dtype=float32), 1.968)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_311.wav', 'Und was bekommt man geboten?', 28, array([-9.42486338e-07, -6.20736901e-05, -1.13615904e-04, ...,\n",
+      "        1.05647247e-04,  4.75407724e-05,  7.68981190e-05], dtype=float32), 1.9626666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_313.wav', 'Nimm doch mal den Hut ab!', 25, array([-1.4411381e-06,  1.8580539e-04,  1.8933907e-04, ...,\n",
+      "       -1.0257358e-04, -9.1900030e-05, -2.2193763e-04], dtype=float32), 1.5733333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_322.wav', 'Der ist sauber.', 15, array([1.3459381e-04, 1.1068168e-04, 1.4088971e-04, ..., 1.4206764e-04,\n",
+      "       1.0958829e-05, 9.0381429e-05], dtype=float32), 1.344)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_333.wav', 'Danke für nichts!', 18, array([-2.6258719e-04, -2.9124424e-04, -4.0630574e-04, ...,\n",
+      "        9.1923815e-05, -9.6123731e-06,  3.9555922e-05], dtype=float32), 1.408)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_351.wav', 'Hier ist sie.', 13, array([-3.23740860e-05, -1.03745086e-04, -6.84802653e-05, ...,\n",
+      "        6.36538107e-06,  6.47425259e-05, -2.68384956e-05], dtype=float32), 1.2693333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_354.wav', 'Ist sie international bekannt?', 30, array([ 1.5060005e-05,  5.7448578e-05,  1.3811006e-04, ...,\n",
+      "        6.0413648e-05, -4.7934391e-05, -1.9190535e-05], dtype=float32), 1.9626666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_363.wav', 'Ich meine ja nur.', 17, array([ 5.6321147e-05,  9.9655284e-05, -8.9936962e-05, ...,\n",
+      "        1.1549123e-05,  3.7268135e-05,  7.3645397e-06], dtype=float32), 1.1253333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_390.wav', 'Gib mal die Seriennummer durch.', 31, array([ 7.2849958e-05,  9.1718932e-05,  5.6555116e-05, ...,\n",
+      "       -2.9702240e-05,  3.8465154e-05,  2.2035034e-05], dtype=float32), 1.9466666666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_399.wav', 'Steht das Wasser auf dem Herd?', 30, array([6.5801214e-05, 1.3084775e-04, 8.1372353e-05, ..., 6.8494905e-05,\n",
+      "       2.1234882e-06, 2.7409065e-05], dtype=float32), 1.84)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_401.wav', 'Oh ja!', 6, array([ 2.2632883e-05, -2.7574149e-05,  2.7717488e-05, ...,\n",
+      "        2.9032512e-07,  1.7548422e-05, -1.3465881e-05], dtype=float32), 0.7146666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_409.wav', 'Ja oder nein?', 13, array([ 3.4988134e-05, -6.8858870e-05, -8.5955844e-06, ...,\n",
+      "       -4.4800227e-06,  1.7184280e-05,  3.7901282e-05], dtype=float32), 1.4346666666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_411.wav', 'Ist doch Jacke wie Hose.', 24, array([ 1.1507938e-04,  5.0565839e-05, -2.7287895e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_413.wav', 'Ich habe es nie gelernt.', 24, array([ 2.58978853e-05,  6.50478396e-05, -1.03702390e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_429.wav', 'Nicht schon wieder eine Razzia!', 31, array([-5.1378167e-05, -2.5352152e-05, -3.2764001e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_431.wav', 'Niemand will es gewesen sein.', 29, array([6.13634029e-06, 1.00043821e-04, 1.26646410e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_443.wav', 'Ihr seid doch bloÃ\\x9f neidisch.', 29, array([ 4.71922749e-06, -1.42986255e-05,  4.10590292e-05, ...,\n",
+      "       -1.13690789e-04, -4.82848300e-05,  3.64537264e-05], dtype=float32), 1.7493333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_453.wav', 'Lesen lohnt sich.', 17, array([-1.1143904e-04, -9.7466742e-05, -1.4505965e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_458.wav', 'Oder er wurde dabei gestört.', 29, array([-1.8823694e-05, -3.1060394e-05, -9.3846960e-05, ...,\n",
+      "       -1.2105788e-05, -3.4755056e-05,  3.5802004e-05], dtype=float32), 1.84)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_459.wav', 'Die Seele baumeln lassen.', 25, array([-4.6934008e-05, -1.4115409e-04, -1.9004452e-04, ...,\n",
+      "       -4.7015623e-05, -2.2894224e-07, -4.3300730e-05], dtype=float32), 1.6746666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_468.wav', 'Der Nächste, bitte!', 20, array([ 8.1093880e-05,  2.9958397e-05, -3.9947310e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_469.wav', 'Wird schon schiefgehen.', 23, array([-1.8012641e-05, -6.1548446e-05, -1.2534855e-04, ...,\n",
+      "       -2.9845067e-05,  3.1653948e-05,  1.2874776e-04], dtype=float32), 1.552)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_476.wav', 'Keine falsche Bewegung!', 23, array([-1.3065083e-04, -1.9577878e-04, -9.6719399e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_484.wav', 'Danach geht es ins Bett.', 24, array([1.4125947e-04, 1.4533960e-04, 1.3352933e-04, ..., 4.6569412e-06,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_495.wav', 'Vorwärts immer, rückwarts nimmer!', 35, array([ 9.8868964e-05,  1.4638813e-04,  8.2029030e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_508.wav', 'Ein Spanngurt ist gerissen.', 27, array([-1.3210842e-05,  5.2183852e-05,  1.1509426e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_511.wav', 'Das musst du gerade sagen!', 26, array([ 8.16162283e-05,  1.48853534e-04,  1.20252385e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_517.wav', 'Lösen Sie das Captcha!', 23, array([-3.2288870e-05,  5.6598521e-05,  4.2188087e-05, ...,\n",
+      "        7.7064447e-05, -4.7475376e-05,  4.4163811e-05], dtype=float32), 1.6746666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_520.wav', 'Ihr werdet schon sehen.', 23, array([-6.5363100e-05,  4.7253379e-05,  5.9942446e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_521.wav', 'Ich erkläre es dir.', 20, array([ 5.3491673e-05, -1.2072490e-05,  3.4197161e-05, ...,\n",
+      "       -3.4515979e-05, -5.6132449e-05,  1.3709931e-04], dtype=float32), 1.5093333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_525.wav', 'Hau rein!', 9, array([ 2.57931824e-04,  2.11816674e-04,  1.78339556e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_526.wav', 'Tief durchatmen!', 16, array([-2.6787920e-05, -3.2204316e-05, -5.5490927e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_538.wav', 'Und was bringt das?', 19, array([-5.9224880e-05, -4.4477289e-05,  3.8521583e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_541.wav', 'Karnickelfangschlag?', 20, array([3.9227842e-05, 3.2782922e-05, 4.6346566e-05, ..., 1.3389443e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_562.wav', 'Ist ja mega!', 12, array([-1.1508126e-04, -1.5385580e-04, -1.8046032e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_570.wav', 'Jasmin, du bist dran.', 21, array([-6.0017886e-05,  3.1120195e-05,  1.0854354e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_579.wav', 'Läuft es separat ab?', 21, array([ 2.2939121e-05,  2.0304271e-05,  4.7305216e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_594.wav', 'Ich zitiere!', 12, array([ 7.3269119e-05,  4.1316580e-06, -7.5483302e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_597.wav', 'Die Karten sind ja markiert!', 28, array([-7.7787427e-06,  1.3373171e-05,  1.1130486e-04, ...,\n",
+      "       -3.4429740e-05, -9.2525712e-05, -3.0399795e-05], dtype=float32), 1.8613333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_600.wav', 'Weniger ist manchmal mehr.', 26, array([-3.2105188e-05, -1.2411436e-04, -1.7373836e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_610.wav', 'Zur Hölle mit ihm!', 19, array([ 4.1287938e-05, -1.5668693e-05, -4.7829257e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_622.wav', 'Sonst kommt die Polizei.', 24, array([ 1.33967542e-05, -2.86651575e-05,  1.20430150e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_623.wav', 'Papa fährt immer schneller.', 28, array([-4.1551000e-05,  1.8333099e-05, -4.5995697e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_627.wav', 'Das Problem kenne ich.', 22, array([-1.6575548e-06, -6.4681786e-05, -2.4183499e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_646.wav', 'Gerade jetzt wird es spannend.', 30, array([-7.0382644e-05, -2.6976499e-05, -8.4537001e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_650.wav', 'Pass mal auf!', 13, array([ 8.8038476e-05,  6.2287538e-05,  8.6767104e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_653.wav', 'Führe mich nicht in Versuchung!', 32, array([ 1.5389375e-04,  8.4856605e-05,  1.1764471e-04, ...,\n",
+      "       -4.1702488e-06,  4.8200640e-05,  3.7042355e-05], dtype=float32), 1.8986666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_658.wav', 'Dabei soll es bleiben.', 22, array([-6.8817273e-05, -1.4116750e-04, -2.5068663e-04, ...,\n",
+      "        3.3109423e-05, -1.2034771e-05,  5.3297503e-05], dtype=float32), 1.3653333333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_677.wav', 'Ich denke nicht daran.', 22, array([ 2.7965652e-06, -8.1217448e-05, -1.5171595e-04, ...,\n",
+      "       -6.0021226e-05,  5.8105360e-07, -2.3721210e-05], dtype=float32), 1.472)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_702.wav', 'Sieh zu, dass du Land gewinnst!', 31, array([-3.9686485e-05, -4.1371659e-05, -5.1444043e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_705.wav', 'Was sagt uns das?', 17, array([-1.11950721e-04, -1.12432775e-04, -1.54395209e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_715.wav', 'Von nichts komm nichts.', 23, array([ 5.0694278e-05, -1.0824220e-04, -7.8278521e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_718.wav', 'Warum auch?', 11, array([ 2.5824769e-05,  7.0119269e-05,  3.9937982e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_721.wav', 'Wo wohne ich noch mal?', 22, array([ 1.1702570e-04,  1.8368529e-04,  1.5237987e-04, ...,\n",
+      "       -3.3846823e-05, -4.2944125e-06,  2.2590933e-05], dtype=float32), 1.6)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_725.wav', 'Zum Wohl!', 9, array([-2.1576473e-06,  2.8079157e-05, -2.9355248e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_735.wav', 'Wie geht es dir?', 16, array([ 3.0780422e-05, -4.9582297e-05, -8.5829226e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_746.wav', 'Einmal drücken reicht.', 23, array([-4.4286557e-05, -5.6155724e-05, -5.2055671e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_761.wav', 'Ersatz muss her.', 16, array([ 8.3686442e-05,  9.1279635e-06, -8.3661522e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_769.wav', 'Kennen Sie diesen Eisbären?', 28, array([ 1.8226114e-04,  1.1602399e-04,  8.7942906e-05, ...,\n",
+      "       -3.1415253e-05,  6.8828485e-05,  2.8598015e-05], dtype=float32), 1.7173333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_774.wav', 'Du tüdelst wohl!', 17, array([4.2244592e-05, 4.7479767e-05, 4.4327684e-05, ..., 2.9398587e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_776.wav', 'Einen Versuch ist es wert.', 26, array([-2.0919964e-05, -8.0129103e-05, -7.8644814e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_779.wav', 'Kruzifix noch mal!', 18, array([ 5.9276794e-05,  7.1346542e-05,  1.3115312e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_781.wav', 'Sind die echt?', 14, array([-3.2039690e-05, -4.8189206e-05, -9.0187306e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_789.wav', 'Wie war euer Jahrgangstreffen?', 30, array([ 9.86098894e-05,  1.05807514e-04,  1.31781504e-04, ...,\n",
+      "       -6.47349443e-05,  5.55652514e-06,  6.68639914e-05], dtype=float32), 1.9946666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_796.wav', 'Langt das?', 10, array([-2.58835917e-05, -1.11602596e-04, -2.00994928e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_851.wav', 'Nein, das gehört so.', 21, array([4.30460314e-05, 1.00948644e-04, 1.14135793e-04, ...,\n",
+      "       2.88395531e-04, 1.62498865e-04, 8.75307087e-05], dtype=float32), 1.7493333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_852.wav', 'Stellen Sie Blickkontakt her.', 29, array([-2.3877754e-05, -3.1883523e-05, -1.3378897e-04, ...,\n",
+      "       -3.8810729e-05,  4.3067663e-05,  3.8920269e-05], dtype=float32), 1.9946666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_858.wav', 'Also echt jetzt!', 16, array([ 1.62354499e-05,  4.22473058e-05, -1.46273105e-05, ...,\n",
+      "       -2.93930316e-05,  5.34094252e-05,  7.98595574e-05], dtype=float32), 1.216)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_7.wav', 'Ich glaube nicht.', 17, array([-1.0143876e-05, -3.8619244e-05,  8.2748767e-05, ...,\n",
+      "       -9.9806406e-05, -4.3946784e-05,  6.9558562e-05], dtype=float32), 1.1946666666666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_18.wav', 'Hier ist es sicherer.', 21, array([ 4.6870970e-05,  9.9823235e-05, -4.0877108e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_49.wav', 'Ja ja, als ob!', 14, array([ 5.3198488e-05,  1.8346685e-04, -2.1753046e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_59.wav', 'Geh, such deine Schwester!', 26, array([ 9.13840049e-05,  1.68439132e-04,  3.04173911e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_94.wav', 'Gib mir meinen Becher wieder!', 29, array([-2.1092707e-04, -2.3195105e-04, -2.0152969e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_100.wav', 'Das führt doch zu nichts.', 26, array([-1.0273771e-04, -8.6229462e-05, -1.2574486e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_105.wav', 'Wo denn?', 8, array([-4.0845240e-05,  1.0149255e-04,  5.9910049e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_106.wav', 'Du sitzt hinten.', 16, array([ 1.1350374e-04,  1.3197908e-04,  5.9344729e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_112.wav', 'Das kann ich nicht.', 19, array([-9.4199102e-05, -3.3980414e-05,  9.0330948e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_139.wav', 'Das hat sie gelernt.', 20, array([ 1.5456244e-04,  3.1872053e-04,  3.7880472e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_153.wav', 'Nicht alle Teenager sind so.', 28, array([7.9220721e-05, 5.8759109e-05, 1.1493213e-04, ..., 6.8786328e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_156.wav', 'Frische Seeluft macht gesund.', 29, array([ 1.8124521e-04,  1.7306159e-04,  5.9669415e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_164.wav', 'Gönn dir!', 10, array([ 5.2993961e-05,  2.8179937e-05,  7.8242076e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_176.wav', 'Sag ich doch!', 13, array([ 4.2398951e-05,  5.6847359e-05,  7.0788061e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_177.wav', 'Das darf doch nicht wahr sein.', 30, array([-5.1426803e-05, -5.0517308e-05,  4.6803252e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_199.wav', 'Jetzt sind wir quitt.', 21, array([-2.4918138e-05,  8.0159109e-05, -7.1328832e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_207.wav', 'Eben ging das noch.', 19, array([-5.0324921e-05,  1.3549793e-04, -3.3347860e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_208.wav', 'Bug oder Feature?', 17, array([-3.7243055e-06,  6.9413843e-05,  7.5392752e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_274.wav', 'Wir brauchen mehr davon!', 24, array([-2.0753406e-04, -1.9484414e-05, -2.8117347e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_280.wav', 'Lass uns raus gehen.', 20, array([ 1.03469618e-04,  1.97744346e-04, -7.93442814e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_286.wav', 'SchluÃ\\x9f mit lustig.', 19, array([ 2.99623178e-04,  2.43378381e-04,  1.65333462e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_302.wav', 'Woher nehmt ihr eure Bildung?', 29, array([1.7700881e-04, 2.1893253e-04, 1.3036304e-04, ..., 1.3868474e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_308.wav', 'Du fährst, ich schieÃ\\x9fe!', 25, array([1.5563566e-04, 1.4856170e-04, 2.2446582e-04, ..., 6.8505600e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_328.wav', 'Wirkt die Betäubung noch?', 26, array([-8.7537330e-05, -3.0825776e-04, -2.8424736e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_385.wav', 'Es kann nur einen geben!', 24, array([-1.8947560e-04, -2.3450297e-05, -1.2145152e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_400.wav', 'Wer weiÃ\\x9f es?', 13, array([ 8.2401210e-05,  1.2261249e-05,  1.3193028e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_406.wav', 'Tja, das ist Pech.', 18, array([2.4313416e-04, 4.7331341e-05, 1.6022228e-04, ..., 3.0806483e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_412.wav', 'Alles muss raus.', 16, array([2.3146431e-04, 2.1641712e-04, 1.4716707e-04, ..., 1.4341300e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_415.wav', 'Stell die Heizung höher.', 25, array([-3.96930409e-05,  1.02812344e-04,  1.21250734e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_418.wav', 'Etwa über mich?', 16, array([-0.00020996, -0.00011494, -0.00010331, ..., -0.00017556,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_421.wav', 'Das ist natürlich bitter.', 26, array([-3.3627803e-04, -2.5203897e-04, -2.3072124e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_440.wav', 'Hier knicken.', 13, array([-0.000481  , -0.00023708, -0.00018911, ..., -0.00022185,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_464.wav', 'Alles Lügen!', 13, array([-0.00027017, -0.00016623, -0.00022159, ..., -0.00033337,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_465.wav', 'Alles oder nichts!', 18, array([2.8375158e-05, 6.5034241e-05, 9.6457785e-05, ..., 1.0699107e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_467.wav', 'Warum bleibst du stehen?', 24, array([-1.4808709e-04, -1.8631479e-04, -1.2836477e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_473.wav', 'Zumindest ein bisschen.', 23, array([-0.00024013, -0.00025727, -0.00025987, ..., -0.00023257,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_474.wav', 'Sprich mir nach!', 16, array([-1.7584162e-04, -1.6248986e-04, -8.6785782e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_500.wav', 'Sehr witzig!', 12, array([ 7.5077987e-05,  1.1926649e-04,  1.8323194e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_502.wav', 'Achtung, Achtung!', 17, array([-4.0950408e-04, -2.9606355e-04, -3.7786187e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_513.wav', 'SchlieÃ\\x9fen Sie bitte die Luke.', 30, array([ 0.00012086,  0.00019177,  0.00012352, ..., -0.00014259,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_549.wav', 'Ich hasse meinen Wecker.', 24, array([-1.9575720e-05, -1.5009989e-04, -1.6873972e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_576.wav', 'Nicht so laut!', 14, array([-1.6541444e-04, -8.3816949e-06, -1.0135791e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_595.wav', 'Ich tu mein Bestes.', 19, array([ 8.3501960e-05,  1.7197721e-04,  2.2250456e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_597.wav', 'Alle guten Dinge sind drei.', 27, array([-1.1909505e-05, -8.7172106e-05, -1.2401433e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_607.wav', 'Welche Vase?', 12, array([-1.8119848e-04, -2.7736003e-04, -1.8833524e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_651.wav', 'Zeig mal deine Muckis.', 22, array([-0.00038406, -0.0003124 , -0.00026326, ...,  0.00032153,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_661.wav', 'Wir sind umzingelt.', 19, array([ 4.0317194e-05,  2.1714004e-04,  1.5210512e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_669.wav', 'Du zitterst ja!', 15, array([-0.0002655 , -0.00018808, -0.00023504, ...,  0.00028222,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_687.wav', 'Ob sie schon Hunger haben?', 26, array([-7.1925861e-05,  1.8567745e-06, -5.7103756e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_699.wav', 'Das wird schon wieder.', 22, array([-2.5816666e-04, -8.4095438e-05, -1.2401373e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_700.wav', 'Köpfe runter!', 14, array([ 8.14295272e-05,  1.14302085e-04,  1.28549975e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_712.wav', 'Sie sollten sich schämen!', 26, array([ 2.6346499e-04,  9.5443167e-05,  1.6159609e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_732.wav', 'Schwund ist überall.', 21, array([-0.00039054, -0.00025168, -0.00026237, ...,  0.00020222,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_734.wav', 'Schon fertig?', 13, array([-6.8748363e-06,  5.9082297e-05, -3.8726441e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_743.wav', 'Musst du da reinschieÃ\\x9fen?', 26, array([0.00038867, 0.00026221, 0.0002308 , ..., 0.0001513 , 0.00017203,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_764.wav', 'Das wäre mir neu.', 18, array([-1.6335897e-04, -1.3920359e-04, -6.9949492e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_773.wav', 'Mission gescheitert!', 20, array([ 5.22215014e-05,  1.20894714e-04,  1.96668057e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_782.wav', 'Dir kann geholfen werden.', 25, array([-5.4091932e-05, -2.9271763e-05,  1.2364880e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_793.wav', 'Wie stellen Sie sich das vor?', 29, array([5.7090012e-05, 9.3246163e-05, 1.4314597e-04, ..., 1.8600497e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_802.wav', 'Ist es nicht so?', 16, array([ 8.5881460e-05,  1.9039282e-04,  2.1635044e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_15_FINAL/15_820.wav', 'Ihr könnt nicht fliehen.', 25, array([ 3.3208958e-04,  1.8373384e-04,  2.8849186e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_8.wav', 'Erkennst du ihn wieder?', 23, array([-7.1132112e-05,  1.8191178e-04,  2.2640963e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_29.wav', 'Du bist so ein Charmeur!', 24, array([ 8.7156383e-05, -7.5441625e-05, -8.7413508e-05, ...,\n",
+      "       -3.7287452e-04, -2.6756592e-04, -2.7199855e-04], dtype=float32), 1.99915625)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_30.wav', 'Das Wochenende war sehr schón.', 31, array([0.00010696, 0.00019241, 0.00022398, ..., 0.00018996, 0.00018264,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_44.wav', 'Na, GroÃ\\x9fer!', 12, array([2.9556373e-05, 1.2606342e-04, 2.0366564e-04, ..., 1.8486078e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_53.wav', 'Lassen wir das!', 15, array([-1.0015550e-03, -1.1123064e-03, -1.0633026e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_57.wav', 'Es kann jeden treffen.', 22, array([-1.2930187e-04, -3.5622310e-05,  1.1325534e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_61.wav', 'Das dürfen Sie nicht!', 22, array([7.9696401e-05, 2.5238540e-05, 2.6919068e-05, ..., 2.0004300e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_69.wav', 'Oder muss man die einfrieren?', 29, array([ 2.3387831e-04,  2.0287969e-04,  2.3305746e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_75.wav', 'Nur für einen Tag.', 19, array([ 1.1010072e-04,  7.5059768e-05,  1.5811465e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_85.wav', 'Ã\\x9cberall lauern Fallen.', 23, array([ 7.3672440e-05,  1.1084337e-04,  5.4723707e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_89.wav', 'Schön, dass du da warst.', 25, array([-1.5644990e-04, -1.6062504e-04, -1.5125731e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_104.wav', 'Bleib, wo du bist!', 18, array([-6.7565779e-05, -2.1604590e-06,  1.6737869e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_113.wav', 'Erwischt!', 9, array([ 8.53675301e-05, -1.39195807e-04, -1.12849986e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_119.wav', 'Dann lass es liegen.', 20, array([-1.4928725e-04,  2.6696865e-05, -8.1158723e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_125.wav', \"Mach 'ne Fliege!\", 16, array([-4.2133670e-05, -4.1710995e-05, -9.2710856e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_132.wav', 'Bei wem?', 8, array([ 3.2050626e-05, -1.8802975e-05,  6.2951531e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_164.wav', 'Einer reicht.', 13, array([-2.7248763e-05, -1.8096254e-04, -6.8749752e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_167.wav', 'Komm du mal hier her!', 21, array([-1.5554769e-04,  3.7891259e-06,  4.7066398e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_187.wav', 'Die Dämmerung bricht an.', 25, array([-1.3250955e-06,  2.9998255e-05,  7.1768205e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_209.wav', 'Ich will nur mal gucken!', 24, array([ 2.0323754e-05, -4.8527312e-05,  7.2813884e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_219.wav', 'Weg damit!', 10, array([-3.5334317e-05, -1.1389485e-04, -8.2927254e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_228.wav', 'Der kleine Tümpel?', 19, array([-1.11052366e-04, -1.58417228e-04,  1.12858004e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_261.wav', 'Danke vielmals!', 15, array([-1.0886707e-04, -2.8663597e-04, -2.3995244e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_262.wav', 'Greifen Sie zu!', 15, array([ 1.7402765e-04,  5.4675427e-05, -2.1378555e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_270.wav', 'Sein Telefon ist verwanzt.', 26, array([ 1.7227376e-05,  1.3369569e-04,  2.4036576e-04, ...,\n",
+      "       -1.2941840e-04, -7.5057469e-05,  4.6790487e-05], dtype=float32), 1.9628125)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_280.wav', 'Das kann ich nicht gutheiÃ\\x9fen.', 30, array([-2.46016367e-04, -1.46169405e-04, -1.01338104e-04, ...,\n",
+      "       -2.12353916e-06, -4.44089965e-05,  4.71521271e-05], dtype=float32), 1.9385833333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_284.wav', 'Nicht im Geringsten.', 20, array([ 6.9896785e-05,  4.9565413e-05, -5.2745858e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_290.wav', 'Magst du Sushi?', 15, array([ 4.5281922e-06, -7.7349956e-05, -9.6111427e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_294.wav', 'Ich hätte warten sollen.', 25, array([ 1.3215349e-05,  2.5886698e-05,  9.2406181e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_300.wav', 'Vielen Dank für den Hinweis.', 29, array([ 1.21899466e-04,  1.44075893e-04,  1.06153289e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_306.wav', 'Her mit dem Zaster!', 19, array([ 9.2032889e-05, -7.7123856e-05,  1.8857928e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_324.wav', 'Moment mal!', 11, array([-9.6486969e-05, -8.5642452e-05,  1.3726056e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_332.wav', 'Lass es sein.', 13, array([-6.2611114e-05,  8.5420121e-05,  1.1575574e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_334.wav', 'Wir kommen bei ihnen vorbei.', 28, array([ 3.2983281e-04,  5.1712846e-05, -1.6061698e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_350.wav', 'Es fehlt nicht mehr viel.', 25, array([ 3.4581102e-05, -3.2403619e-05,  6.4223466e-05, ...,\n",
+      "       -4.1160070e-05,  2.3247363e-05,  1.4443042e-04], dtype=float32), 1.8537708333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_351.wav', 'So entdeckt man Fehler.', 23, array([-1.5804017e-05, -7.4724383e-05,  1.1222719e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_356.wav', 'Salve!', 6, array([-1.3447071e-04,  5.3523188e-05,  8.5717998e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_374.wav', 'Angeber!', 8, array([ 4.8461781e-05,  1.5487269e-04,  9.4685849e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_376.wav', 'Wer duckt sich da weg?', 22, array([ 8.9025889e-05,  2.0651723e-04, -8.5901571e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_16_FINAL/16_396.wav', 'Schlaf schön.', 14, array([ 1.56835347e-04,  2.10795515e-05,  6.19498023e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_22.wav', 'Eindeutig nein.', 15, array([ 1.7040480e-06, -2.4771760e-05,  2.0656289e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_26.wav', 'Sie nickte.', 11, array([ 1.3571361e-04,  1.4810856e-04,  1.6444136e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_34.wav', 'Von wegen Rabenmutter!', 22, array([ 3.9614300e-05,  3.0917236e-05,  1.4100775e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_35.wav', 'Woran liegt das?', 16, array([ 1.03992148e-04,  8.12370126e-05,  1.09074477e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_68.wav', 'Das ist schlecht fürs Geschäft.', 33, array([ 3.3433552e-04,  4.7215325e-04,  3.9332887e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_70.wav', 'Das überlege ich mir noch.', 27, array([-2.7926452e-04, -4.7232458e-04, -4.5905521e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_71.wav', 'Oder er behält ihn.', 20, array([-1.95691573e-05,  1.42454119e-05, -1.12822245e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_76.wav', 'Viel Vergnügen!', 16, array([-1.4641756e-04, -2.3690579e-04, -2.0291538e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_82.wav', 'Sehr schön erklärt.', 21, array([ 2.8675116e-04,  4.4330378e-04,  3.8435950e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_104.wav', 'Du bist nicht fair.', 19, array([ 8.8045017e-05,  1.6864744e-04,  1.3682757e-04, ...,\n",
+      "       -9.7046555e-05, -1.7125324e-04, -8.5282416e-05], dtype=float32), 1.5105833333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_119.wav', 'Die in Pulverform.', 18, array([ 2.4460370e-04,  3.1504090e-04,  2.7829470e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_141.wav', 'Hier machen wir einen Schnitt.', 30, array([ 1.7057944e-04,  2.5346698e-04,  2.6541931e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_161.wav', 'Ganz und gar nicht!', 19, array([-1.09976885e-04, -1.06159037e-04, -9.40025275e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_162.wav', 'Du schnarchst.', 14, array([ 9.8031691e-05,  1.0789345e-04,  1.0408189e-04, ...,\n",
+      "        2.8527650e-06,  1.8555178e-05, -1.7833072e-05], dtype=float32), 1.2911041666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_175.wav', 'Die in der zweiten Reihe.', 25, array([-7.3503418e-04, -1.0330433e-03, -9.6690352e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_185.wav', 'Viertel nach neun.', 18, array([-0.00025316, -0.00042128, -0.00041847, ...,  0.00012852,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_214.wav', 'Der hat gut reden!', 18, array([-3.1999915e-04, -4.8188152e-04, -4.3341244e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_230.wav', \"Was gibt's denn?\", 16, array([ 5.3894956e-04,  7.5124111e-04,  6.7086820e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_233.wav', 'Fahren Sie bitte schneller.', 27, array([ 4.8254660e-04,  7.2192971e-04,  6.9296843e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_243.wav', 'Keine Ursache!', 14, array([-2.2485174e-04, -3.4637007e-04, -2.4121681e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_278.wav', 'Dann aber mit Fanfare.', 22, array([ 3.0009818e-04,  5.0011458e-04,  4.6210812e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_305.wav', 'Jeder nur eine Kugel!', 21, array([ 1.0893906e-04,  3.5140860e-05, -8.6934997e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_345.wav', 'Irgendwas ist anders.', 21, array([-0.00026138, -0.00012453, -0.00022627, ..., -0.00013074,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_371.wav', 'Ein bisschen.', 13, array([ 6.19466882e-05,  1.81855256e-04,  2.56517378e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_375.wav', 'Wir sitzen fest.', 16, array([-1.0016920e-05, -5.8360743e-05, -5.3961080e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_424.wav', 'Hat er nicht gesagt.', 20, array([1.4237937e-04, 3.5439979e-04, 4.2451522e-04, ..., 2.9889754e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_428.wav', 'Mach das ordentlich!', 20, array([-2.2249017e-04, -3.4736985e-04, -2.4423364e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_430.wav', 'Zurück zum Thema.', 18, array([0.00021488, 0.00048195, 0.00039156, ..., 0.00020808, 0.0002092 ,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_433.wav', 'Auf mich hört sowieso niemand.', 31, array([-4.4078504e-05,  1.2701395e-04,  1.5659831e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_434.wav', 'Weiter so!', 10, array([ 7.8434707e-05,  2.3782127e-04,  2.0620505e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_437.wav', 'Darüber herrscht Konsens.', 26, array([ 2.0915098e-04,  1.6340525e-04, -4.4762099e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_440.wav', 'Was ist so schlimm daran?', 25, array([ 5.3402138e-05, -1.7599798e-04,  1.1747003e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_447.wav', 'Brüllend komisch!', 18, array([2.5463186e-04, 3.0699532e-04, 1.7949699e-04, ..., 1.3379526e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_462.wav', 'Sehr einfallsreich!', 19, array([ 1.6625131e-04,  1.4804797e-04,  6.6010347e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_468.wav', 'Einer fehlt hier noch.', 22, array([0.00021585, 0.0002281 , 0.00034421, ..., 0.00031288, 0.00025684,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_505.wav', 'Wollen wir?', 11, array([-0.000173  , -0.00033364, -0.00012876, ...,  0.00012244,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_514.wav', 'Und wir singen zusammen!', 24, array([ 0.00028886,  0.00030063,  0.00037314, ..., -0.00011231,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_541.wav', 'Hier, fang!', 11, array([-9.6539197e-06,  9.8090044e-05,  7.5100412e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_548.wav', 'Ignorieren Sie die Warnung nicht.', 33, array([-7.0703449e-05, -2.1341034e-06, -2.6835096e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_558.wav', 'Nirgends ist ein Ausweg.', 24, array([ 0.0002789 ,  0.00025432,  0.00026059, ..., -0.0001307 ,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_563.wav', 'Er will schmusen.', 17, array([ 8.3865758e-05, -4.9942853e-05,  5.9117421e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_590.wav', 'GrüÃ\\x9f Gott!', 12, array([ 3.8686660e-05,  8.4167688e-05, -4.1444160e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_591.wav', 'Doch, muss es.', 14, array([-2.7301039e-05, -9.8715776e-05, -5.1679286e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_594.wav', 'Höchst verdächtig!', 20, array([-1.5668831e-04, -1.4814634e-05,  1.2133464e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_597.wav', 'Hat man das schon mal gehört?', 30, array([ 2.9468083e-05,  8.5217485e-05, -1.1223685e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_628.wav', 'Habt noch ein wenig Geduld.', 27, array([-3.1721203e-05, -6.6361958e-05,  6.2947714e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_630.wav', 'Och, Schnucki!', 14, array([-3.5877591e-05, -2.9018152e-04, -1.0041694e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_654.wav', 'Womit kann ich dienen?', 22, array([-3.79744961e-05,  4.58159229e-05,  5.13197449e-07, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_657.wav', 'Ich bin der Gerichtsvollzieher.', 31, array([ 2.9084453e-05, -2.4720324e-05,  1.8879551e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_670.wav', 'Gute Nacht zusammen!', 20, array([ 1.36263785e-04,  8.22485454e-05,  1.07259955e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_691.wav', 'Läuft die Waschmaschine noch?', 30, array([-1.7628371e-04,  3.7217360e-05,  5.7620698e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_728.wav', 'Der zweite war nicht mehr so chic.', 34, array([ 3.7413691e-05,  2.5557930e-04,  3.8776739e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_748.wav', 'Das Licht wird schwächer.', 26, array([-8.6605805e-06, -9.4557421e-05, -4.0338778e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_789.wav', 'Du hast mich durchschaut.', 25, array([-7.2653616e-05, -5.6117566e-05, -2.1032026e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_797.wav', 'Kennt ihr den Weg?', 18, array([-1.6756072e-04, -1.5301499e-04, -6.5641878e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_835.wav', 'Alles klar bei dir?', 19, array([ 1.1695884e-04,  1.1995935e-05, -1.2846527e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_18_FINAL/18_841.wav', 'Kommt jemand mit?', 17, array([ 4.9882954e-05,  4.0318602e-05,  1.2408203e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_6.wav', 'Nur vom Hörensagen.', 20, array([ 4.0408637e-04,  5.5643718e-04,  5.7215214e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_14.wav', 'Ich weiÃ\\x9f es nicht mehr.', 24, array([0.00023374, 0.00015971, 0.0001749 , ..., 0.00011659, 0.00024648,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_17.wav', 'Lass es raus!', 13, array([-3.1531116e-04, -3.3344212e-04, -5.9053692e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_68.wav', 'Sie müssen mir glauben!', 24, array([ 1.4851260e-04,  2.9638095e-04,  2.5485444e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_92.wav', 'Ihre Bestellung, bitte!', 23, array([ 9.8706114e-05,  2.2661808e-04,  1.6781769e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_95.wav', 'Was können Sie mir anbieten?', 29, array([-1.9375395e-04, -3.1588171e-04, -3.9896931e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_100.wav', 'Also nicht missverstehen!', 25, array([-1.1475936e-04,  3.5450608e-05,  5.9234120e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_101.wav', 'Jeder macht mal Fehler.', 23, array([-2.0121370e-05,  3.3358188e-05,  1.4433647e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_107.wav', 'Immer dasselbe mit dir.', 23, array([ 4.04063358e-05,  2.61971072e-05, -1.03683014e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_113.wav', 'Jetzt erinnere ich mich.', 24, array([ 5.7016779e-05,  9.8553166e-05,  8.2001083e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_131.wav', 'Freiwillige vor!', 16, array([ 5.72854588e-05,  1.07770924e-04,  1.99439557e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_137.wav', 'Ich lehne ihn sogar ab.', 23, array([ 4.1758478e-05,  1.8570285e-05,  2.1333873e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_148.wav', 'Setz dich!', 10, array([-1.4053716e-04, -1.2715683e-04, -3.6183195e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_159.wav', 'Wie lief die Klausur?', 21, array([4.5470217e-05, 1.4640424e-04, 9.2724607e-05, ..., 1.4090222e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_188.wav', 'So viel Zeit muss sein!', 23, array([-7.5860844e-05, -1.8835207e-04, -2.0893685e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_194.wav', 'Zeit fürs Bettchen.', 20, array([-9.7486656e-05, -5.1642677e-05, -8.1966471e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_236.wav', 'Wir sind gleich da.', 19, array([-6.8177519e-06,  6.7671383e-05, -1.0620675e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_243.wav', 'Herrgott noch mal!', 18, array([ 1.7256364e-04,  1.5818405e-04,  2.4684667e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_262.wav', 'Früher war alles besser.', 25, array([ 1.6410025e-04,  2.0620895e-04,  2.0922835e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_264.wav', 'Wie heiÃ\\x9ft du?', 14, array([ 2.3004458e-04,  3.3690900e-04,  3.8855671e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_269.wav', 'Totgesagte leben länger.', 25, array([-1.0916409e-05, -1.7836766e-05, -5.1411305e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_291.wav', 'Bin ich männlich?', 18, array([-2.0014251e-05,  2.6616051e-05,  1.2375216e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_349.wav', 'Eine letzte Windung noch.', 25, array([-1.8898114e-05, -4.0488834e-05,  1.2324851e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_375.wav', 'Kein Signal gefunden.', 21, array([-1.1299809e-04, -9.9104131e-05, -2.1005377e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_461.wav', 'Bist du noch Single?', 20, array([-7.4286567e-05, -1.6158549e-04, -1.6719839e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_468.wav', 'Mein Licht ist kaputt.', 22, array([ 4.02122387e-05, -1.00659774e-04, -8.88236755e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_518.wav', 'Er hat Mama gesagt!', 19, array([ 2.6662483e-05, -7.8772522e-05, -5.4227519e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_551.wav', 'Ciao!', 5, array([-8.1419050e-05, -2.2554104e-05, -9.1002643e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_618.wav', 'Geht das in Ordnung?', 20, array([-1.7039385e-04, -4.3828294e-04, -3.7954788e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_649.wav', 'Komm noch etwas näher!', 23, array([ 2.4222159e-06, -1.3579089e-04, -4.4756231e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_19_FINAL/19_703.wav', 'Ja, ist sie.', 12, array([-8.3997344e-05, -2.7474607e-05, -1.9123188e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_1_FINAL/1_203.wav', 'Ach Mann.', 9, array([-1.1161302e-05, -4.8241122e-06,  1.0564104e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_1_FINAL/1_220.wav', 'Ach so das.', 11, array([1.7692106e-05, 1.0481614e-05, 2.4560395e-05, ..., 1.1682997e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_22.wav', 'Eine letzte Sache noch.', 23, array([6.1327388e-05, 1.8792783e-04, 6.4210355e-05, ..., 9.2773196e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_57.wav', 'Wie machst du das?', 18, array([ 2.4910548e-04,  4.8663982e-04,  3.5670877e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_88.wav', 'Die Göre lügt wie gedruckt.', 29, array([-2.1256006e-04, -1.5941747e-04, -9.0014306e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_94.wav', 'Nur wenn das Essen nicht schmeckt.', 34, array([-7.44715726e-05, -1.21678349e-04,  3.31091655e-07, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_97.wav', 'Niemals!', 8, array([-4.9271861e-05,  5.3212247e-05,  3.3188411e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_98.wav', 'Und nun zum Wetter.', 19, array([ 5.21471120e-05, -9.25690911e-05, -1.22024496e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_99.wav', 'Die Ã\\x96ffnung ist dehnbar.', 25, array([ 5.6826313e-05,  6.8275417e-06,  9.2087415e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_108.wav', 'Habt ihr schon angefangen?', 26, array([1.8725816e-04, 1.5125435e-04, 1.8410715e-04, ..., 7.2607516e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_120.wav', 'Wie konnte das passieren?', 25, array([8.6616979e-05, 1.3365489e-04, 4.9586175e-05, ..., 2.3242908e-06,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_121.wav', 'Schnappen Sie die!', 18, array([ 3.8893679e-05, -8.0967751e-05,  9.0245063e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_127.wav', 'Kommst du mit auf die Demo?', 27, array([2.5501425e-04, 3.7619186e-04, 2.3280202e-04, ..., 1.0214894e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_131.wav', 'Neymar schummelt immer.', 23, array([ 9.5439558e-05, -2.0274975e-04, -2.7297903e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_146.wav', 'Hä, wie jetzt?', 15, array([ 1.0428468e-05,  1.2862872e-04,  1.4709163e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_154.wav', 'Gehst du mit mir kicken?', 24, array([-2.3674214e-05,  1.5158611e-04,  2.0247647e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_165.wav', 'Die Erlösung naht.', 19, array([-2.7500470e-05,  4.6476634e-05,  9.3239294e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_170.wav', 'Worauf wartest du noch?', 23, array([ 1.9643597e-04,  1.8858226e-04,  1.2341220e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_174.wav', 'Wegen der Sicherheit.', 21, array([ 5.0312192e-05, -4.7642745e-05,  7.9094330e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_179.wav', 'Was tun Sie da?', 15, array([8.1822727e-05, 1.5520566e-04, 2.9996689e-04, ..., 9.4358256e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_190.wav', 'Die Play-Offs haben begonnen.', 29, array([-2.7691500e-04, -2.5398214e-04, -1.5421546e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_193.wav', 'Spinnst du?', 11, array([ 1.0871515e-04,  1.6241276e-04, -7.8830650e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_194.wav', 'Nicht mit mir!', 14, array([ 4.5433408e-05, -1.3075510e-04,  6.4006963e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_197.wav', 'Lang lebe die Königin!', 23, array([-1.6047362e-04, -1.5451153e-05, -1.0221335e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_220.wav', 'Der Punkt geht an euch.', 23, array([ 6.8754802e-05, -3.1321447e-06,  2.6729414e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_253.wav', 'Ich geh jetzt duschen.', 22, array([-3.17401755e-05,  7.48557359e-05, -5.43324859e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_270.wav', 'Ich bin stärker.', 17, array([ 9.11816460e-05,  1.44324003e-04, -2.98500763e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_273.wav', 'Wie lange fahre ich noch?', 25, array([ 2.9487488e-05, -1.3105408e-04,  5.8441510e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_275.wav', 'Wer hat es dir verraten?', 24, array([ 1.2313928e-04,  1.3087156e-04, -1.2932777e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_293.wav', 'Gib nicht anderen die Schuld.', 29, array([-7.5512668e-05, -3.6905835e-06,  6.9531779e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_297.wav', 'Ist es schon so weit gekommen?', 30, array([-4.3128319e-05, -1.7937485e-04, -1.0890597e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_318.wav', 'Einfach reinstechen!', 20, array([ 9.1551570e-05,  8.9795518e-05, -6.6505017e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_320.wav', 'Also bleibt alles beim Alten.', 29, array([-4.5057204e-06,  1.0390608e-04,  2.8324797e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_324.wav', 'Fragen wir das Publikum!', 24, array([ 3.0478600e-06, -1.7624698e-04, -1.1634296e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_336.wav', 'Nicht nur in Norddeutschland.', 29, array([ 1.6894817e-05,  7.2304661e-05, -1.7737957e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_339.wav', 'Lass uns welche wegräumen.', 27, array([-1.3355519e-04,  3.6361063e-05,  1.2765500e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_356.wav', 'Bauch schlägt Hirn.', 20, array([-8.7791312e-05, -9.9132430e-06, -7.8506528e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_20_FINAL/20_381.wav', 'Polen Sie die Maschine um!', 26, array([ 8.2736617e-05,  1.0996176e-04,  9.2422182e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_125.wav', 'Danke sehr!', 11, array([ 3.3982175e-05,  3.0489264e-05, -3.2230830e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_132.wav', 'Nerve ich dich?', 15, array([-1.3204044e-04, -3.8424434e-05, -1.6640245e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_188.wav', 'Kann ich mal riechen?', 21, array([7.4782380e-05, 1.5360968e-04, 1.7683143e-04, ..., 7.1163136e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_211.wav', 'Ein Dessert gefällig?', 22, array([ 6.1982937e-05,  8.9088433e-05,  2.1896411e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_264.wav', 'Was schätzen Sie?', 18, array([-9.39443475e-04, -1.31584110e-03, -1.22378767e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_266.wav', 'Hast du Geld dabei?', 19, array([-1.3200377e-05,  3.8996362e-04,  1.0263748e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_346.wav', 'Was soll ich da machen?', 23, array([-2.5878362e-05,  2.1881026e-05, -1.2260079e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_349.wav', 'Immer dasselbe mit euch!', 24, array([-2.3236821e-04, -3.3517351e-04, -3.0884243e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_373.wav', 'Kennen wir uns?', 15, array([-5.0764916e-06, -7.3543859e-05,  1.1312031e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_378.wav', 'Redet ihr nicht miteinander?', 28, array([ 3.3598881e-05,  2.8617033e-05, -4.8224880e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_3_FINAL/3_577.wav', 'Nun ja.', 7, array([-4.87583275e-05, -1.09872217e-05, -2.24729556e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_4_FINAL/4_399.wav', 'Ganz wie ihre Mutter!', 21, array([-1.3625373e-05, -1.5324851e-05, -8.2329316e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_95.wav', 'Ist noch alles dran?', 20, array([-2.2047247e-04, -3.2201153e-04, -2.8738266e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_99.wav', 'Nie glaubt sie mir.', 19, array([ 1.5801163e-05,  5.7899309e-05,  3.1942949e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_207.wav', 'Sperrt sie ein!', 15, array([1.7913821e-04, 3.0638310e-04, 2.4345164e-04, ..., 5.7913669e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_249.wav', 'Ja was geht denn ab?', 20, array([-1.0661902e-04, -9.4065879e-05, -6.9818758e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_250.wav', 'Dümmste Ausrede ever!', 22, array([ 3.16905534e-05,  3.74705655e-06, -2.55898794e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_251.wav', 'Wir sind hier ja unter uns.', 27, array([-3.3862656e-04, -5.0057843e-04, -4.7798100e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_278.wav', 'Er ist ein User!', 16, array([ 5.7516689e-05,  4.9558192e-05,  6.3942927e-05, ...,\n",
+      "       -2.3214375e-06,  1.1798247e-05,  3.6477853e-05], dtype=float32), 1.9626666666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_282.wav', 'Zurückbleiben, bitte!', 22, array([ 1.8404999e-04,  2.6386097e-04,  3.0643051e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_287.wav', 'Gut getrollt.', 13, array([-3.0470208e-05, -6.1425657e-05, -3.8205933e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_336.wav', 'Ganz sicher sogar.', 18, array([2.2912030e-04, 2.5114618e-04, 1.9525687e-04, ..., 8.7549386e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_341.wav', 'Wohl kaum.', 10, array([ 1.6102573e-04,  1.7911245e-04,  1.5706589e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_405.wav', 'Wie geht das?', 13, array([-1.6796951e-04, -1.9163813e-04, -1.9830326e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_407.wav', 'Befehl ist Befehl!', 18, array([ 9.3892188e-05,  1.0890782e-04,  9.6308002e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_412.wav', 'Mit wem spreche ich?', 20, array([ 7.7782068e-05,  9.2144561e-05,  2.8574361e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_422.wav', 'An schlechten Tagen ja.', 23, array([ 4.2690190e-05, -2.3120232e-05, -2.5523063e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_460.wav', 'Sie haben richtig geraten!', 26, array([-9.0950904e-05, -1.4647168e-04, -7.1847418e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_476.wav', 'Alle sprechen so leise.', 23, array([-6.9834332e-06, -3.1972188e-05, -3.9375213e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_477.wav', 'Woher willst du das wissen?', 27, array([-2.12417421e-04, -2.56415573e-04, -2.42886104e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_488.wav', 'Anders als man denkt.', 21, array([ 1.8948530e-04,  3.4113604e-04,  1.9700162e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_495.wav', 'Runter mit den Waffen!', 22, array([ 1.12369155e-04,  4.44092657e-05,  8.84383553e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_504.wav', 'Und jetzt?', 10, array([-5.6267181e-06, -5.9708807e-05, -3.4106170e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_511.wav', 'Jein.', 5, array([ 5.89297160e-05,  1.19100565e-04,  6.77589633e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_522.wav', 'Vorsicht Stufe!', 15, array([ 6.2581657e-06,  4.7380847e-05,  8.6832886e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_526.wav', 'War ich zu zickig?', 18, array([1.6193213e-03, 2.2825657e-03, 2.0064272e-03, ..., 6.6650551e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_530.wav', 'Wo drückt der Schuh?', 21, array([-1.46389175e-05,  3.62552214e-06, -9.26516877e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_534.wav', 'Kann das noch warten?', 21, array([1.74110639e-04, 1.80995979e-04, 2.26840231e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_539.wav', 'Passen die Sätze so?', 21, array([-3.1769360e-04, -4.7089945e-04, -4.3369626e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_542.wav', 'Ã\\x9cbermorgen.', 12, array([-2.4301407e-04, -3.5653665e-04, -2.1825638e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_589.wav', 'Ich mag deinen Mantel.', 22, array([-2.1532472e-04, -3.8814778e-04, -2.9697348e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_604.wav', 'Wie macht er das bloÃ\\x9f?', 23, array([-1.8150010e-04, -2.0398400e-04, -1.5460433e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_666.wav', 'Was soll ich sagen?', 19, array([-8.8535160e-07, -7.4019059e-05,  7.4082243e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_694.wav', 'Wie misst man das?', 18, array([ 2.5176766e-04,  1.8225121e-04,  3.6178919e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_704.wav', 'Mir fehlen die Worte.', 21, array([ 1.7020236e-04,  3.3776514e-04,  3.4704659e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_714.wav', 'Gehen wir?', 10, array([ 1.5890028e-04,  1.6513607e-04,  1.7650245e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_723.wav', 'Ich komme noch mal dran.', 24, array([-4.6879621e-05, -1.1869792e-04, -5.2995206e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_742.wav', 'Bitte schön!', 13, array([-3.4623430e-04, -4.4416677e-04, -3.0297900e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_743.wav', 'Das haben sie gesagt.', 21, array([-2.3902958e-05,  4.5714452e-05,  7.7266725e-07, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_799.wav', 'Ist der Kugelschreiber blau?', 28, array([-1.6907173e-04, -2.9390136e-04, -2.4633619e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_21.wav', 'Alles nach vorne!', 17, array([2.0106880e-04, 3.4844220e-04, 2.3129249e-04, ..., 9.6451986e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_55.wav', 'Nichts dergleichen.', 19, array([-2.7673854e-04, -3.7996779e-04, -2.6658855e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_66.wav', \"Langsam nervt's.\", 16, array([ 7.7058452e-05,  4.7672478e-05,  2.6094380e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_98.wav', 'Seid ihr verrückt?', 19, array([-1.3435316e-04, -1.8146966e-04, -1.6307829e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_110.wav', 'Gib mir fünf!', 14, array([1.9428060e-04, 2.9409130e-04, 2.5521498e-04, ..., 1.9916235e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_140.wav', 'Auch wieder wahr.', 17, array([-6.21244908e-05, -1.39888449e-04, -1.16935575e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_167.wav', 'Sicher ist sicher.', 18, array([ 1.6774700e-04,  2.7458806e-04,  1.3175888e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_175.wav', 'Wie soll ich sagen?', 19, array([-2.0688836e-05, -6.4790765e-05, -1.1548823e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_200.wav', 'Ist doch Ehrensache!', 20, array([ 1.07319385e-04,  1.08591557e-04,  6.78624638e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_258.wav', 'Jeder Mensch ist anders.', 24, array([ 9.4392788e-05,  1.3444535e-04,  1.5623294e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_262.wav', 'Nächstes Mal vielleicht.', 25, array([-4.9963495e-04, -7.3549181e-04, -5.7168922e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_290.wav', 'Ich wollte nur nett sein.', 25, array([-3.0248266e-04, -4.1539475e-04, -4.3182663e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_293.wav', 'Sie haben Post.', 15, array([7.0743052e-05, 1.5683858e-04, 7.2936782e-05, ..., 3.4985551e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_302.wav', 'Mit dem Raumschiff bitte!', 25, array([-3.3868386e-05, -4.2923082e-05,  2.2873657e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_309.wav', 'Hä, wieso das denn?', 20, array([-4.2834796e-05, -1.3094838e-04, -2.1130700e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_321.wav', 'Lass dich nicht so hängen!', 27, array([ 3.3312430e-05,  1.1557561e-04,  1.7304946e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_333.wav', 'Sehen wir uns in der Bib?', 25, array([1.8330962e-04, 1.0809512e-04, 2.0564985e-04, ..., 5.3472275e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_340.wav', 'Klingt logisch.', 15, array([-4.9080444e-07, -4.6037778e-05, -1.0552061e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_361.wav', 'Lies mir etwas vor!', 19, array([-5.9860780e-05, -1.2714561e-04, -4.6063276e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_368.wav', 'Nö, nicht wirklich.', 20, array([1.4233610e-05, 5.8029418e-05, 2.2922040e-05, ..., 2.8016962e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_401.wav', 'Besser als gar nichts.', 22, array([-1.9661777e-04, -3.8629526e-04, -3.8140707e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_402.wav', 'Lass mich doch mal träumen.', 28, array([5.1605228e-05, 2.0454232e-05, 5.4702823e-06, ..., 1.0539140e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_407.wav', 'Wochenende!', 11, array([-7.1158116e-05, -1.3735623e-04, -1.4360537e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_410.wav', 'Ich habe dich gewarnt.', 22, array([-2.9008405e-04, -3.9160642e-04, -3.8535651e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_420.wav', 'Kann schon sein.', 16, array([8.5848145e-04, 1.2030958e-03, 1.0428407e-03, ..., 9.0862151e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_430.wav', 'Schön gespielt.', 16, array([-3.1265599e-04, -3.5982658e-04, -3.4920897e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_432.wav', 'Gut geschlafen?', 15, array([-4.6266021e-05, -4.5735891e-05, -1.5800438e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_435.wav', 'Auf das Wetter natürlich auch.', 31, array([-3.5034932e-04, -4.7157385e-04, -4.0150300e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_437.wav', 'Komm, geh weg!', 14, array([ 3.15589714e-05,  1.08517845e-04,  6.59165744e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_469.wav', 'Schluss mit lustig!', 19, array([ 1.0199297e-04,  1.2600295e-04,  1.6211855e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_483.wav', 'Das spart Geschirr.', 19, array([ 6.6607544e-04,  7.1844418e-04,  6.1214896e-04, ...,\n",
+      "       -3.3901462e-05,  1.3226962e-04,  3.8378406e-05], dtype=float32), 1.8080208333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_486.wav', 'Das haben Recherchen ergeben.', 29, array([-9.0566078e-05, -2.1272554e-04, -1.9089306e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_487.wav', 'Frohes Schaffen!', 16, array([ 6.8461159e-05,  1.5294057e-04,  2.2618793e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_500.wav', 'Sie sind ja noch blutjung!', 26, array([-0.00065145, -0.00103323, -0.00116705, ..., -0.0001188 ,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_516.wav', 'Lebe ich noch?', 14, array([-4.3064877e-04, -5.6503405e-04, -4.1817623e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_527.wav', 'Nicht dafür!', 13, array([ 3.5247151e-04,  4.8163909e-04,  3.9777748e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_534.wav', 'Genau hundert Stück.', 21, array([-0.00059065, -0.00093307, -0.00079542, ...,  0.00016691,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_535.wav', 'Wie ist das möglich?', 21, array([ 3.7494919e-04,  5.0490367e-04,  3.7185123e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_536.wav', 'Alles wiederholt sich.', 22, array([-7.8303702e-03, -9.4565414e-03,  4.3799067e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_538.wav', 'Der Klügere gibt nach.', 23, array([-3.3002507e-04, -4.8394629e-04, -4.5790782e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_559.wav', 'Schwing die Hufe!', 17, array([-0.00077766, -0.00118464, -0.00101971, ..., -0.00019519,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_561.wav', 'Was das wieder kostet!', 22, array([ 8.5937936e-04,  1.1237016e-03,  9.1907283e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_563.wav', 'Wieso immer ich?', 16, array([4.5056498e-04, 7.2014128e-04, 6.0793286e-04, ..., 8.4482606e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_582.wav', 'Dann gäbe es dich jetzt nicht.', 31, array([-2.4657813e-04, -3.9872411e-04, -3.3457237e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_585.wav', 'Dem werde ich Beine machen!', 27, array([0.00027461, 0.00040794, 0.00034263, ..., 0.00012492, 0.00024055,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_588.wav', 'Wollen wir Ihn herein lassen?', 29, array([-3.2398489e-04, -4.3375781e-04, -3.6100275e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_590.wav', 'Richtig geraten!', 16, array([-2.6969259e-04, -4.4567345e-04, -5.3715584e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_634.wav', 'Nun sag schon!', 14, array([-0.00074525, -0.0010401 , -0.00091129, ...,  0.00015909,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_658.wav', 'Mit Vergnügen!', 15, array([-1.9300323e-04, -2.6942717e-04, -2.3031878e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_659.wav', 'Komm sofort her!', 16, array([ 5.0228823e-04,  8.3419622e-04,  7.3006074e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_674.wav', 'Chill mal!', 10, array([ 3.6116564e-04,  5.9050595e-04,  4.8674442e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_677.wav', 'Jetzt mal Butter bei die Fische.', 32, array([-0.00017322, -0.00025202, -0.0003011 , ..., -0.00014372,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_705.wav', 'Das wird Macken geben.', 22, array([ 2.6667553e-06,  2.4150137e-05,  6.4756452e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_707.wav', 'Hilf mir mal auf die Sprünge.', 30, array([ 3.0066914e-04,  4.8592529e-04,  4.8968260e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_747.wav', 'Versuch macht klug.', 19, array([-6.13919692e-04, -8.45544797e-04, -7.43770273e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_768.wav', 'Kapiere ich nicht.', 18, array([ 4.0008963e-04,  6.7968445e-04,  6.0982589e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_776.wav', 'Der ist ja mickrig!', 19, array([-1.7217337e-04, -2.9700578e-04, -2.6711932e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_777.wav', 'Ja, sogar mehrere.', 18, array([ 1.1276272e-03,  1.6285295e-03,  1.3798362e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_6_FINAL/6_778.wav', 'Fünf oder lieber sechs?', 24, array([-0.00051076, -0.00086243, -0.00095237, ..., -0.00015284,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_1.wav', 'Wen interessiert das schon?', 27, array([-2.0386204e-04, -1.6595512e-04, -3.4064340e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_4.wav', 'Das sieht man sofort.', 21, array([-4.7220071e-04, -6.1083253e-04, -5.2480790e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_9.wav', 'Kannst du ein Instrument spielen?', 33, array([-5.8206980e-04, -9.0975891e-04, -9.2016242e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_17.wav', 'Nein, hör mir zu!', 18, array([1.8352878e-04, 2.3541819e-04, 1.9473537e-04, ..., 3.8015917e-06,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_24.wav', 'Sowas ist schade.', 17, array([ 5.2204914e-04,  7.2680251e-04,  7.3363306e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_48.wav', 'Ich will zocken!', 16, array([-0.00016469, -0.00039593, -0.00179843, ...,  0.00018615,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_55.wav', 'Ein Insider berichtet.', 22, array([ 3.7575817e-05,  2.7695228e-04,  1.8994253e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_65.wav', 'Evelyn ist seekrank.', 20, array([0.00062829, 0.00093936, 0.0008276 , ..., 0.00017747, 0.00012535,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_66.wav', 'Zunächst der Blick aufs Wetter.', 32, array([-0.00092968, -0.00141539, -0.00128506, ...,  0.00019455,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_77.wav', 'Was schmeckt am besten?', 23, array([6.4622820e-04, 1.0704662e-03, 1.1439651e-03, ..., 1.9296777e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_78.wav', 'Wir rufen Sie dann auf.', 23, array([-1.0261516e-03, -1.4563096e-03, -1.2881490e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_84.wav', 'Das Essen war vorzüglich.', 26, array([-5.0324254e-04, -7.2285999e-04, -5.4835685e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_89.wav', 'SüÃ\\x9fes oder Saures!', 20, array([-2.1448301e-04, -3.2685092e-04, -1.9420320e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_96.wav', 'Woran das wohl liegt?', 21, array([7.9406239e-04, 1.0801835e-03, 8.6238224e-04, ..., 1.5784081e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_118.wav', 'Hier hast du deinen Fisch.', 26, array([0.00047934, 0.0008143 , 0.00071459, ..., 0.00040429, 0.00026866,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_135.wav', 'Und zwar hochverdient!', 22, array([-3.4465449e-04, -5.7459215e-04, -4.8516967e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_144.wav', 'Da kräht kein Hahn nach.', 25, array([-2.4579404e-05, -2.7367115e-04, -1.3865142e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_148.wav', 'Du zuerst.', 10, array([-8.7500273e-05, -8.8356370e-05,  3.9270883e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_149.wav', 'Hier mal eine Faustregel.', 25, array([7.6173781e-04, 9.7895204e-04, 8.7399769e-04, ..., 5.2696447e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_150.wav', 'Ich sehe kein Leerzeichen.', 26, array([-2.0238354e-05, -3.9017228e-05, -1.8151976e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_151.wav', 'Hast du mal einen Fünfziger?', 29, array([-6.5894198e-04, -9.4568409e-04, -8.3610136e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_155.wav', 'Mit Pommes?', 11, array([ 0.0003422 ,  0.0003448 ,  0.00032375, ..., -0.00023719,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_172.wav', 'Noch fünf Minuten bitte, Schatz!', 33, array([-4.4656807e-04, -5.2705233e-04, -5.8281276e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_217.wav', 'So, so.', 7, array([5.1622407e-04, 8.1000535e-04, 6.2310486e-04, ..., 1.1862206e-04,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_298.wav', 'Nachricht bitte faxen!', 22, array([-3.4957391e-04, -4.1374876e-04, -4.3978900e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_333.wav', 'Was will man mehr?', 18, array([-8.3821319e-04, -1.1214241e-03, -1.0474359e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_362.wav', 'Ganz der Papa!', 14, array([ 1.0614250e-06,  1.0387501e-04,  2.6466480e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_368.wav', 'Es geht schon, danke!', 21, array([-3.1714016e-04, -4.7203674e-04, -3.6235168e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_404.wav', 'Notieren Sie sich das.', 22, array([-3.1276091e-04, -4.1585916e-04, -4.4194568e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_440.wav', 'Geh, Martin. Geh!', 17, array([-0.0006147 , -0.00096355, -0.00084441, ..., -0.00019064,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_464.wav', 'Hören Sie erst einmal zu!', 26, array([-2.3209564e-03, -3.7553089e-03, -3.8581355e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_481.wav', 'Du kennst doch Tessa.', 21, array([-9.7565542e-05, -8.4838466e-05, -2.1631434e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_487.wav', 'Angeber und Neidhammel.', 23, array([-4.2524905e-04, -5.5071624e-04, -4.9216941e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_491.wav', 'Können diese Augen lügen?', 27, array([-1.04710832e-03, -1.57430710e-03, -1.43215503e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_542.wav', 'Nimm deine Maske endlich ab!', 28, array([ 5.4343470e-04,  7.2278164e-04,  7.2296784e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_599.wav', 'Das ist knorke.', 15, array([-8.0439750e-06, -4.1563135e-06, -3.6478632e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_603.wav', 'Suchst du Ã\\x84rger?', 17, array([ 1.8951594e-04,  3.2533749e-04,  2.3231433e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_7_FINAL/7_745.wav', 'Kannst du bitte das Licht anlassen?', 35, array([-4.5024044e-05, -6.6272514e-05, -1.4942518e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_254.wav', 'Weiter zum nächsten Kapitel.', 29, array([ 2.7818656e-05,  2.9083269e-05,  2.7292099e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_255.wav', 'Holla die Waldfee!', 18, array([ 3.2722608e-05, -3.4862321e-06,  2.1344584e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_278.wav', 'Lass mich nicht allein.', 23, array([-6.2487576e-05, -5.1307488e-05,  3.3147335e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_283.wav', 'Warst du beim Frisör?', 22, array([-5.1554598e-05, -2.8181448e-05, -2.1276550e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_307.wav', 'Das klingt sehr gut.', 20, array([ 8.8375481e-07,  1.4093188e-06, -8.0541049e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_310.wav', 'Kann man das mitessen?', 22, array([-1.8419527e-05, -2.5431269e-05, -8.9255473e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_311.wav', 'Wo liegt das Problem?', 21, array([-1.7069402e-05,  2.2379625e-06, -8.6348446e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_313.wav', 'Wo kann man sich ausloggen?', 27, array([-2.94713544e-07, -2.60781735e-06,  2.09315767e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_337.wav', 'Und wenn man die nicht hat?', 27, array([-4.4960318e-05,  5.2144351e-05, -2.9507015e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_352.wav', 'Jetzt oder nie!', 15, array([-1.7882267e-05,  1.5871639e-05, -7.5667369e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_368.wav', 'Hinsetzen und FüÃ\\x9fe hoch!', 26, array([-3.0999392e-05, -7.2621566e-05, -4.7179296e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_394.wav', 'Nimm dir mal eine Pause!', 24, array([-2.7986377e-04, -3.0645030e-04, -2.3860915e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_427.wav', 'Vom Kinde verschmäht.', 22, array([2.4927745e-05, 5.9401387e-05, 5.5517099e-05, ..., 8.8263223e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_468.wav', 'Das lass mal meine Sorge sein.', 30, array([-4.7126541e-05, -5.9281327e-05, -3.5599784e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_500.wav', 'Ich habe heute Geburtstag.', 26, array([-5.1001366e-06,  4.8161728e-05,  1.0626727e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_534.wav', 'Den Schuss nicht hören.', 24, array([1.0162838e-04, 1.3316146e-04, 1.3368837e-04, ..., 5.8495625e-06,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_569.wav', 'Ein Zirkus ohne Tiere?', 22, array([-2.8725301e-05, -5.8967784e-05, -4.7625667e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_594.wav', 'Einer zur Zeit!', 15, array([-1.9068037e-05, -2.0037192e-05, -8.8215660e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_606.wav', 'Okay, und nun?', 14, array([-1.2366170e-05,  2.3954278e-06, -1.8647337e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_638.wav', 'Nicht schlecht der Specht!', 26, array([-1.0250892e-05,  1.4861113e-05, -5.1604333e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_647.wav', 'Was haben die vor?', 18, array([ 1.2927976e-06, -4.4330540e-05, -4.2087355e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_648.wav', 'Ich habe gar nichts mitbekommen.', 32, array([ 1.6062468e-05,  4.4314598e-05,  1.1317232e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_664.wav', 'Je mehr, desto besser.', 22, array([-1.0978662e-05,  2.8232571e-06, -2.7930673e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_677.wav', 'Da vorne links!', 15, array([ 3.8325859e-05,  3.2421449e-05,  1.5961947e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_682.wav', 'Jetzt mal halblang!', 19, array([-2.0417360e-06, -1.3626728e-05, -2.8990502e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_687.wav', 'Gib uns ein Beispiel!', 21, array([-8.4907850e-05, -5.6986839e-05,  3.7472455e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_689.wav', 'Von wegen!', 10, array([-2.7207323e-05, -6.9836324e-06, -9.1906164e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_691.wav', 'Das finde ich ziemlich doof.', 28, array([-2.9834633e-05,  5.6474819e-06, -2.5375591e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_697.wav', 'Das trifft sich gut.', 20, array([ 7.9529818e-06,  3.9593842e-06,  3.0517844e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_704.wav', 'Jetzt gibt es Zoff.', 19, array([ 1.7251841e-05,  3.0525447e-05,  4.0081544e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_708.wav', 'Liebe ist kein Verbrechen.', 26, array([ 1.3942296e-03,  2.0183886e-03,  1.7392144e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_725.wav', 'Auch das noch!', 14, array([ 6.9235853e-06,  1.0541713e-05, -6.9821567e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_728.wav', 'Toller Hengst!', 14, array([ 1.5415973e-05,  1.2052349e-05,  2.2745300e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_748.wav', 'Reine Gewöhungssache.', 22, array([-7.46887818e-05,  3.63702893e-05,  2.65028193e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_764.wav', 'Siehe weiter unten.', 19, array([ 1.0315010e-04,  1.2668683e-04,  1.3160890e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_778.wav', 'Hilfe ein Ã\\x9cberfall!', 20, array([-9.1011774e-05, -1.6054764e-04, -6.9503607e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_8_FINAL/8_804.wav', \"Wen wundert's?\", 14, array([ 1.8174978e-05,  1.0757233e-05,  1.4760263e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_19.wav', 'Bis die Schwarte kracht.', 24, array([ 3.15900324e-05, -1.30308879e-04,  3.94875406e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_26.wav', 'Auch das wäre möglich.', 24, array([-4.5410670e-05,  1.9743770e-06, -1.9743769e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_28.wav', 'Was geht gar nicht?', 19, array([ 1.9743770e-06,  9.8718847e-06, -3.3564411e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_33.wav', 'Die Geschichte geht anders.', 27, array([-3.3564411e-05, -7.7000703e-05, -8.2923834e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_42.wav', 'Welche SchuhgröÃ\\x9fe?', 20, array([ 0.0000000e+00, -1.1846262e-05, -5.9231311e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_51.wav', 'Mediathek aufrufen!', 19, array([-3.6328536e-04,  1.9941208e-04, -8.4898209e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_54.wav', 'Es tut ihr furchtbar leid.', 26, array([-3.3564411e-05, -4.9359427e-05,  1.1846262e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_62.wav', 'Noch mal von vorne, bitte.', 26, array([ 2.5666901e-05, -2.9615656e-05, -3.7513164e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_63.wav', 'Oh jemine!', 10, array([-1.7769393e-05, -6.9103196e-05, -3.7513164e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_84.wav', 'Reich mir den mal rüber.', 25, array([-1.5795016e-05, -9.8718847e-06,  6.7128822e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_101.wav', 'Findest du nicht auch?', 22, array([-7.3051953e-05, -9.8718847e-06,  5.9231311e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_113.wav', 'Alles korrekt.', 14, array([ 1.1846262e-05,  2.9615656e-05,  1.2833450e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_128.wav', 'Alles wird gut.', 15, array([-9.2795723e-05, -3.1590032e-05,  8.2923834e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_156.wav', 'Würde ich auch machen.', 23, array([-6.1205690e-05, -5.3308180e-05, -5.5282559e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_173.wav', 'Gib Gas!', 8, array([ 3.7513164e-05,  7.1077571e-05, -1.9743770e-06, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_221.wav', 'Weil er es kann.', 16, array([ 3.9487541e-06, -3.1590032e-05,  2.1718148e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_225.wav', 'Was ist der Sinn des Lebens?', 28, array([-4.7385049e-05, -9.0821341e-05,  8.6872591e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_247.wav', 'Es ist kalt.', 12, array([-5.33081802e-05, -1.14513867e-04,  2.36925243e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_259.wav', 'ScheiÃ\\x9f drauf!', 14, array([ 9.8718854e-05,  3.9487541e-06,  5.9231312e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_271.wav', 'Katzen haben sieben Leben.', 26, array([-1.57950162e-05,  7.89750811e-06, -6.12056901e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_285.wav', 'Nicht so lasch!', 15, array([ 3.3564411e-05,  1.4412952e-04, -8.8846966e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_309.wav', 'Ich gehe dann mal kicken.', 25, array([-7.8975077e-05, -5.1333802e-05,  2.1718148e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_313.wav', 'Versuchen Sie es später noch einmal!', 37, array([1.7769393e-04, 1.5597578e-04, 7.7000703e-05, ..., 1.7769393e-05,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_327.wav', 'Ulrike muss es ja wissen.', 25, array([ 1.3820640e-05, -4.3436296e-05, -2.5666901e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_329.wav', 'So viele schon?', 15, array([-1.1451387e-04, -9.4770097e-05,  1.3820640e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_339.wav', 'Noch nicht.', 11, array([-1.1569849e-03, -1.1234205e-03, -1.1056511e-03, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_351.wav', 'So alt bin ich dann auch wieder nicht.', 38, array([ 1.0957792e-03,  8.6082838e-04,  5.8836438e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_352.wav', 'Diese Gelegenheit kann man nutzen.', 34, array([ 1.2043700e-04,  1.9743769e-05,  7.5026328e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_361.wav', 'Bist neidisch, was?', 19, array([ 3.9487539e-05,  1.7769393e-05, -2.9615656e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_364.wav', 'Puh, das Quiz ist schwer!', 25, array([-5.9231312e-05, -6.9103196e-05, -8.2923834e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_371.wav', 'Alles klärchen!', 16, array([-3.9487541e-06,  3.3564411e-05,  1.5795016e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_392.wav', 'Zeig mal dein Piercing!', 23, array([ 5.7256933e-05,  1.3820640e-05,  3.5538786e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_393.wav', 'Parlieren Sie doch im Park!', 27, array([-7.1077571e-05, -5.3308180e-05, -5.7256933e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_400.wav', 'Hä, was? 400', 13, array([ 3.5538786e-04,  2.7443841e-04,  2.5469463e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_431.wav', 'Tun Sie nicht so überrascht!', 29, array([-2.2310461e-04, -2.6259213e-04, -3.0800281e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_438.wav', 'Was lernen wir daraus?', 22, array([ 3.35644108e-05, -7.70007027e-05, -7.30519532e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_439.wav', 'Was will sie denn noch?', 23, array([ 5.3308180e-05,  5.7256933e-05, -3.9487539e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_441.wav', 'Ich stecke fest.', 16, array([-1.6584767e-04, -1.5795015e-04, -1.3030888e-04, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_451.wav', 'Es riecht nach Sonnencreme.', 27, array([-4.1461917e-05, -3.7513164e-05,  2.1718148e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_452.wav', 'Da entlang!', 11, array([-1.7769393e-05,  5.9231311e-06,  1.7769393e-05, ...,\n",
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+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_453.wav', 'Tja, Thaddäus!', 15, array([-9.87188541e-05, -1.46103906e-04, -1.24385755e-04, ...,\n",
+      "        1.02667604e-04,  1.97437703e-06, -2.76412793e-05], dtype=float32), 1.6787395833333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_458.wav', 'Das Leben ist voller Ã\\x9cberraschungen.', 37, array([-3.9487539e-05,  1.1846262e-05, -1.3820640e-05, ...,\n",
+      "        6.1205690e-05,  3.1590032e-05,  1.9743770e-06], dtype=float32), 1.9739375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_460.wav', 'Danke der Nachfrage!460', 23, array([ 8.29238343e-05,  1.16488241e-04,  9.67444721e-05, ...,\n",
+      "       -1.12539492e-04, -1.08590735e-04, -1.42155142e-04], dtype=float32), 1.4275833333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_465.wav', 'Ich will auch so ein Pferd.', 27, array([ 0.0000000e+00,  1.9743770e-06, -3.1590032e-05, ...,\n",
+      "        7.8975077e-05, -3.9487539e-05, -5.7256933e-05], dtype=float32), 1.5404791666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_473.wav', 'Was hat das zu bedeuten?', 24, array([-1.26360130e-04, -1.08590735e-04, -1.16488241e-04, ...,\n",
+      "        8.29238343e-05,  2.36925243e-05, -1.57950162e-05], dtype=float32), 1.8241354166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_492.wav', 'Die Narkose wirkt nicht.', 24, array([ 1.7571956e-04,  1.6782204e-04,  7.8975077e-05, ...,\n",
+      "        3.1590032e-05, -2.1718148e-05, -2.7641279e-05], dtype=float32), 1.965125)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_495.wav', 'Dein Bruder ist echt krass drauf.', 33, array([-7.10775712e-05, -3.35644108e-05, -2.17181478e-05, ...,\n",
+      "        1.16488241e-04,  1.02667604e-04,  7.89750775e-05], dtype=float32), 1.9563125)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_498.wav', 'Das behaupten alle.', 19, array([-1.1253949e-04, -1.1846262e-04, -9.8718854e-05, ...,\n",
+      "        5.5282559e-05, -1.1846262e-05,  4.5410670e-05], dtype=float32), 1.3658958333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_500.wav', 'So einfach ist es nicht.', 24, array([-0.00019349, -0.00019744, -0.00022113, ..., -0.00021521,\n",
+      "       -0.0002231 , -0.00020534], dtype=float32), 1.7976979166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_503.wav', 'Das gibt ihm den Rest.', 22, array([-3.94875406e-06, -6.71288217e-05, -1.20436998e-04, ...,\n",
+      "        1.04641986e-04,  1.24385755e-04,  1.14513867e-04], dtype=float32), 1.5068958333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_507.wav', 'Was fällt euch ein?', 20, array([9.8718854e-05, 9.0821341e-05, 6.7128822e-05, ..., 1.7374518e-04,\n",
+      "       2.0730958e-04, 1.5795015e-04], dtype=float32), 1.5421458333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_511.wav', 'Lass die Glucke in Ruhe!', 24, array([2.6851529e-04, 2.3692525e-04, 8.2923834e-05, ..., 9.2795723e-05,\n",
+      "       6.3180065e-05, 6.1205690e-05], dtype=float32), 1.4099583333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_512.wav', 'Wieso denn das nicht?', 21, array([-3.9487541e-06, -2.5666901e-05, -6.9103196e-05, ...,\n",
+      "        3.1590032e-05, -1.9743770e-06,  1.3820640e-05], dtype=float32), 1.4804583333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_542.wav', 'Na gut, ich komme mit.', 22, array([ 6.3180065e-05, -3.9487541e-06,  4.3436296e-05, ...,\n",
+      "       -6.9103196e-05, -6.5154440e-05,  7.8975081e-06], dtype=float32), 1.8329479166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_543.wav', 'Entschuldige dich bei ihr.', 26, array([-9.6744472e-05, -7.8975077e-05, -5.1333802e-05, ...,\n",
+      "       -7.7000703e-05, -1.2241138e-04, -5.9231312e-05], dtype=float32), 1.7624479166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_544.wav', 'Das reimt sich ja.', 18, array([ 1.1056512e-04,  8.4898209e-05,  1.1648824e-04, ...,\n",
+      "       -9.0821341e-05, -1.1451387e-04, -1.1253949e-04], dtype=float32), 1.25134375)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_549.wav', 'Sie kamen, um zu bleiben.', 25, array([ 3.9487539e-05,  7.8975081e-06,  3.3564411e-05, ...,\n",
+      "        2.1718148e-05, -2.7641279e-05, -9.6744472e-05], dtype=float32), 1.8593854166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_555.wav', 'Sie nimmt kein Blatt vor den Mund.', 34, array([-1.1569849e-03, -7.0287823e-04, -5.3308180e-05, ...,\n",
+      "        2.5666901e-05,  1.5795016e-05, -1.9743769e-05], dtype=float32), 1.8021041666666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_556.wav', 'Hoffentlich geht es ihm gut.', 28, array([1.0187785e-03, 1.1372411e-03, 1.2616270e-03, ..., 3.5538786e-05,\n",
+      "       7.8975081e-06, 5.9231312e-05], dtype=float32), 1.9342916666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_557.wav', 'Vergiss deine Schoner nicht!', 28, array([ 3.9487539e-05, -5.5282559e-05, -2.0336083e-04, ...,\n",
+      "       -6.9103196e-05, -7.1077571e-05, -7.1077571e-05], dtype=float32), 1.8461666666666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_590.wav', 'Wenn du schon so fragst!', 24, array([ 1.4610391e-04,  1.4807828e-04,  1.7966831e-04, ...,\n",
+      "        1.7769393e-05, -4.3436296e-05, -2.7641279e-05], dtype=float32), 1.8329479166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_595.wav', 'Was muss ich einkaufen?', 23, array([ 0.00016387,  0.00012636,  0.00011254, ..., -0.00010464,\n",
+      "       -0.00011649, -0.00010464], dtype=float32), 1.6038229166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_599.wav', 'Der tut nichts!', 15, array([ 9.2795723e-05,  6.1205690e-05,  2.5666901e-05, ...,\n",
+      "       -1.1648824e-04, -9.8718854e-05, -7.8975077e-05], dtype=float32), 1.16321875)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_605.wav', 'Natürlich war es das.', 22, array([3.1590032e-05, 1.9743769e-05, 7.8975077e-05, ..., 1.4610391e-04,\n",
+      "       1.6782204e-04, 1.4412952e-04], dtype=float32), 1.5157083333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_612.wav', 'Sprechen Sie deutsch?', 21, array([ 2.05335207e-04,  1.91514569e-04,  1.57950155e-04, ...,\n",
+      "        2.96156559e-05, -6.31800649e-05, -1.02667604e-04], dtype=float32), 1.5157083333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_616.wav', 'Gleich hole ich Anne ab.', 24, array([6.3180065e-05, 7.1077571e-05, 1.2636013e-04, ..., 2.1718148e-05,\n",
+      "       3.1590032e-05, 1.3820640e-05], dtype=float32), 1.4099583333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_646.wav', 'Ich bin ganz hin und weg!', 25, array([-3.5143911e-04, -2.5666901e-04, -1.6979642e-04, ...,\n",
+      "       -4.3436296e-05, -6.1205690e-05,  4.3436296e-05], dtype=float32), 1.7800729166666667)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_647.wav', 'Frische Luft tut gut.', 21, array([ 1.04641986e-04,  1.97437703e-06, -7.89750811e-06, ...,\n",
+      "        8.48982090e-05,  1.38206397e-05, -7.89750811e-06], dtype=float32), 1.6655104166666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_652.wav', 'Nein, du Genie!', 15, array([1.3623202e-04, 1.2833450e-04, 1.2833450e-04, ..., 2.7641279e-05,\n",
+      "       4.5410670e-05, 5.1333802e-05], dtype=float32), 1.5068958333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_675.wav', 'Das kann doch wohl nicht wahr sein!', 35, array([-1.9743769e-05, -3.3564411e-05,  3.1590032e-05, ...,\n",
+      "        9.2795723e-05,  9.6744472e-05,  1.2043700e-04], dtype=float32), 1.9827604166666666)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_684.wav', 'Jedes Kind weiÃ\\x9f das.', 21, array([-1.204370e-04, -1.382064e-04, -9.674447e-05, ..., -1.461039e-04,\n",
+      "       -1.382064e-04, -8.489821e-05], dtype=float32), 1.4716458333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_711.wav', 'Die werden ja nicht schlecht.', 29, array([-1.3030888e-04, -1.0069323e-04, -8.2923834e-05, ...,\n",
+      "       -1.3228325e-04, -1.1253949e-04, -9.6744472e-05], dtype=float32), 1.4716458333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_719.wav', 'Das Leben ist schön!', 21, array([ 2.5666901e-05,  4.7385049e-05,  2.9615656e-05, ...,\n",
+      "       -3.3564411e-05,  3.3564411e-05,  7.8975077e-05], dtype=float32), 1.2953958333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_720.wav', 'Was machst du jetzt?', 20, array([-1.5597578e-04, -1.2833450e-04, -1.3425764e-04, ...,\n",
+      "       -5.3308180e-05, -3.9487541e-06,  3.9487541e-06], dtype=float32), 1.4099583333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_728.wav', 'Falsche Antwort.', 16, array([-5.1333802e-05, -1.1846262e-05,  9.8718847e-06, ...,\n",
+      "       -9.8718847e-06, -4.7385049e-05, -5.3308180e-05], dtype=float32), 1.3923333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_743.wav', 'Oder wir gehen Burger essen.', 28, array([-2.9615656e-05, -4.7385049e-05, -3.1590032e-05, ...,\n",
+      "       -8.2923834e-05, -5.1333802e-05,  6.3180065e-05], dtype=float32), 1.7007604166666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_744.wav', 'Lasst mich allein!', 18, array([-0.00018757, -0.00018757, -0.00024877, ..., -0.00011846,\n",
+      "       -0.00011057, -0.00013031], dtype=float32), 1.7007604166666668)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_745.wav', 'Da sind wir wieder.', 19, array([7.2459638e-04, 7.7395578e-04, 8.3911023e-04, ..., 0.0000000e+00,\n",
+      "       1.3820640e-05, 1.7769393e-05], dtype=float32), 1.4804583333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_754.wav', 'So weit, so gut.', 16, array([-1.7769393e-05, -3.5538786e-05,  3.5538786e-05, ...,\n",
+      "        9.8718847e-06, -5.3308180e-05, -4.3436296e-05], dtype=float32), 1.5245208333333333)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_756.wav', 'Alles war voller Qualm.', 23, array([ 6.3180065e-05,  2.9615656e-05,  3.7513164e-05, ...,\n",
+      "       -3.1590032e-05, -3.3564411e-05,  2.1718148e-05], dtype=float32), 1.5333333333333334)\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_9_FINAL/9_765.wav', 'Fick dich!', 10, array([-3.9487539e-05, -8.0949460e-05, -5.7256933e-05, ...,\n",
+      "        3.9487539e-05,  7.8975077e-05,  9.4770097e-05], dtype=float32), 0.9076666666666666)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print clips shorter than 2 sec\n",
+    "for item in data:\n",
+    "    if item[-1] < 2:\n",
+    "        print(item)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "sec_per_chars = []\n",
+    "for item in data:\n",
+    "    text = item[1]\n",
+    "    dur = item[-1]\n",
+    "    sec_per_char = dur / len(text)\n",
+    "    sec_per_chars.append(sec_per_char)\n",
+    "# sec_per_char /= len(data)\n",
+    "# print(sec_per_char)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " > Average durations per char:  0.07641993439576344\n",
+      " > STD duration per char:  0.015251748851166484\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean = np.mean(sec_per_chars)\n",
+    "std = np.std(sec_per_chars)\n",
+    "print(\" > Average durations per char: \", mean)\n",
+    "print(\" > STD duration per char: \", std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "# fit a distribution\n",
+    "dist = norm(mean, std)\n",
+    "\n",
+    "# find irregular instances long or short voice durations\n",
+    "items =[]\n",
+    "pdfs = []\n",
+    "for item in data:\n",
+    "    text = item[1]\n",
+    "    dur = item[-1]\n",
+    "    sec_per_char = dur / len(text)\n",
+    "    pdf = norm.pdf(sec_per_char)\n",
+    "    pdfs.append(pdf)\n",
+    "    items.append(item)\n",
+    "#     if pdf < 0.395:\n",
+    "#         print(item)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe27ef1d450>]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x1152 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot pdf values too see outliers\n",
+    "plt.figure(figsize=[16,16])\n",
+    "plt.plot(pdfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_11_FINAL/11_227.wav', 'Q-R-S-T-U-V-W-X-Y-Z macht es komplett!', 38, array([-4.0032621e-04, -3.3042193e-04, -3.4537757e-04, ...,\n",
+      "        7.7704317e-06,  2.7401828e-05,  7.1041533e-05], dtype=float32), 11.323739583333333) 0.38161673291429454\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_14_FINAL/14_496.wav', 'Ist der Kuli blau?', 18, array([ 1.2363373e-05, -3.6298752e-05,  2.1456377e-05, ...,\n",
+      "        3.9692618e-06, -6.7328816e-05, -9.5399046e-05], dtype=float32), 5.530666666666667) 0.38054811432758695\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_17_FINAL/17_426.wav', 'H-I-J-K-L-M-N-O-P!', 18, array([ 4.7872534e-05, -3.4164757e-05, -2.1835160e-04, ...,\n",
+      "       -4.3899294e-05, -7.5021897e-05, -3.4489829e-05], dtype=float32), 11.167979166666667) 0.32909346861901806\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_119.wav', 'Kann ich mich irgendwie revanchieren?', 37, array([-5.1586820e-05, -9.1837741e-05, -9.9342957e-05, ...,\n",
+      "       -1.4234778e-04, -1.2327779e-04, -1.4810068e-04], dtype=float32), 9.728) 0.3853891360487213\n",
+      "('/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_5_FINAL/5_41.wav', 'Ja, eben.', 9, array([ 8.6438486e-05,  1.5554321e-04,  1.1511238e-04, ...,\n",
+      "       -1.3761004e-05, -2.3534812e-05, -5.6318945e-06], dtype=float32), 2.1033333333333335) 0.38819509492217963\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print outliers\n",
+    "threshold = 0.39\n",
+    "for item, pdf in zip(items, pdfs):\n",
+    "    if pdf < threshold:\n",
+    "        print(item, pdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source 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\" type=\"audio/x-wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Audio\n",
+    "Audio(\"/home/erogol/Data/Mozilla_DE_Thomas3/BATCH_2_FINAL/2_119.wav\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "### Plot Dataset Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fe27e668c10>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"text length vs mean audio duration\")\n",
+    "plt.scatter(list(text_vs_avg.keys()), list(text_vs_avg.values()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fe27f555510>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"text length vs median audio duration\")\n",
+    "plt.scatter(list(text_vs_median.keys()), list(text_vs_median.values()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fe27f436d90>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"text length vs STD\")\n",
+    "plt.scatter(list(text_vs_std.keys()), list(text_vs_std.values()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fe27f2fc110>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"text length vs # instances\")\n",
+    "plt.scatter(list(text_len_counter.keys()), list(text_len_counter.values()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "source": [
+    "### Check words frequencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "w_count_df = pd.DataFrame.from_dict(w_count, orient='index')\n",
+    "w_count_df.sort_values(0, ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "Collapsed": "false",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>die</th>\n",
+       "      <td>3066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>der</th>\n",
+       "      <td>2362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>das</th>\n",
+       "      <td>1794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ist</th>\n",
+       "      <td>1767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nicht</th>\n",
+       "      <td>1467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wertvollsten,</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blutgruppe</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gelenkschmerzen</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entgeltbefreiung</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>anrã¼cken.</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>27102 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     0\n",
+       "die               3066\n",
+       "der               2362\n",
+       "das               1794\n",
+       "ist               1767\n",
+       "nicht             1467\n",
+       "...                ...\n",
+       "wertvollsten,        1\n",
+       "blutgruppe           1\n",
+       "gelenkschmerzen      1\n",
+       "entgeltbefreiung     1\n",
+       "anrã¼cken.           1\n",
+       "\n",
+       "[27102 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_count_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check a certain word\n",
+    "w_count_df.at['auto', 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7652b5aa10>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/erogol/miniconda3/lib/python3.7/site-packages/matplotlib-3.2.0rc3-py3.7-linux-x86_64.egg/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 159 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "/home/erogol/miniconda3/lib/python3.7/site-packages/matplotlib-3.2.0rc3-py3.7-linux-x86_64.egg/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 156 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "/home/erogol/miniconda3/lib/python3.7/site-packages/matplotlib-3.2.0rc3-py3.7-linux-x86_64.egg/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 159 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "/home/erogol/miniconda3/lib/python3.7/site-packages/matplotlib-3.2.0rc3-py3.7-linux-x86_64.egg/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 156 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fequency bar plot - it takes time!!\n",
+    "w_count_df.plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/dataset_analysis/AnalyzeDataset.ipynb b/notebooks/dataset_analysis/AnalyzeDataset.ipynb
similarity index 99%
rename from dataset_analysis/AnalyzeDataset.ipynb
rename to notebooks/dataset_analysis/AnalyzeDataset.ipynb
index 3ed54ded..62870fdc 100644
--- a/dataset_analysis/AnalyzeDataset.ipynb
+++ b/notebooks/dataset_analysis/AnalyzeDataset.ipynb
@@ -27,7 +27,7 @@
     "from multiprocessing import Pool\n",
     "from matplotlib import pylab as plt\n",
     "from collections import Counter\n",
-    "from TTS.datasets.preprocess import *\n",
+    "from TTS.tts.datasets.preprocess import *\n",
     "%matplotlib inline"
    ]
   },
diff --git a/dataset_analysis/CheckDatasetSNR.ipynb b/notebooks/dataset_analysis/CheckDatasetSNR.ipynb
similarity index 100%
rename from dataset_analysis/CheckDatasetSNR.ipynb
rename to notebooks/dataset_analysis/CheckDatasetSNR.ipynb
diff --git a/dataset_analysis/README.md b/notebooks/dataset_analysis/README.md
similarity index 100%
rename from dataset_analysis/README.md
rename to notebooks/dataset_analysis/README.md
diff --git a/dataset_analysis/analyze.py b/notebooks/dataset_analysis/analyze.py
similarity index 100%
rename from dataset_analysis/analyze.py
rename to notebooks/dataset_analysis/analyze.py
diff --git a/run_tests.sh b/run_tests.sh
new file mode 100755
index 00000000..e5e46476
--- /dev/null
+++ b/run_tests.sh
@@ -0,0 +1,10 @@
+# tests
+nosetests tests -x
+
+# runtime tests
+./tests/test_server_package.sh
+./tests/test_tts_train.sh
+./tests/test_vocoder_train.sh
+
+# linter check
+cardboardlinter --refspec master
\ No newline at end of file
diff --git a/server/README.md b/server/README.md
deleted file mode 100644
index 3c65c961..00000000
--- a/server/README.md
+++ /dev/null
@@ -1,47 +0,0 @@
-## TTS example web-server
-
-You'll need a model package (Zip file, includes TTS Python wheel, model files, server configuration, and optional nginx/uwsgi configs). Publicly available models are listed [here](https://github.com/mozilla/TTS/wiki/Released-Models#simple-packaging---self-contained-package-that-runs-an-http-api-for-a-pre-trained-tts-model).
-
-Instructions below are based on a Ubuntu 18.04 machine, but it should be simple to adapt the package names to other distros if needed. Python 3.6 is recommended, as some of the dependencies' versions predate Python 3.7 and will force building from source, which requires extra dependencies and is not guaranteed to work.
-
-#### Development server:
-
-##### Using server.py
-If you have the environment set already for TTS, then you can directly call ```server.py```.
-
-##### Using .whl
-1. apt-get install -y espeak libsndfile1 python3-venv
-2. python3 -m venv /tmp/venv
-3. source /tmp/venv/bin/activate
-4. pip install -U pip setuptools wheel
-5. pip install -U https//example.com/url/to/python/package.whl
-6. python -m TTS.server.server
-
-You can now open http://localhost:5002 in a browser
-
-#### Running with nginx/uwsgi:
-
-1. apt-get install -y uwsgi uwsgi-plugin-python3 nginx espeak libsndfile1 python3-venv
-2. python3 -m venv /tmp/venv
-3. source /tmp/venv/bin/activate
-4. pip install -U pip setuptools wheel
-5. pip install -U https//example.com/url/to/python/package.whl
-6. curl -LO https://github.com/reuben/TTS/releases/download/t2-ljspeech-mold/t2-ljspeech-mold-nginx-uwsgi.zip
-7. unzip *-nginx-uwsgi.zip
-8. cp tts_site_nginx /etc/nginx/sites-enabled/default
-9. service nginx restart
-10. uwsgi --ini uwsgi.ini
-
-You can now open http://localhost:80 in a browser (edit the port in /etc/nginx/sites-enabled/tts_site_nginx).
-Configure number of workers (number of requests that will be processed in parallel) by editing the `uwsgi.ini` file, specifically the `processes` setting.
-
-#### Creating a server package with an embedded model
-
-[setup.py](../setup.py) was extended with two new parameters when running the `bdist_wheel` command:
-
-- `--checkpoint <path to checkpoint file>` - path to model checkpoint file you want to embed in the package
-- `--model_config <path to config.json file>` - path to corresponding config.json file for the checkpoint
-
-To create a package, run `python setup.py bdist_wheel --checkpoint /path/to/checkpoint --model_config /path/to/config.json`.
-
-A Python `.whl` file will be created in the `dist/` folder with the checkpoint and config embedded in it.
diff --git a/server/__init__.py b/server/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/server/conf.json b/server/conf.json
deleted file mode 100644
index 00045365..00000000
--- a/server/conf.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-    "tts_path":"/media/erogol/data_ssd/Models/libri_tts/5049/",  // tts model root folder
-    "tts_file":"best_model.pth.tar",     // tts checkpoint file
-    "tts_config":"config.json",     // tts config.json file
-    "tts_speakers": null,           // json file listing speaker ids. null if no speaker embedding.
-    "vocoder_config":null,
-    "vocoder_file": null,
-    "wavernn_lib_path": null,   // Rootpath to wavernn project folder to be imported. If this is null, model uses GL for speech synthesis.
-    "wavernn_path":null,  // wavernn model root path
-    "wavernn_file":null, // wavernn checkpoint file name
-    "wavernn_config": null, // wavernn config file
-    "is_wavernn_batched":true,
-    "port": 5002,
-    "use_cuda": true,
-    "debug": true
-}
diff --git a/server/server.py b/server/server.py
deleted file mode 100644
index bd23ea9c..00000000
--- a/server/server.py
+++ /dev/null
@@ -1,86 +0,0 @@
-#!flask/bin/python
-import argparse
-import os
-
-from flask import Flask, request, render_template, send_file
-from TTS.server.synthesizer import Synthesizer
-
-
-def create_argparser():
-    def convert_boolean(x):
-        return x.lower() in ['true', '1', 'yes']
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--tts_checkpoint', type=str, help='path to TTS checkpoint file')
-    parser.add_argument('--tts_config', type=str, help='path to TTS config.json file')
-    parser.add_argument('--tts_speakers', type=str, help='path to JSON file containing speaker ids, if speaker ids are used in the model')
-    parser.add_argument('--wavernn_lib_path', type=str, default=None, help='path to WaveRNN project folder to be imported. If this is not passed, model uses Griffin-Lim for synthesis.')
-    parser.add_argument('--wavernn_checkpoint', type=str, default=None, help='path to WaveRNN checkpoint file.')
-    parser.add_argument('--wavernn_config', type=str, default=None, help='path to WaveRNN config file.')
-    parser.add_argument('--is_wavernn_batched', type=convert_boolean, default=False, help='true to use batched WaveRNN.')
-    parser.add_argument('--vocoder_config', type=str, default=None, help='path to TTS.vocoder config file.')
-    parser.add_argument('--vocoder_checkpoint', type=str, default=None, help='path to TTS.vocoder checkpoint file.')
-    parser.add_argument('--port', type=int, default=5002, help='port to listen on.')
-    parser.add_argument('--use_cuda', type=convert_boolean, default=False, help='true to use CUDA.')
-    parser.add_argument('--debug', type=convert_boolean, default=False, help='true to enable Flask debug mode.')
-    return parser
-
-
-synthesizer = None
-
-embedded_models_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'model')
-
-embedded_tts_folder = os.path.join(embedded_models_folder, 'tts')
-tts_checkpoint_file = os.path.join(embedded_tts_folder, 'checkpoint.pth.tar')
-tts_config_file = os.path.join(embedded_tts_folder, 'config.json')
-
-embedded_vocoder_folder = os.path.join(embedded_models_folder, 'vocoder')
-vocoder_checkpoint_file = os.path.join(embedded_vocoder_folder, 'checkpoint.pth.tar')
-vocoder_config_file = os.path.join(embedded_vocoder_folder, 'config.json')
-
-# These models are soon to be deprecated
-embedded_wavernn_folder = os.path.join(embedded_models_folder, 'wavernn')
-wavernn_checkpoint_file = os.path.join(embedded_wavernn_folder, 'checkpoint.pth.tar')
-wavernn_config_file = os.path.join(embedded_wavernn_folder, 'config.json')
-
-args = create_argparser().parse_args()
-
-# If these were not specified in the CLI args, use default values with embedded model files
-if not args.tts_checkpoint and os.path.isfile(tts_checkpoint_file):
-    args.tts_checkpoint = tts_checkpoint_file
-if not args.tts_config and os.path.isfile(tts_config_file):
-    args.tts_config = tts_config_file
-
-if not args.vocoder_checkpoint and os.path.isfile(vocoder_checkpoint_file):
-    args.vocoder_checkpoint = vocoder_checkpoint_file
-if not args.vocoder_config and os.path.isfile(vocoder_config_file):
-    args.vocoder_config = vocoder_config_file
-
-if not args.wavernn_checkpoint and os.path.isfile(wavernn_checkpoint_file):
-    args.wavernn_checkpoint = wavernn_checkpoint_file
-if not args.wavernn_config and os.path.isfile(wavernn_config_file):
-    args.wavernn_config = wavernn_config_file
-
-synthesizer = Synthesizer(args)
-
-app = Flask(__name__)
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-
-@app.route('/api/tts', methods=['GET'])
-def tts():
-    text = request.args.get('text')
-    print(" > Model input: {}".format(text))
-    data = synthesizer.tts(text)
-    return send_file(data, mimetype='audio/wav')
-
-
-def main():
-    app.run(debug=args.debug, host='0.0.0.0', port=args.port)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/server/synthesizer.py b/server/synthesizer.py
deleted file mode 100644
index 0f743d87..00000000
--- a/server/synthesizer.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import io
-import sys
-import time
-
-import numpy as np
-import torch
-import yaml
-import pysbd
-
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.io import load_config
-from TTS.utils.generic_utils import setup_model
-from TTS.utils.speakers import load_speaker_mapping
-from TTS.vocoder.utils.generic_utils import setup_generator
-# pylint: disable=unused-wildcard-import
-# pylint: disable=wildcard-import
-from TTS.utils.synthesis import *
-
-from TTS.utils.text import make_symbols, phonemes, symbols
-
-
-class Synthesizer(object):
-    def __init__(self, config):
-        self.wavernn = None
-        self.vocoder_model = None
-        self.config = config
-        print(config)
-        self.seg = self.get_segmenter("en")
-        self.use_cuda = self.config.use_cuda
-        if self.use_cuda:
-            assert torch.cuda.is_available(), "CUDA is not availabe on this machine."
-        self.load_tts(self.config.tts_checkpoint, self.config.tts_config,
-                      self.config.use_cuda)
-        if self.config.vocoder_checkpoint:
-            self.load_vocoder(self.config.vocoder_checkpoint, self.config.vocoder_config, self.config.use_cuda)
-        if self.config.wavernn_lib_path:
-            self.load_wavernn(self.config.wavernn_lib_path, self.config.wavernn_checkpoint,
-                              self.config.wavernn_config, self.config.use_cuda)
-
-    @staticmethod
-    def get_segmenter(lang):
-        return pysbd.Segmenter(language=lang, clean=True)
-
-    def load_tts(self, tts_checkpoint, tts_config, use_cuda):
-        # pylint: disable=global-statement
-        global symbols, phonemes
-
-        print(" > Loading TTS model ...")
-        print(" | > model config: ", tts_config)
-        print(" | > checkpoint file: ", tts_checkpoint)
-
-        self.tts_config = load_config(tts_config)
-        self.use_phonemes = self.tts_config.use_phonemes
-        self.ap = AudioProcessor(**self.tts_config.audio)
-
-        if 'characters' in self.tts_config.keys():
-            symbols, phonemes = make_symbols(**self.tts_config.characters)
-
-        if self.use_phonemes:
-            self.input_size = len(phonemes)
-        else:
-            self.input_size = len(symbols)
-        # TODO: fix this for multi-speaker model - load speakers
-        if self.config.tts_speakers is not None:
-            self.tts_speakers = load_speaker_mapping(self.config.tts_speakers)
-            num_speakers = len(self.tts_speakers)
-        else:
-            num_speakers = 0
-        self.tts_model = setup_model(self.input_size, num_speakers=num_speakers, c=self.tts_config)
-        # load model state
-        cp = torch.load(tts_checkpoint, map_location=torch.device('cpu'))
-        # load the model
-        self.tts_model.load_state_dict(cp['model'])
-        if use_cuda:
-            self.tts_model.cuda()
-        self.tts_model.eval()
-        self.tts_model.decoder.max_decoder_steps = 3000
-        if 'r' in cp:
-            self.tts_model.decoder.set_r(cp['r'])
-            print(f" > model reduction factor: {cp['r']}")
-
-    def load_vocoder(self, model_file, model_config, use_cuda):
-        self.vocoder_config = load_config(model_config)
-        self.vocoder_model = setup_generator(self.vocoder_config)
-        self.vocoder_model.load_state_dict(torch.load(model_file, map_location="cpu")["model"])
-        self.vocoder_model.remove_weight_norm()
-        self.vocoder_model.inference_padding = 0
-        self.vocoder_config = load_config(model_config)
-
-        if use_cuda:
-            self.vocoder_model.cuda()
-        self.vocoder_model.eval()
-
-    def load_wavernn(self, lib_path, model_file, model_config, use_cuda):
-        # TODO: set a function in wavernn code base for model setup and call it here.
-        sys.path.append(lib_path) # set this if WaveRNN is not installed globally
-        #pylint: disable=import-outside-toplevel
-        from WaveRNN.models.wavernn import Model
-        print(" > Loading WaveRNN model ...")
-        print(" | > model config: ", model_config)
-        print(" | > model file: ", model_file)
-        self.wavernn_config = load_config(model_config)
-        # This is the default architecture we use for our models.
-        # You might need to update it
-        self.wavernn = Model(
-            rnn_dims=512,
-            fc_dims=512,
-            mode=self.wavernn_config.mode,
-            mulaw=self.wavernn_config.mulaw,
-            pad=self.wavernn_config.pad,
-            use_aux_net=self.wavernn_config.use_aux_net,
-            use_upsample_net=self.wavernn_config.use_upsample_net,
-            upsample_factors=self.wavernn_config.upsample_factors,
-            feat_dims=80,
-            compute_dims=128,
-            res_out_dims=128,
-            res_blocks=10,
-            hop_length=self.ap.hop_length,
-            sample_rate=self.ap.sample_rate,
-        ).cuda()
-
-        check = torch.load(model_file, map_location="cpu")
-        self.wavernn.load_state_dict(check['model'])
-        if use_cuda:
-            self.wavernn.cuda()
-        self.wavernn.eval()
-
-    def save_wav(self, wav, path):
-        # wav *= 32767 / max(1e-8, np.max(np.abs(wav)))
-        wav = np.array(wav)
-        self.ap.save_wav(wav, path)
-
-    def split_into_sentences(self, text):
-        return self.seg.segment(text)
-
-    def tts(self, text, speaker_id=None):
-        start_time = time.time()
-        wavs = []
-        sens = self.split_into_sentences(text)
-        print(sens)
-        speaker_id = id_to_torch(speaker_id)
-        if speaker_id is not None and self.use_cuda:
-            speaker_id = speaker_id.cuda()
-
-        for sen in sens:
-            # preprocess the given text
-            inputs = text_to_seqvec(sen, self.tts_config)
-            inputs = numpy_to_torch(inputs, torch.long, cuda=self.use_cuda)
-            inputs = inputs.unsqueeze(0)
-            # synthesize voice
-            _, postnet_output, _, _ = run_model_torch(self.tts_model, inputs, self.tts_config, False, speaker_id, None)
-            if self.vocoder_model:
-                # use native vocoder model
-                vocoder_input = postnet_output[0].transpose(0, 1).unsqueeze(0)
-                wav = self.vocoder_model.inference(vocoder_input)
-                if self.use_cuda:
-                    wav = wav.cpu().numpy()
-                else:
-                    wav = wav.numpy()
-                wav = wav.flatten()
-            elif self.wavernn:
-                # use 3rd paty wavernn
-                vocoder_input = None
-                if self.tts_config.model == "Tacotron":
-                    vocoder_input = torch.FloatTensor(self.ap.out_linear_to_mel(linear_spec=postnet_output.T).T).T.unsqueeze(0)
-                else:
-                    vocoder_input = postnet_output[0].transpose(0, 1).unsqueeze(0)
-                if self.use_cuda:
-                    vocoder_input.cuda()
-                wav = self.wavernn.generate(vocoder_input, batched=self.config.is_wavernn_batched, target=11000, overlap=550)
-            else:
-                # use GL
-                if self.use_cuda:
-                    postnet_output = postnet_output[0].cpu()
-                else:
-                    postnet_output = postnet_output[0]
-                postnet_output = postnet_output.numpy()
-                wav = inv_spectrogram(postnet_output, self.ap, self.tts_config)
-
-            # trim silence
-            wav = trim_silence(wav, self.ap)
-
-            wavs += list(wav)
-            wavs += [0] * 10000
-
-        out = io.BytesIO()
-        self.save_wav(wavs, out)
-
-        # compute stats
-        process_time = time.time() - start_time
-        audio_time = len(wavs) / self.tts_config.audio['sample_rate']
-        print(f" > Processing time: {process_time}")
-        print(f" > Real-time factor: {process_time / audio_time}")
-        return out
diff --git a/server/templates/index.html b/server/templates/index.html
deleted file mode 100644
index 45b874a9..00000000
--- a/server/templates/index.html
+++ /dev/null
@@ -1,111 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-  <head>
-
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-    <meta name="description" content="">
-    <meta name="author" content="">
-
-    <title>Mozilla - Text2Speech engine</title>
-
-    <!-- Bootstrap core CSS -->
-    <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.1/css/bootstrap.min.css"
-     integrity="sha384-WskhaSGFgHYWDcbwN70/dfYBj47jz9qbsMId/iRN3ewGhXQFZCSftd1LZCfmhktB" crossorigin="anonymous" rel="stylesheet">
-
-    <!-- Custom styles for this template -->
-    <style>
-      body {
-        padding-top: 54px;
-      }
-      @media (min-width: 992px) {
-        body {
-          padding-top: 56px;
-        }
-      }
-
-    </style>
-  </head>
-
-  <body>
-    <a href="https://github.com/mozilla/TTS"><img style="position: absolute; z-index:1000; top: 0; left: 0; border: 0;" src="https://s3.amazonaws.com/github/ribbons/forkme_left_darkblue_121621.png" alt="Fork me on GitHub"></a>
-
-    <!-- Navigation -->
-    <!--
-    <nav class="navbar navbar-expand-lg navbar-dark bg-dark fixed-top">
-      <div class="container">
-        <a class="navbar-brand" href="#">Mozilla TTS</a>
-        <button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarResponsive" aria-controls="navbarResponsive" aria-expanded="false" aria-label="Toggle navigation">
-          <span class="navbar-toggler-icon"></span>
-        </button>
-        <div class="collapse navbar-collapse" id="navbarResponsive">
-          <ul class="navbar-nav ml-auto">
-            <li class="nav-item active">
-              <a class="nav-link" href="#">Home
-                <span class="sr-only">(current)</span>
-              </a>
-            </li>
-          </ul>
-        </div>
-      </div>
-    </nav>
-    -->
-
-    <!-- Page Content -->
-    <div class="container">
-      <div class="row">
-        <div class="col-lg-12 text-center">
-          <img class="mt-5" src="https://user-images.githubusercontent.com/1402048/52643646-c2102980-2edd-11e9-8c37-b72f3c89a640.png" alt=></img>
-          <h1 class="mt-5">Mozilla TTS</h1>
-          <ul class="list-unstyled">
-          </ul>
-          <input id="text" placeholder="Type here..." size=45 type="text" name="text">
-          <button id="speak-button" name="speak">Speak</button><br/><br/>
-          <audio id="audio" controls autoplay hidden></audio>
-          <p id="message"></p>
-        </div>
-      </div>
-    </div>
-
-    <!-- Bootstrap core JavaScript -->
-    <script>
-            function q(selector) {return document.querySelector(selector)}
-            q('#text').focus()
-            function do_tts(e) {
-                text = q('#text').value
-                if (text) {
-                    q('#message').textContent = 'Synthesizing...'
-                    q('#speak-button').disabled = true
-                    q('#audio').hidden = true
-                    synthesize(text)
-                }
-                e.preventDefault()
-                return false
-            }
-            q('#speak-button').addEventListener('click', do_tts)
-            q('#text').addEventListener('keyup', function(e) {
-              if (e.keyCode == 13) { // enter
-                do_tts(e)
-              }
-            })
-            function synthesize(text) {
-                fetch('/api/tts?text=' + encodeURIComponent(text), {cache: 'no-cache'})
-                    .then(function(res) {
-                        if (!res.ok) throw Error(res.statusText)
-                            return res.blob()
-                        }).then(function(blob) {
-                            q('#message').textContent = ''
-                            q('#speak-button').disabled = false
-                            q('#audio').src = URL.createObjectURL(blob)
-                            q('#audio').hidden = false
-                        }).catch(function(err) {
-                            q('#message').textContent = 'Error: ' + err.message
-                            q('#speak-button').disabled = false
-                        })
-            }
-        </script>
-
-  </body>
-
-</html>
diff --git a/setup.py b/setup.py
index 3f02dd09..260aa20f 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ args, unknown_args = parser.parse_known_args()
 # Remove our arguments from argv so that setuptools doesn't see them
 sys.argv = [sys.argv[0]] + unknown_args
 
-version = '0.0.3'
+version = '0.0.4'
 
 # Adapted from https://github.com/pytorch/pytorch
 cwd = os.path.dirname(os.path.abspath(__file__))
@@ -112,6 +112,8 @@ setup(
     name='TTS',
     version=version,
     url='https://github.com/mozilla/TTS',
+    author='Eren Gölge',
+    author_email='egolge@mozilla.com',
     description='Text to Speech with Deep Learning',
     license='MPL-2.0',
     entry_points={
@@ -119,11 +121,7 @@ setup(
             'tts-server = TTS.server.server:main'
         ]
     },
-    package_dir={'': 'tts_namespace'},
-    packages=find_packages('tts_namespace'),
-    package_data={
-        'TTS': package_data,
-    },
+    packages=find_packages(include=['TTS*']),
     project_urls={
         'Documentation': 'https://github.com/mozilla/TTS/wiki',
         'Tracker': 'https://github.com/mozilla/TTS/issues',
diff --git a/speaker_encoder/README.md b/speaker_encoder/README.md
deleted file mode 100644
index b6f541f8..00000000
--- a/speaker_encoder/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### Speaker Encoder
-
-This is an implementation of https://arxiv.org/abs/1710.10467. This model can be used for voice and speaker embedding.
-
-With the code here you can generate d-vectors for both multi-speaker and single-speaker TTS datasets, then visualise and explore them along with the associated audio files in an interactive chart.
-
-Below is an example showing embedding results of various speakers. You can generate the same plot with the provided notebook as demonstrated in [this video](https://youtu.be/KW3oO7JVa7Q).
-
-![](umap.png)
-
-Download a pretrained model from [Released Models](https://github.com/mozilla/TTS/wiki/Released-Models) page.
-
-To run the code, you need to follow the same flow as in TTS.
-
-- Define 'config.json' for your needs. Note that, audio parameters should match your TTS model.
-- Example training call ```python speaker_encoder/train.py --config_path speaker_encoder/config.json --data_path ~/Data/Libri-TTS/train-clean-360```
-- Generate embedding vectors ```python speaker_encoder/compute_embeddings.py --use_cuda true /model/path/best_model.pth.tar model/config/path/config.json dataset/path/ output_path``` . This code parses all .wav files at the given dataset path and generates the same folder structure under the output path with the generated embedding files.
-- Watch training on Tensorboard as in TTS
diff --git a/speaker_encoder/__init__.py b/speaker_encoder/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/speaker_encoder/compute_embeddings.py b/speaker_encoder/compute_embeddings.py
deleted file mode 100644
index bfa377e3..00000000
--- a/speaker_encoder/compute_embeddings.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import argparse
-import glob
-import os
-
-import numpy as np
-from tqdm import tqdm
-
-import torch
-from TTS.speaker_encoder.model import SpeakerEncoder
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.generic_utils import load_config
-
-parser = argparse.ArgumentParser(
-    description='Compute embedding vectors for each wav file in a dataset. ')
-parser.add_argument(
-    'model_path',
-    type=str,
-    help='Path to model outputs (checkpoint, tensorboard etc.).')
-parser.add_argument(
-    'config_path',
-    type=str,
-    help='Path to config file for training.',
-)
-parser.add_argument(
-    'data_path',
-    type=str,
-    help='Data path for wav files - directory or CSV file')
-parser.add_argument(
-    'output_path',
-    type=str,
-    help='path for training outputs.')
-parser.add_argument(
-    '--use_cuda', type=bool, help='flag to set cuda.', default=False
-)
-parser.add_argument(
-    '--separator', type=str, help='Separator used in file if CSV is passed for data_path', default='|'
-)
-args = parser.parse_args()
-
-
-c = load_config(args.config_path)
-ap = AudioProcessor(**c['audio'])
-
-data_path = args.data_path
-split_ext = os.path.splitext(data_path)
-sep = args.separator
-
-if len(split_ext) > 0 and split_ext[1].lower() == '.csv':
-    # Parse CSV
-    print(f'CSV file: {data_path}')
-    with open(data_path) as f:
-        wav_path = os.path.join(os.path.dirname(data_path), 'wavs')
-        wav_files = []
-        print(f'Separator is: {sep}')
-        for line in f:
-            components = line.split(sep)
-            if len(components) != 2:
-                print("Invalid line")
-                continue
-            wav_file = os.path.join(wav_path, components[0] + '.wav')
-            #print(f'wav_file: {wav_file}')
-            if os.path.exists(wav_file):
-                wav_files.append(wav_file)
-    print(f'Count of wavs imported: {len(wav_files)}')
-else:
-    # Parse all wav files in data_path
-    wav_path = data_path
-    wav_files = glob.glob(data_path + '/**/*.wav', recursive=True)
-
-output_files = [wav_file.replace(wav_path, args.output_path).replace(
-    '.wav', '.npy') for wav_file in wav_files]
-
-for output_file in output_files:
-    os.makedirs(os.path.dirname(output_file), exist_ok=True)
-
-model = SpeakerEncoder(**c.model)
-model.load_state_dict(torch.load(args.model_path)['model'])
-model.eval()
-if args.use_cuda:
-    model.cuda()
-
-for idx, wav_file in enumerate(tqdm(wav_files)):
-    mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T
-    mel_spec = torch.FloatTensor(mel_spec[None, :, :])
-    if args.use_cuda:
-        mel_spec = mel_spec.cuda()
-    embedd = model.compute_embedding(mel_spec)
-    np.save(output_files[idx], embedd.detach().cpu().numpy())
diff --git a/speaker_encoder/config.json b/speaker_encoder/config.json
deleted file mode 100644
index 0d0f8f68..00000000
--- a/speaker_encoder/config.json
+++ /dev/null
@@ -1,59 +0,0 @@
-{
-    "run_name": "libritts_360-half",
-    "run_description": "train speaker encoder for libritts 360",
-    "audio": {
-        // Audio processing parameters
-        "num_mels": 40, // size of the mel spec frame. 
-        "num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "frame_length_ms": 50, // stft window length in ms.
-        "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
-        "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "min_level_db": -100, // normalization range
-        "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
-        // Normalization parameters
-        "signal_norm": true, // normalize the spec values in range [0, 1]
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true, // clip normalized values into the range.
-        "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
-        "do_trim_silence": false // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-    },
-    "reinit_layers": [],
-    "grad_clip": 3.0, // upper limit for gradients for clipping.
-    "epochs": 1000, // total number of epochs to train.
-    "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
-    "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
-    "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
-    "steps_plot_stats": 10, // number of steps to plot embeddings.
-    "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-    "wd": 0.000001, // Weight decay weight.
-    "checkpoint": true, // If true, it saves checkpoints per "save_step"
-    "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
-    "print_step": 1, // Number of steps to log traning on console.
-    "output_path": "/media/erogol/data_ssd/Models/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
-    "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
-    "model": {
-        "input_dim": 40,
-        "proj_dim": 128,
-        "lstm_dim": 384,
-        "num_lstm_layers": 3
-    },
-    "datasets": 
-        [
-            {
-                "name": "libri_tts",
-                "path": "/home/erogol/Data/Libri-TTS/train-clean-360/",
-                "meta_file_train": null,
-                "meta_file_val": null
-            },
-            {
-                "name": "libri_tts",
-                "path": "/home/erogol/Data/Libri-TTS/train-clean-100/",
-                "meta_file_train": null,
-                "meta_file_val": null
-            }
-        ]
-}
\ No newline at end of file
diff --git a/speaker_encoder/dataset.py b/speaker_encoder/dataset.py
deleted file mode 100644
index 913b7a6d..00000000
--- a/speaker_encoder/dataset.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import numpy as np
-import torch
-import random
-from torch.utils.data import Dataset
-
-
-class MyDataset(Dataset):
-    def __init__(self, ap, meta_data, voice_len=1.6, num_speakers_in_batch=64,
-                 num_utter_per_speaker=10, skip_speakers=False, verbose=False):
-        """
-        Args:
-            ap (TTS.utils.AudioProcessor): audio processor object.
-            meta_data (list): list of dataset instances.
-            seq_len (int): voice segment length in seconds.
-            verbose (bool): print diagnostic information.
-        """
-        self.items = meta_data
-        self.sample_rate = ap.sample_rate
-        self.voice_len = voice_len
-        self.seq_len = int(voice_len * self.sample_rate)
-        self.num_speakers_in_batch = num_speakers_in_batch
-        self.num_utter_per_speaker = num_utter_per_speaker
-        self.skip_speakers = skip_speakers
-        self.ap = ap
-        self.verbose = verbose
-        self.__parse_items()
-        if self.verbose:
-            print("\n > DataLoader initialization")
-            print(f" | > Number of instances : {len(self.items)}")
-            print(f" | > Sequence length: {self.seq_len}")
-            print(f" | > Num speakers: {len(self.speakers)}")
-
-    def load_wav(self, filename):
-        audio = self.ap.load_wav(filename)
-        return audio
-
-    def load_data(self, idx):
-        text, wav_file, speaker_name = self.items[idx]
-        wav = np.asarray(self.load_wav(wav_file), dtype=np.float32)
-        mel = self.ap.melspectrogram(wav).astype("float32")
-        # sample seq_len
-
-        assert text.size > 0, self.items[idx][1]
-        assert wav.size > 0, self.items[idx][1]
-
-        sample = {
-            "mel": mel,
-            "item_idx": self.items[idx][1],
-            "speaker_name": speaker_name,
-        }
-        return sample
-
-    def __parse_items(self):
-        """
-        Find unique speaker ids and create a dict mapping utterances from speaker id
-        """
-        speakers = list({item[-1] for item in self.items})
-        self.speaker_to_utters = {}
-        self.speakers = []
-        for speaker in speakers:
-            speaker_utters = [item[1] for item in self.items if item[2] == speaker]
-            if len(speaker_utters) < self.num_utter_per_speaker and self.skip_speakers:
-                print(
-                    f" [!] Skipped speaker {speaker}. Not enough utterances {self.num_utter_per_speaker} vs {len(speaker_utters)}."
-                )
-            else:
-                self.speakers.append(speaker)
-                self.speaker_to_utters[speaker] = speaker_utters
-
-    def __len__(self):
-        return int(1e10)
-
-    def __sample_speaker(self):
-        speaker = random.sample(self.speakers, 1)[0]
-        if self.num_utter_per_speaker > len(self.speaker_to_utters[speaker]):
-            utters = random.choices(
-                self.speaker_to_utters[speaker], k=self.num_utter_per_speaker
-            )
-        else:
-            utters = random.sample(
-                self.speaker_to_utters[speaker], self.num_utter_per_speaker
-            )
-        return speaker, utters
-
-    def __sample_speaker_utterances(self, speaker):
-        """
-        Sample all M utterances for the given speaker.
-        """
-        feats = []
-        labels = []
-        for _ in range(self.num_utter_per_speaker):
-            # TODO:dummy but works
-            while True:
-                if len(self.speaker_to_utters[speaker]) > 0:
-                    utter = random.sample(self.speaker_to_utters[speaker], 1)[0]
-                else:
-                    self.speakers.remove(speaker)
-                    speaker, _ = self.__sample_speaker()
-                    continue
-                wav = self.load_wav(utter)
-                if wav.shape[0] - self.seq_len > 0:
-                    break
-                self.speaker_to_utters[speaker].remove(utter)
-
-            offset = random.randint(0, wav.shape[0] - self.seq_len)
-            mel = self.ap.melspectrogram(wav[offset : offset + self.seq_len])
-            feats.append(torch.FloatTensor(mel))
-            labels.append(speaker)
-        return feats, labels
-
-    def __getitem__(self, idx):
-        speaker, _ = self.__sample_speaker()
-        return speaker
-
-    def collate_fn(self, batch):
-        labels = []
-        feats = []
-        for speaker in batch:
-            feats_, labels_ = self.__sample_speaker_utterances(speaker)
-            labels.append(labels_)
-            feats.extend(feats_)
-        feats = torch.stack(feats)
-        return feats.transpose(1, 2), labels
diff --git a/speaker_encoder/generic_utils.py b/speaker_encoder/generic_utils.py
deleted file mode 100644
index c568d129..00000000
--- a/speaker_encoder/generic_utils.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import os
-import datetime
-import torch
-
-
-def save_checkpoint(model, optimizer, model_loss, out_path,
-                    current_step, epoch):
-    checkpoint_path = 'checkpoint_{}.pth.tar'.format(current_step)
-    checkpoint_path = os.path.join(out_path, checkpoint_path)
-    print(" | | > Checkpoint saving : {}".format(checkpoint_path))
-
-    new_state_dict = model.state_dict()
-    state = {
-        'model': new_state_dict,
-        'optimizer': optimizer.state_dict() if optimizer is not None else None,
-        'step': current_step,
-        'epoch': epoch,
-        'GE2Eloss': model_loss,
-        'date': datetime.date.today().strftime("%B %d, %Y"),
-    }
-    torch.save(state, checkpoint_path)
-
-
-def save_best_model(model, optimizer, model_loss, best_loss, out_path,
-                    current_step):
-    if model_loss < best_loss:
-        new_state_dict = model.state_dict()
-        state = {
-            'model': new_state_dict,
-            'optimizer': optimizer.state_dict(),
-            'step': current_step,
-            'GE2Eloss': model_loss,
-            'date': datetime.date.today().strftime("%B %d, %Y"),
-        }
-        best_loss = model_loss
-        bestmodel_path = 'best_model.pth.tar'
-        bestmodel_path = os.path.join(out_path, bestmodel_path)
-        print("\n > BEST MODEL ({0:.5f}) : {1:}".format(
-            model_loss, bestmodel_path))
-        torch.save(state, bestmodel_path)
-    return best_loss
\ No newline at end of file
diff --git a/speaker_encoder/loss.py b/speaker_encoder/loss.py
deleted file mode 100644
index ab290547..00000000
--- a/speaker_encoder/loss.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-# adapted from https://github.com/cvqluu/GE2E-Loss
-class GE2ELoss(nn.Module):
-    def __init__(self, init_w=10.0, init_b=-5.0, loss_method="softmax"):
-        """
-        Implementation of the Generalized End-to-End loss defined in https://arxiv.org/abs/1710.10467 [1]
-        Accepts an input of size (N, M, D)
-            where N is the number of speakers in the batch,
-            M is the number of utterances per speaker,
-            and D is the dimensionality of the embedding vector (e.g. d-vector)
-        Args:
-            - init_w (float): defines the initial value of w in Equation (5) of [1]
-            - init_b (float): definies the initial value of b in Equation (5) of [1]
-        """
-        super(GE2ELoss, self).__init__()
-        # pylint: disable=E1102
-        self.w = nn.Parameter(torch.tensor(init_w))
-        # pylint: disable=E1102
-        self.b = nn.Parameter(torch.tensor(init_b))
-        self.loss_method = loss_method
-
-        assert self.loss_method in ["softmax", "contrast"]
-
-        if self.loss_method == "softmax":
-            self.embed_loss = self.embed_loss_softmax
-        if self.loss_method == "contrast":
-            self.embed_loss = self.embed_loss_contrast
-
-    # pylint: disable=R0201
-    def calc_new_centroids(self, dvecs, centroids, spkr, utt):
-        """
-        Calculates the new centroids excluding the reference utterance
-        """
-        excl = torch.cat((dvecs[spkr, :utt], dvecs[spkr, utt + 1 :]))
-        excl = torch.mean(excl, 0)
-        new_centroids = []
-        for i, centroid in enumerate(centroids):
-            if i == spkr:
-                new_centroids.append(excl)
-            else:
-                new_centroids.append(centroid)
-        return torch.stack(new_centroids)
-
-    def calc_cosine_sim(self, dvecs, centroids):
-        """
-        Make the cosine similarity matrix with dims (N,M,N)
-        """
-        cos_sim_matrix = []
-        for spkr_idx, speaker in enumerate(dvecs):
-            cs_row = []
-            for utt_idx, utterance in enumerate(speaker):
-                new_centroids = self.calc_new_centroids(
-                    dvecs, centroids, spkr_idx, utt_idx
-                )
-                # vector based cosine similarity for speed
-                cs_row.append(
-                    torch.clamp(
-                        torch.mm(
-                            utterance.unsqueeze(1).transpose(0, 1),
-                            new_centroids.transpose(0, 1),
-                        )
-                        / (torch.norm(utterance) * torch.norm(new_centroids, dim=1)),
-                        1e-6,
-                    )
-                )
-            cs_row = torch.cat(cs_row, dim=0)
-            cos_sim_matrix.append(cs_row)
-        return torch.stack(cos_sim_matrix)
-
-    # pylint: disable=R0201
-    def embed_loss_softmax(self, dvecs, cos_sim_matrix):
-        """
-        Calculates the loss on each embedding $L(e_{ji})$ by taking softmax
-        """
-        N, M, _ = dvecs.shape
-        L = []
-        for j in range(N):
-            L_row = []
-            for i in range(M):
-                L_row.append(-F.log_softmax(cos_sim_matrix[j, i], 0)[j])
-            L_row = torch.stack(L_row)
-            L.append(L_row)
-        return torch.stack(L)
-
-    # pylint: disable=R0201
-    def embed_loss_contrast(self, dvecs, cos_sim_matrix):
-        """
-        Calculates the loss on each embedding $L(e_{ji})$ by contrast loss with closest centroid
-        """
-        N, M, _ = dvecs.shape
-        L = []
-        for j in range(N):
-            L_row = []
-            for i in range(M):
-                centroids_sigmoids = torch.sigmoid(cos_sim_matrix[j, i])
-                excl_centroids_sigmoids = torch.cat(
-                    (centroids_sigmoids[:j], centroids_sigmoids[j + 1 :])
-                )
-                L_row.append(
-                    1.0
-                    - torch.sigmoid(cos_sim_matrix[j, i, j])
-                    + torch.max(excl_centroids_sigmoids)
-                )
-            L_row = torch.stack(L_row)
-            L.append(L_row)
-        return torch.stack(L)
-
-    def forward(self, dvecs):
-        """
-        Calculates the GE2E loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
-        """
-        centroids = torch.mean(dvecs, 1)
-        cos_sim_matrix = self.calc_cosine_sim(dvecs, centroids)
-        torch.clamp(self.w, 1e-6)
-        cos_sim_matrix = self.w * cos_sim_matrix + self.b
-        L = self.embed_loss(dvecs, cos_sim_matrix)
-        return L.mean()
diff --git a/speaker_encoder/model.py b/speaker_encoder/model.py
deleted file mode 100644
index b3bd71ff..00000000
--- a/speaker_encoder/model.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import torch
-from torch import nn
-
-
-class LSTMWithProjection(nn.Module):
-    def __init__(self, input_size, hidden_size, proj_size):
-        super().__init__()
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.proj_size = proj_size
-        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
-        self.linear = nn.Linear(hidden_size, proj_size, bias=False)
-
-    def forward(self, x):
-        self.lstm.flatten_parameters()
-        o, (_, _) = self.lstm(x)
-        return self.linear(o)
-
-
-class SpeakerEncoder(nn.Module):
-    def __init__(self, input_dim, proj_dim=256, lstm_dim=768, num_lstm_layers=3):
-        super().__init__()
-        layers = []
-        layers.append(LSTMWithProjection(input_dim, lstm_dim, proj_dim))
-        for _ in range(num_lstm_layers - 1):
-            layers.append(LSTMWithProjection(proj_dim, lstm_dim, proj_dim))
-        self.layers = nn.Sequential(*layers)
-        self._init_layers()
-
-    def _init_layers(self):
-        for name, param in self.layers.named_parameters():
-            if "bias" in name:
-                nn.init.constant_(param, 0.0)
-            elif "weight" in name:
-                nn.init.xavier_normal_(param)
-
-    def forward(self, x):
-        # TODO: implement state passing for lstms
-        d = self.layers(x)
-        d = torch.nn.functional.normalize(d[:, -1], p=2, dim=1)
-        return d
-
-    def inference(self, x):
-        d = self.layers.forward(x)
-        d = torch.nn.functional.normalize(d[:, -1], p=2, dim=1)
-        return d
-
-    def compute_embedding(self, x, num_frames=160, overlap=0.5):
-        """
-        Generate embeddings for a batch of utterances
-        x: 1xTxD
-        """
-        num_overlap = int(num_frames * overlap)
-        max_len = x.shape[1]
-        embed = None
-        cur_iter = 0
-        for offset in range(0, max_len, num_frames - num_overlap):
-            cur_iter += 1
-            end_offset = min(x.shape[1], offset + num_frames)
-            frames = x[:, offset:end_offset]
-            if embed is None:
-                embed = self.inference(frames)
-            else:
-                embed += self.inference(frames)
-        return embed / cur_iter
-
-    def batch_compute_embedding(self, x, seq_lens, num_frames=160, overlap=0.5):
-        """
-        Generate embeddings for a batch of utterances
-        x: BxTxD
-        """
-        num_overlap = num_frames * overlap
-        max_len = x.shape[1]
-        embed = None
-        num_iters = seq_lens / (num_frames - num_overlap)
-        cur_iter = 0
-        for offset in range(0, max_len, num_frames - num_overlap):
-            cur_iter += 1
-            end_offset = min(x.shape[1], offset + num_frames)
-            frames = x[:, offset:end_offset]
-            if embed is None:
-                embed = self.inference(frames)
-            else:
-                embed[cur_iter <= num_iters, :] += self.inference(
-                    frames[cur_iter <= num_iters, :, :]
-                )
-        return embed / num_iters
-
diff --git a/speaker_encoder/notebooks/PlotUmapLibriTTS.ipynb b/speaker_encoder/notebooks/PlotUmapLibriTTS.ipynb
deleted file mode 100644
index 159f040c..00000000
--- a/speaker_encoder/notebooks/PlotUmapLibriTTS.ipynb
+++ /dev/null
@@ -1,325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Overview\n",
-    "\n",
-    "This notebook can be used with both a single or multi- speaker corpus and allows the interactive plotting of speaker embeddings linked to underlying audio (see instructions in the repo's speaker_embedding directory)\n",
-    "\n",
-    "Depending on the directory structure used for your corpus, you may need to adjust handling of **speaker_to_utter** and **locations**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import glob\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import umap\n",
-    "\n",
-    "from TTS.speaker_encoder.model import SpeakerEncoder\n",
-    "from TTS.utils.audio import AudioProcessor\n",
-    "from TTS.utils.generic_utils import load_config\n",
-    "\n",
-    "from bokeh.io import output_notebook, show\n",
-    "from bokeh.plotting import figure\n",
-    "from bokeh.models import HoverTool, ColumnDataSource, BoxZoomTool, ResetTool, OpenURL, TapTool\n",
-    "from bokeh.transform import factor_cmap, factor_mark\n",
-    "from bokeh.palettes import Category10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For larger sets of speakers, you can use **Category20**, but you need to change it in the **pal** variable too\n",
-    "\n",
-    "List of Bokeh palettes here: http://docs.bokeh.org/en/1.4.0/docs/reference/palettes.html\n",
-    "\n",
-    "**NB:** if you have problems with other palettes, first see https://stackoverflow.com/questions/48333820/why-do-some-bokeh-palettes-raise-a-valueerror-when-used-in-factor-cmap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_notebook()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You should also adjust all the path constants to point at the relevant locations for you locally"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MODEL_RUN_PATH = \"/media/erogol/data_ssd/Models/libri_tts/speaker_encoder/libritts_360-half-October-31-2019_04+54PM-19d2f5f/\"\n",
-    "MODEL_PATH = MODEL_RUN_PATH + \"best_model.pth.tar\"\n",
-    "CONFIG_PATH = MODEL_RUN_PATH + \"config.json\"\n",
-    "\n",
-    "# My single speaker locations\n",
-    "#EMBED_PATH = \"/home/neil/main/Projects/TTS3/embeddings/neil14/\"\n",
-    "#AUDIO_PATH = \"/home/neil/data/Projects/NeilTTS/neil14/wavs/\"\n",
-    "\n",
-    "# My multi speaker locations\n",
-    "EMBED_PATH = \"/home/erogol/Data/Libri-TTS/train-clean-360-embed_128/\"\n",
-    "AUDIO_PATH = \"/home/erogol/Data/Libri-TTS/train-clean-360/\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!ls -1 $MODEL_RUN_PATH"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "CONFIG = load_config(CONFIG_PATH)\n",
-    "ap = AudioProcessor(**CONFIG['audio'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Bring in the embeddings created by **compute_embeddings.py**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embed_files = glob.glob(EMBED_PATH+\"/**/*.npy\", recursive=True)\n",
-    "print(f'Embeddings found: {len(embed_files)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check that we did indeed find an embedding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embed_files[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Process the speakers\n",
-    "\n",
-    "Assumes count of **speaker_paths** corresponds to number of speakers (so a corpus in just one directory would be treated like a single speaker and the multiple directories of LibriTTS are treated as distinct speakers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "speaker_paths = list(set([os.path.dirname(os.path.dirname(embed_file)) for embed_file in embed_files]))\n",
-    "speaker_to_utter = {}\n",
-    "for embed_file in embed_files:\n",
-    "    speaker_path = os.path.dirname(os.path.dirname(embed_file))\n",
-    "    try:\n",
-    "        speaker_to_utter[speaker_path].append(embed_file)\n",
-    "    except:\n",
-    "        speaker_to_utter[speaker_path]=[embed_file]\n",
-    "print(f'Speaker count: {len(speaker_paths)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set up the embeddings\n",
-    "\n",
-    "Adjust the number of speakers to select and the number of utterances from each speaker and they will be randomly sampled from the corpus"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeds = []\n",
-    "labels = []\n",
-    "locations = []\n",
-    "\n",
-    "# single speaker \n",
-    "#num_speakers = 1\n",
-    "#num_utters = 1000\n",
-    "\n",
-    "# multi speaker\n",
-    "num_speakers = 10\n",
-    "num_utters = 20\n",
-    "\n",
-    "\n",
-    "speaker_idxs = np.random.choice(range(len(speaker_paths)), num_speakers, replace=False )\n",
-    "\n",
-    "for speaker_num, speaker_idx in enumerate(speaker_idxs):\n",
-    "    speaker_path = speaker_paths[speaker_idx]\n",
-    "    speakers_utter = speaker_to_utter[speaker_path]\n",
-    "    utter_idxs = np.random.randint(0, len(speakers_utter) , num_utters)\n",
-    "    for utter_idx in utter_idxs:\n",
-    "            embed_path = speaker_to_utter[speaker_path][utter_idx]\n",
-    "            embed = np.load(embed_path)\n",
-    "            embeds.append(embed)\n",
-    "            labels.append(str(speaker_num))\n",
-    "            locations.append(embed_path.replace(EMBED_PATH, '').replace('.npy','.wav'))\n",
-    "embeds = np.concatenate(embeds)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load embeddings with UMAP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = umap.UMAP()\n",
-    "projection = model.fit_transform(embeds)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interactively charting the data in Bokeh\n",
-    "\n",
-    "Set up various details for Bokeh to plot the data\n",
-    "\n",
-    "You can use the regular Bokeh [tools](http://docs.bokeh.org/en/1.4.0/docs/user_guide/tools.html?highlight=tools) to explore the data, with reset setting it back to normal\n",
-    "\n",
-    "Once you have started the local server (see cell below) you can then click on plotted points which will open a tab to play the audio for that point, enabling easy exploration of your corpus\n",
-    "\n",
-    "File location in the tooltip is given relative to **AUDIO_PATH**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_wav_stems = ColumnDataSource(\n",
-    "        data=dict(\n",
-    "            x = projection.T[0].tolist(),\n",
-    "            y = projection.T[1].tolist(),\n",
-    "            desc=locations,\n",
-    "            label=labels\n",
-    "        )\n",
-    "    )\n",
-    "\n",
-    "hover = HoverTool(\n",
-    "        tooltips=[\n",
-    "            (\"file\", \"@desc\"),\n",
-    "            (\"speaker\", \"@label\"),\n",
-    "        ]\n",
-    "    )\n",
-    "\n",
-    "# optionally consider adding these to the tooltips if you want additional detail\n",
-    "# for the coordinates: (\"(x,y)\", \"($x, $y)\"),\n",
-    "# for the index of the embedding / wav file: (\"index\", \"$index\"),\n",
-    "\n",
-    "factors = list(set(labels))\n",
-    "pal_size = max(len(factors), 3)\n",
-    "pal = Category10[pal_size]\n",
-    "\n",
-    "p = figure(plot_width=600, plot_height=400, tools=[hover,BoxZoomTool(), ResetTool(), TapTool()])\n",
-    "\n",
-    "\n",
-    "p.circle('x', 'y',  source=source_wav_stems, color=factor_cmap('label', palette=pal, factors=factors),)\n",
-    "\n",
-    "url = \"http://localhost:8000/@desc\"\n",
-    "taptool = p.select(type=TapTool)\n",
-    "taptool.callback = OpenURL(url=url)\n",
-    "\n",
-    "show(p)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Local server to serve wav files from corpus\n",
-    "\n",
-    "This is required so that when you click on a data point the hyperlink associated with it will be served the file locally.\n",
-    "\n",
-    "There are other ways to serve this if you prefer and you can also run the commands manually on the command line\n",
-    "\n",
-    "The server will continue to run until stopped. To stop it simply interupt the kernel (ie square button or under Kernel menu)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%cd $AUDIO_PATH\n",
-    "%pwd\n",
-    "!python -m http.server"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/speaker_encoder/requirements.txt b/speaker_encoder/requirements.txt
deleted file mode 100644
index a486cc45..00000000
--- a/speaker_encoder/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-umap-learn
-numpy>=1.17.0
diff --git a/speaker_encoder/train.py b/speaker_encoder/train.py
deleted file mode 100644
index f74d0880..00000000
--- a/speaker_encoder/train.py
+++ /dev/null
@@ -1,252 +0,0 @@
-import argparse
-import os
-import sys
-import time
-import traceback
-
-import torch
-from torch.utils.data import DataLoader
-from TTS.datasets.preprocess import load_meta_data
-from TTS.speaker_encoder.dataset import MyDataset
-from TTS.speaker_encoder.loss import GE2ELoss
-from TTS.speaker_encoder.model import SpeakerEncoder
-from TTS.speaker_encoder.visual import plot_embeddings
-from TTS.speaker_encoder.generic_utils import save_best_model
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.generic_utils import (create_experiment_folder, get_git_branch,
-                                     remove_experiment_folder, set_init_dict)
-from TTS.utils.io import load_config, copy_config_file
-from TTS.utils.training import check_update, NoamLR
-from TTS.utils.tensorboard_logger import TensorboardLogger
-from TTS.utils.radam import RAdam
-
-torch.backends.cudnn.enabled = True
-torch.backends.cudnn.benchmark = True
-torch.manual_seed(54321)
-use_cuda = torch.cuda.is_available()
-num_gpus = torch.cuda.device_count()
-print(" > Using CUDA: ", use_cuda)
-print(" > Number of GPUs: ", num_gpus)
-
-
-def setup_loader(ap, is_val=False, verbose=False):
-    if is_val:
-        loader = None
-    else:
-        dataset = MyDataset(ap,
-                            meta_data_eval if is_val else meta_data_train,
-                            voice_len=1.6,
-                            num_utter_per_speaker=10,
-                            skip_speakers=False,
-                            verbose=verbose)
-        # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
-        loader = DataLoader(dataset,
-                            batch_size=c.num_speakers_in_batch,
-                            shuffle=False,
-                            num_workers=c.num_loader_workers,
-                            collate_fn=dataset.collate_fn)
-    return loader
-
-
-def train(model, criterion, optimizer, scheduler, ap, global_step):
-    data_loader = setup_loader(ap, is_val=False, verbose=True)
-    model.train()
-    epoch_time = 0
-    best_loss = float('inf')
-    avg_loss = 0
-    end_time = time.time()
-    for _, data in enumerate(data_loader):
-        start_time = time.time()
-
-        # setup input data
-        inputs = data[0]
-        loader_time = time.time() - end_time
-        global_step += 1
-
-        # setup lr
-        if c.lr_decay:
-            scheduler.step()
-        optimizer.zero_grad()
-
-        # dispatch data to GPU
-        if use_cuda:
-            inputs = inputs.cuda(non_blocking=True)
-            # labels = labels.cuda(non_blocking=True)
-
-        # forward pass model
-        outputs = model(inputs)
-
-        # loss computation
-        loss = criterion(
-            outputs.view(c.num_speakers_in_batch,
-                         outputs.shape[0] // c.num_speakers_in_batch, -1))
-        loss.backward()
-        grad_norm, _ = check_update(model, c.grad_clip)
-        optimizer.step()
-
-        step_time = time.time() - start_time
-        epoch_time += step_time
-
-        avg_loss = 0.01 * loss.item(
-        ) + 0.99 * avg_loss if avg_loss != 0 else loss.item()
-        current_lr = optimizer.param_groups[0]['lr']
-
-        if global_step % c.steps_plot_stats == 0:
-            # Plot Training Epoch Stats
-            train_stats = {
-                "GE2Eloss": avg_loss,
-                "lr": current_lr,
-                "grad_norm": grad_norm,
-                "step_time": step_time
-            }
-            tb_logger.tb_train_epoch_stats(global_step, train_stats)
-            figures = {
-                # FIXME: not constant
-                "UMAP Plot": plot_embeddings(outputs.detach().cpu().numpy(),
-                                             10),
-            }
-            tb_logger.tb_train_figures(global_step, figures)
-
-        if global_step % c.print_step == 0:
-            print(
-                "   | > Step:{}  Loss:{:.5f}  AvgLoss:{:.5f}  GradNorm:{:.5f}  "
-                "StepTime:{:.2f}  LoaderTime:{:.2f}  LR:{:.6f}".format(
-                    global_step, loss.item(), avg_loss, grad_norm, step_time,
-                    loader_time, current_lr),
-                flush=True)
-
-        # save best model
-        best_loss = save_best_model(model, optimizer, avg_loss, best_loss,
-                                    OUT_PATH, global_step)
-
-        end_time = time.time()
-    return avg_loss, global_step
-
-
-def main(args):  # pylint: disable=redefined-outer-name
-    # pylint: disable=global-variable-undefined
-    global meta_data_train
-    global meta_data_eval
-
-    ap = AudioProcessor(**c.audio)
-    model = SpeakerEncoder(input_dim=40,
-                           proj_dim=128,
-                           lstm_dim=384,
-                           num_lstm_layers=3)
-    optimizer = RAdam(model.parameters(), lr=c.lr)
-    criterion = GE2ELoss(loss_method='softmax')
-
-    if args.restore_path:
-        checkpoint = torch.load(args.restore_path)
-        try:
-            # TODO: fix optimizer init, model.cuda() needs to be called before
-            # optimizer restore
-            # optimizer.load_state_dict(checkpoint['optimizer'])
-            if c.reinit_layers:
-                raise RuntimeError
-            model.load_state_dict(checkpoint['model'])
-        except KeyError:
-            print(" > Partial model initialization.")
-            model_dict = model.state_dict()
-            model_dict = set_init_dict(model_dict, checkpoint, c)
-            model.load_state_dict(model_dict)
-            del model_dict
-        for group in optimizer.param_groups:
-            group['lr'] = c.lr
-        print(" > Model restored from step %d" % checkpoint['step'],
-              flush=True)
-        args.restore_step = checkpoint['step']
-    else:
-        args.restore_step = 0
-
-    if use_cuda:
-        model = model.cuda()
-        criterion.cuda()
-
-    if c.lr_decay:
-        scheduler = NoamLR(optimizer,
-                           warmup_steps=c.warmup_steps,
-                           last_epoch=args.restore_step - 1)
-    else:
-        scheduler = None
-
-    num_params = count_parameters(model)
-    print("\n > Model has {} parameters".format(num_params), flush=True)
-
-    # pylint: disable=redefined-outer-name
-    meta_data_train, meta_data_eval = load_meta_data(c.datasets)
-
-    global_step = args.restore_step
-    train_loss, global_step = train(model, criterion, optimizer, scheduler, ap,
-                                    global_step)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--restore_path',
-        type=str,
-        help='Path to model outputs (checkpoint, tensorboard etc.).',
-        default=0)
-    parser.add_argument(
-        '--config_path',
-        type=str,
-        help='Path to config file for training.',
-    )
-    parser.add_argument('--debug',
-                        type=bool,
-                        default=True,
-                        help='Do not verify commit integrity to run training.')
-    parser.add_argument(
-        '--data_path',
-        type=str,
-        default='',
-        help='Defines the data path. It overwrites config.json.')
-    parser.add_argument('--output_path',
-                        type=str,
-                        help='path for training outputs.',
-                        default='')
-    parser.add_argument('--output_folder',
-                        type=str,
-                        default='',
-                        help='folder name for training outputs.')
-    args = parser.parse_args()
-
-    # setup output paths and read configs
-    c = load_config(args.config_path)
-    _ = os.path.dirname(os.path.realpath(__file__))
-    if args.data_path != '':
-        c.data_path = args.data_path
-
-    if args.output_path == '':
-        OUT_PATH = os.path.join(_, c.output_path)
-    else:
-        OUT_PATH = args.output_path
-
-    if args.output_folder == '':
-        OUT_PATH = create_experiment_folder(OUT_PATH, c.run_name, args.debug)
-    else:
-        OUT_PATH = os.path.join(OUT_PATH, args.output_folder)
-
-    new_fields = {}
-    if args.restore_path:
-        new_fields["restore_path"] = args.restore_path
-    new_fields["github_branch"] = get_git_branch()
-    copy_config_file(args.config_path, os.path.join(OUT_PATH, 'config.json'),
-                     new_fields)
-
-    LOG_DIR = OUT_PATH
-    tb_logger = TensorboardLogger(LOG_DIR)
-
-    try:
-        main(args)
-    except KeyboardInterrupt:
-        remove_experiment_folder(OUT_PATH)
-        try:
-            sys.exit(0)
-        except SystemExit:
-            os._exit(0)  # pylint: disable=protected-access
-    except Exception:  # pylint: disable=broad-except
-        remove_experiment_folder(OUT_PATH)
-        traceback.print_exc()
-        sys.exit(1)
diff --git a/speaker_encoder/umap.png b/speaker_encoder/umap.png
deleted file mode 100644
index ca8aefea..00000000
Binary files a/speaker_encoder/umap.png and /dev/null differ
diff --git a/speaker_encoder/visual.py b/speaker_encoder/visual.py
deleted file mode 100644
index 68c48f12..00000000
--- a/speaker_encoder/visual.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import umap
-import numpy as np
-import matplotlib
-import matplotlib.pyplot as plt
-
-matplotlib.use("Agg")
-
-
-colormap = (
-    np.array(
-        [
-            [76, 255, 0],
-            [0, 127, 70],
-            [255, 0, 0],
-            [255, 217, 38],
-            [0, 135, 255],
-            [165, 0, 165],
-            [255, 167, 255],
-            [0, 255, 255],
-            [255, 96, 38],
-            [142, 76, 0],
-            [33, 0, 127],
-            [0, 0, 0],
-            [183, 183, 183],
-        ],
-        dtype=np.float,
-    )
-    / 255
-)
-
-
-def plot_embeddings(embeddings, num_utter_per_speaker):
-    embeddings = embeddings[: 10 * num_utter_per_speaker]
-    model = umap.UMAP()
-    projection = model.fit_transform(embeddings)
-    num_speakers = embeddings.shape[0] // num_utter_per_speaker
-    ground_truth = np.repeat(np.arange(num_speakers), num_utter_per_speaker)
-    colors = [colormap[i] for i in ground_truth]
-
-    fig, ax = plt.subplots(figsize=(16, 10))
-    _ = ax.scatter(projection[:, 0], projection[:, 1], c=colors)
-    plt.gca().set_aspect("equal", "datalim")
-    plt.title("UMAP projection")
-    plt.tight_layout()
-    plt.savefig("umap")
-    return fig
diff --git a/synthesize.py b/synthesize.py
deleted file mode 100644
index 18048c2f..00000000
--- a/synthesize.py
+++ /dev/null
@@ -1,182 +0,0 @@
-# pylint: disable=redefined-outer-name, unused-argument
-import os
-import time
-import argparse
-import torch
-import json
-import string
-
-from TTS.utils.synthesis import synthesis
-from TTS.utils.generic_utils import setup_model
-from TTS.utils.io import load_config
-from TTS.utils.text.symbols import make_symbols, symbols, phonemes
-from TTS.utils.audio import AudioProcessor
-
-
-def tts(model,
-        vocoder_model,
-        C,
-        VC,
-        text,
-        ap,
-        ap_vocoder,
-        use_cuda,
-        batched_vocoder,
-        speaker_id=None,
-        figures=False):
-    t_1 = time.time()
-    use_vocoder_model = vocoder_model is not None
-    waveform, alignment, _, postnet_output, stop_tokens, _ = synthesis(
-        model, text, C, use_cuda, ap, speaker_id, style_wav=False,
-        truncated=False, enable_eos_bos_chars=C.enable_eos_bos_chars,
-        use_griffin_lim=(not use_vocoder_model), do_trim_silence=True)
-
-    if C.model == "Tacotron" and use_vocoder_model:
-        postnet_output = ap.out_linear_to_mel(postnet_output.T).T
-    # correct if there is a scale difference b/w two models
-    if use_vocoder_model:
-        postnet_output = ap._denormalize(postnet_output)
-        postnet_output = ap_vocoder._normalize(postnet_output)
-        vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
-        waveform = vocoder_model.generate(
-            vocoder_input.cuda() if use_cuda else vocoder_input,
-            batched=batched_vocoder,
-            target=8000,
-            overlap=400)
-    print(" >  Run-time: {}".format(time.time() - t_1))
-    return alignment, postnet_output, stop_tokens, waveform
-
-
-if __name__ == "__main__":
-
-    global symbols, phonemes
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('text', type=str, help='Text to generate speech.')
-    parser.add_argument('config_path',
-                        type=str,
-                        help='Path to model config file.')
-    parser.add_argument(
-        'model_path',
-        type=str,
-        help='Path to model file.',
-    )
-    parser.add_argument(
-        'out_path',
-        type=str,
-        help='Path to save final wav file. Wav file will be names as the text given.',
-    )
-    parser.add_argument('--use_cuda',
-                        type=bool,
-                        help='Run model on CUDA.',
-                        default=False)
-    parser.add_argument(
-        '--vocoder_path',
-        type=str,
-        help=
-        'Path to vocoder model file. If it is not defined, model uses GL as vocoder. Please make sure that you installed vocoder library before (WaveRNN).',
-        default="",
-    )
-    parser.add_argument('--vocoder_config_path',
-                        type=str,
-                        help='Path to vocoder model config file.',
-                        default="")
-    parser.add_argument(
-        '--batched_vocoder',
-        type=bool,
-        help="If True, vocoder model uses faster batch processing.",
-        default=True)
-    parser.add_argument('--speakers_json',
-                        type=str,
-                        help="JSON file for multi-speaker model.",
-                        default="")
-    parser.add_argument(
-        '--speaker_id',
-        type=int,
-        help="target speaker_id if the model is multi-speaker.",
-        default=None)
-    args = parser.parse_args()
-
-    if args.vocoder_path != "":
-        assert args.use_cuda, " [!] Enable cuda for vocoder."
-        from WaveRNN.models.wavernn import Model as VocoderModel
-
-    # load the config
-    C = load_config(args.config_path)
-    C.forward_attn_mask = True
-
-    # load the audio processor
-    ap = AudioProcessor(**C.audio)
-
-    # if the vocabulary was passed, replace the default
-    if 'characters' in C.keys():
-        symbols, phonemes = make_symbols(**C.characters)
-
-    # load speakers
-    if args.speakers_json != '':
-        speakers = json.load(open(args.speakers_json, 'r'))
-        num_speakers = len(speakers)
-    else:
-        num_speakers = 0
-
-    # load the model
-    num_chars = len(phonemes) if C.use_phonemes else len(symbols)
-    model = setup_model(num_chars, num_speakers, C)
-    cp = torch.load(args.model_path)
-    model.load_state_dict(cp['model'])
-    model.eval()
-    if args.use_cuda:
-        model.cuda()
-    model.decoder.set_r(cp['r'])
-
-    # load vocoder model
-    if args.vocoder_path != "":
-        VC = load_config(args.vocoder_config_path)
-        ap_vocoder = AudioProcessor(**VC.audio)
-        bits = 10
-        vocoder_model = VocoderModel(rnn_dims=512,
-                                     fc_dims=512,
-                                     mode=VC.mode,
-                                     mulaw=VC.mulaw,
-                                     pad=VC.pad,
-                                     upsample_factors=VC.upsample_factors,
-                                     feat_dims=VC.audio["num_mels"],
-                                     compute_dims=128,
-                                     res_out_dims=128,
-                                     res_blocks=10,
-                                     hop_length=ap.hop_length,
-                                     sample_rate=ap.sample_rate,
-                                     use_aux_net=True,
-                                     use_upsample_net=True)
-
-        check = torch.load(args.vocoder_path)
-        vocoder_model.load_state_dict(check['model'])
-        vocoder_model.eval()
-        if args.use_cuda:
-            vocoder_model.cuda()
-    else:
-        vocoder_model = None
-        VC = None
-        ap_vocoder = None
-
-    # synthesize voice
-    print(" > Text: {}".format(args.text))
-    _, _, _, wav = tts(model,
-                       vocoder_model,
-                       C,
-                       VC,
-                       args.text,
-                       ap,
-                       ap_vocoder,
-                       args.use_cuda,
-                       args.batched_vocoder,
-                       speaker_id=args.speaker_id,
-                       figures=False)
-
-    # save the results
-    file_name = args.text.replace(" ", "_")
-    file_name = file_name.translate(
-        str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
-    out_path = os.path.join(args.out_path, file_name)
-    print(" > Saving output to {}".format(out_path))
-    ap.save_wav(wav, out_path)
diff --git a/tests/generic_utils_text.py b/tests/generic_utils_text.py
index 228df2df..19c48647 100644
--- a/tests/generic_utils_text.py
+++ b/tests/generic_utils_text.py
@@ -1,8 +1,8 @@
 import unittest
 import torch as T
 
-from TTS.utils.generic_utils import save_checkpoint, save_best_model
-from TTS.layers.tacotron import Prenet
+from TTS.tts.utils.generic_utils import save_checkpoint, save_best_model
+from TTS.tts.layers.tacotron import Prenet
 
 OUT_PATH = '/tmp/test.pth.tar'
 
diff --git a/tests/test_config.json b/tests/inputs/test_config.json
similarity index 100%
rename from tests/test_config.json
rename to tests/inputs/test_config.json
diff --git a/config.json b/tests/inputs/test_train_config.json
similarity index 83%
rename from config.json
rename to tests/inputs/test_train_config.json
index 23868a33..e43903ce 100644
--- a/config.json
+++ b/tests/inputs/test_train_config.json
@@ -1,149 +1,151 @@
-{
-    "model": "Tacotron2",
-    "run_name": "ljspeech-ddc-bn",
-    "run_description": "tacotron2 with ddc and batch-normalization",
-
-    // AUDIO PARAMETERS
-    "audio":{
-        // stft parameters
-        "fft_size": 1024,         // number of stft frequency levels. Size of the linear spectogram frame.
-        "win_length": 1024,      // stft window length in ms.
-        "hop_length": 256,       // stft window hop-lengh in ms.
-        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
-        // Audio processing parameters
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate.
-        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-
-        // Silence trimming
-        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
-        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
-
-        // Griffin-Lim
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
-        // MelSpectrogram parameters
-        "num_mels": 80,         // size of the mel spec frame.
-        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
-        "spec_gain": 20.0,
-
-        // Normalization parameters
-        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
-        "min_level_db": -100,   // lower bound for normalization
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
-    },
-
-    // VOCABULARY PARAMETERS
-    // if custom character set is not defined,
-    // default set in symbols.py is used
-    // "characters":{
-    //     "pad": "_",
-    //     "eos": "~",
-    //     "bos": "^",
-    //     "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
-    //     "punctuations":"!'(),-.:;? ",
-    //     "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
-    // },
-
-    // DISTRIBUTED TRAINING
-    "distributed":{
-        "backend": "nccl",
-        "url": "tcp:\/\/localhost:54321"
-    },
-
-    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
-    // TRAINING
-    "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-    "eval_batch_size":16,
-    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
-    "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
-    "loss_masking": true,         // enable / disable loss masking against the sequence padding.
-    "ga_alpha": 10.0,        // weight for guided attention loss. If > 0, guided attention is enabled.
-
-    // VALIDATION
-    "run_eval": true,
-    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
-    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
-    // OPTIMIZER
-    "noam_schedule": false,        // use noam warmup and lr schedule.
-    "grad_clip": 1.0,              // upper limit for gradients for clipping.
-    "epochs": 1000,                // total number of epochs to train.
-    "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "wd": 0.000001,                // Weight decay weight.
-    "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "seq_len_norm": false,         // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
-
-    // TACOTRON PRENET
-    "memory_size": -1,             // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
-    "prenet_type": "bn",     // "original" or "bn".
-    "prenet_dropout": false,        // enable/disable dropout at prenet.
-
-    // TACOTRON ATTENTION
-    "attention_type": "original",  // 'original' or 'graves'
-    "attention_heads": 4,          // number of attention heads (only for 'graves')
-    "attention_norm": "sigmoid",   // softmax or sigmoid.
-    "windowing": false,            // Enables attention windowing. Used only in eval mode.
-    "use_forward_attn": false,     // if it uses forward attention. In general, it aligns faster.
-    "forward_attn_mask": false,    // Additional masking forcing monotonicity only in eval mode.
-    "transition_agent": false,     // enable/disable transition agent of forward attention.
-    "location_attn": true,         // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
-    "bidirectional_decoder": false,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
-    "double_decoder_consistency": true,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
-    "ddc_r": 7,                           // reduction rate for coarse decoder.
-
-    // STOPNET
-    "stopnet": true,               // Train stopnet predicting the end of synthesis.
-    "separate_stopnet": true,      // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
-
-    // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log training on console.
-    "tb_plot_step": 100,    // Number of steps to plot TB training figures.
-    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
-    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
-    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
-    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
-    // DATA LOADING
-    "text_cleaner": "phoneme_cleaners",
-    "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
-    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
-    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
-    "batch_group_size": 0,  //Number of batches to shuffle after bucketing.
-    "min_seq_len": 6,       // DATASET-RELATED: minimum text length to use in training
-    "max_seq_len": 153,     // DATASET-RELATED: maximum text length
-
-    // PATHS
-    "output_path": "/home/erogol/Models/LJSpeech/",
-
-    // PHONEMES
-    "phoneme_cache_path": "/media/erogol/data_ssd2/mozilla_us_phonemes_3",  // phoneme computation is slow, therefore, it caches results in the given folder.
-    "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
-    "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
-
-    // MULTI-SPEAKER and GST
-    "use_speaker_embedding": false,     // use speaker embedding to enable multi-speaker learning.
-    "style_wav_for_test": null,          // path to style wav file to be used in TacotronGST inference.
-    "use_gst": false,       // TACOTRON ONLY: use global style tokens
-
-    // DATASETS
-    "datasets":   // List of datasets. They all merged and they get different speaker_ids.
-        [
-            {
-                "name": "ljspeech",
-                "path": "/home/erogol/Data/LJSpeech-1.1/",
-                "meta_file_train": "metadata.csv",
-                "meta_file_val": null
-            }
-        ]
-
-}
-
+{
+    "model": "Tacotron2",
+    "run_name": "test_sample_dataset_run",
+    "run_description": "sample dataset test run",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        // stft parameters
+        "fft_size": 1024,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // Griffin-Lim
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+        "spec_gain": 20.0,
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // VOCABULARY PARAMETERS
+    // if custom character set is not defined,
+    // default set in symbols.py is used
+    // "characters":{
+    //     "pad": "_",
+    //     "eos": "~",
+    //     "bos": "^",
+    //     "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
+    //     "punctuations":"!'(),-.:;? ",
+    //     "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
+    // },
+
+    // DISTRIBUTED TRAINING
+    "distributed":{
+        "backend": "nccl",
+        "url": "tcp:\/\/localhost:54321"
+    },
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 1,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "eval_batch_size":1,
+    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
+    "gradual_training": [[0, 7, 4]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
+    "loss_masking": true,         // enable / disable loss masking against the sequence padding.
+    "ga_alpha": 10.0,        // weight for guided attention loss. If > 0, guided attention is enabled.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 0,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "noam_schedule": false,        // use noam warmup and lr schedule.
+    "grad_clip": 1.0,              // upper limit for gradients for clipping.
+    "epochs": 1,                // total number of epochs to train.
+    "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "wd": 0.000001,                // Weight decay weight.
+    "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "seq_len_norm": false,         // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
+
+    // TACOTRON PRENET
+    "memory_size": -1,              // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
+    "prenet_type": "bn",            // "original" or "bn".
+    "prenet_dropout": false,        // enable/disable dropout at prenet.
+
+    // TACOTRON ATTENTION
+    "attention_type": "original",  // 'original' or 'graves'
+    "attention_heads": 4,          // number of attention heads (only for 'graves')
+    "attention_norm": "sigmoid",   // softmax or sigmoid.
+    "windowing": false,            // Enables attention windowing. Used only in eval mode.
+    "use_forward_attn": false,     // if it uses forward attention. In general, it aligns faster.
+    "forward_attn_mask": false,    // Additional masking forcing monotonicity only in eval mode.
+    "transition_agent": false,     // enable/disable transition agent of forward attention.
+    "location_attn": true,         // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
+    "bidirectional_decoder": false,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
+    "double_decoder_consistency": true,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
+    "ddc_r": 7,                           // reduction rate for coarse decoder.
+
+    // STOPNET
+    "stopnet": true,               // Train stopnet predicting the end of synthesis.
+    "separate_stopnet": true,      // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
+
+    // TENSORBOARD and LOGGING
+    "print_step": 1,       // Number of steps to log training on console.
+    "tb_plot_step": 100,    // Number of steps to plot TB training figures.
+    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
+    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "text_cleaner": "phoneme_cleaners",
+    "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "batch_group_size": 0,  //Number of batches to shuffle after bucketing.
+    "min_seq_len": 6,       // DATASET-RELATED: minimum text length to use in training
+    "max_seq_len": 153,     // DATASET-RELATED: maximum text length
+
+    // PATHS
+    "output_path": "tests/train_outputs/",
+
+    // PHONEMES
+    "phoneme_cache_path": "tests/train_outputs/phoneme_cache/",  // phoneme computation is slow, therefore, it caches results in the given folder.
+    "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
+    "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
+
+    // MULTI-SPEAKER and GST
+    "use_speaker_embedding": false,     // use speaker embedding to enable multi-speaker learning.
+    "style_wav_for_test": null,          // path to style wav file to be used in TacotronGST inference.
+    "use_gst": false,       // TACOTRON ONLY: use global style tokens
+
+    // DATASETS
+    "train_portion": 0.1,  // dataset portion used for training. It is mainly for internal experiments.
+    "eval_portion": 0.1,   // dataset portion used for training. It is mainly for internal experiments.
+    "datasets":   // List of datasets. They all merged and they get different speaker_ids.
+        [
+            {
+                "name": "ljspeech",
+                "path": "tests/data/ljspeech/",
+                "meta_file_train": "metadata.csv",
+                "meta_file_val": "metadata.csv"
+            }
+        ]
+
+}
+
diff --git a/vocoder/tests/test_config.json b/tests/inputs/test_vocoder_audio_config.json
similarity index 100%
rename from vocoder/tests/test_config.json
rename to tests/inputs/test_vocoder_audio_config.json
diff --git a/vocoder/configs/multiband_melgan_config.json b/tests/inputs/test_vocoder_multiband_melgan_config.json
similarity index 89%
rename from vocoder/configs/multiband_melgan_config.json
rename to tests/inputs/test_vocoder_multiband_melgan_config.json
index a89d43bb..c0f552a4 100644
--- a/vocoder/configs/multiband_melgan_config.json
+++ b/tests/inputs/test_vocoder_multiband_melgan_config.json
@@ -31,7 +31,7 @@
         "symmetric_norm": true, // move normalization to range [-1, 1]
         "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
         "clip_norm": true,      // clip normalized values into the range.
-        "stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy"    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
     },
 
     // DISTRIBUTED TRAINING
@@ -90,7 +90,7 @@
     },
 
     // DATASET
-    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
+    "data_path": "tests/data/ljspeech/wavs/",
     "feature_path": null,
     "seq_len": 16384,
     "pad_short": 2000,
@@ -101,7 +101,7 @@
     "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
 
     // TRAINING
-    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "batch_size": 4,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
 
     // VALIDATION
     "run_eval": true,
@@ -109,7 +109,7 @@
     "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
 
     // OPTIMIZER
-    "epochs": 10000,                // total number of epochs to train.
+    "epochs": 1,                // total number of epochs to train.
     "wd": 0.0,                // Weight decay weight.
     "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
     "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
@@ -127,7 +127,7 @@
     "lr_disc": 1e-4,
 
     // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log traning on console.
+    "print_step": 1,       // Number of steps to log traning on console.
     "print_eval": false,     // If True, it prints loss values for each step in eval run.
     "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"
@@ -139,6 +139,6 @@
     "eval_split_size": 10,
 
     // PATHS
-    "output_path": "/home/erogol/Models/LJSpeech/"
+    "output_path": "tests/outputs/train_outputs/"
 }
 
diff --git a/tests/outputs/dummy_model_config.json b/tests/outputs/dummy_model_config.json
deleted file mode 100644
index 36fac3e5..00000000
--- a/tests/outputs/dummy_model_config.json
+++ /dev/null
@@ -1,88 +0,0 @@
-{
-    "run_name": "mozilla-no-loc-fattn-stopnet-sigmoid-loss_masking",
-    "run_description": "using forward attention, with original prenet, loss masking,separate stopnet, sigmoid. Compare this with 4817. Pytorch DPP",
-
-    "audio":{
-        // Audio processing parameters
-        "num_mels": 80,         // size of the mel spec frame.
-        "fft_size": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "hop_length": 256,
-        "win_length": 1024,
-        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "min_level_db": -100,   // normalization range
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-        // Normalization parameters
-        "signal_norm": true,    // normalize the spec values in range [0, 1]
-        "symmetric_norm": false, // move normalization to range [-1, 1]
-        "max_norm": 1,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
-        "do_trim_silence": true  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-    },
-
-    "distributed":{
-        "backend": "nccl",
-        "url": "tcp:\/\/localhost:54321"
-    },
-
-    "reinit_layers": [],
-
-    "model": "Tacotron2",          // one of the model in models/
-    "grad_clip": 1,                // upper limit for gradients for clipping.
-    "epochs": 1000,                // total number of epochs to train.
-    "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_decay": false,             // if true, Noam learning rate decaying is applied through training.
-    "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "windowing": false,            // Enables attention windowing. Used only in eval mode.
-    "memory_size": 5,              // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
-    "attention_norm": "sigmoid",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
-    "prenet_type": "original",     // ONLY TACOTRON2 - "original" or "bn".
-    "prenet_dropout": true,        // ONLY TACOTRON2 - enable/disable dropout at prenet.
-    "use_forward_attn": true,      // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
-    "forward_attn_mask": false,
-    "attention_type": "original",
-    "attention_heads": 5,
-    "bidirectional_decoder": false,
-    "transition_agent": false,     // ONLY TACOTRON2 - enable/disable transition agent of forward attention.
-    "location_attn": false,        // ONLY TACOTRON2 - enable_disable location sensitive attention. It is enabled for TACOTRON by default.
-    "loss_masking": true,         // enable / disable loss masking against the sequence padding.
-    "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
-    "stopnet": true,               // Train stopnet predicting the end of synthesis.
-    "separate_stopnet": true,     // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
-    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-    "use_gst": false,
-    "double_decoder_consistency": true,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
-    "ddc_r": 7,                           // reduction rate for coarse decoder.
-
-    "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
-    "eval_batch_size":16,
-    "r": 1,                 // Number of frames to predict for step.
-    "wd": 0.000001,         // Weight decay weight.
-    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
-    "save_step": 1000,      // Number of training steps expected to save traning stats and checkpoints.
-    "print_step": 10,       // Number of steps to log traning on console.
-    "batch_group_size": 0,  //Number of batches to shuffle after bucketing.
-
-    "run_eval": true,
-    "test_delay_epochs": 5,  //Until attention is aligned, testing only wastes computation time.
-    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-    "data_path": "/media/erogol/data_ssd/Data/Mozilla/",  // DATASET-RELATED: can overwritten from command argument
-    "meta_file_train": "metadata_train.txt",      // DATASET-RELATED: metafile for training dataloader.
-    "meta_file_val": "metadata_val.txt",    // DATASET-RELATED: metafile for evaluation dataloader.
-    "dataset": "mozilla",      // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
-    "min_seq_len": 0,       // DATASET-RELATED: minimum text length to use in training
-    "max_seq_len": 150,     // DATASET-RELATED: maximum text length
-    "output_path": "../keep/",      // DATASET-RELATED: output path for all training outputs.
-    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
-    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
-    "phoneme_cache_path": "mozilla_us_phonemes",  // phoneme computation is slow, therefore, it caches results in the given folder.
-    "use_phonemes": false,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
-    "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
-    "text_cleaner": "phoneme_cleaners",
-    "use_speaker_embedding": false // whether to use additional embeddings for separate speakers
-}
-
diff --git a/tests/symbols_tests.py b/tests/symbols_tests.py
index 4c32c7d6..87ec4a8a 100644
--- a/tests/symbols_tests.py
+++ b/tests/symbols_tests.py
@@ -1,6 +1,6 @@
 import unittest
 
-from TTS.utils.text import phonemes
+from TTS.tts.utils.text import phonemes
 
 class SymbolsTest(unittest.TestCase):
     def test_uniqueness(self):  #pylint: disable=no-self-use
diff --git a/tests/test_audio.py b/tests/test_audio.py
index 4b8ee276..a4d69de5 100644
--- a/tests/test_audio.py
+++ b/tests/test_audio.py
@@ -1,7 +1,7 @@
 import os
 import unittest
 
-from TTS.tests import get_tests_path, get_tests_input_path, get_tests_output_path
+from tests import get_tests_path, get_tests_input_path, get_tests_output_path
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.io import load_config
 
@@ -10,7 +10,7 @@ OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests")
 WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
 
 os.makedirs(OUT_PATH, exist_ok=True)
-conf = load_config(os.path.join(TESTS_PATH, 'test_config.json'))
+conf = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
 
 # pylint: disable=protected-access
diff --git a/tests/test_demo_server.py b/tests/test_demo_server.py
index 5404304b..e8a86094 100644
--- a/tests/test_demo_server.py
+++ b/tests/test_demo_server.py
@@ -4,10 +4,11 @@ import unittest
 import torch as T
 
 from TTS.server.synthesizer import Synthesizer
-from TTS.tests import get_tests_input_path, get_tests_output_path
-from TTS.utils.text.symbols import make_symbols, phonemes, symbols
-from TTS.utils.generic_utils import setup_model
-from TTS.utils.io import load_config, save_checkpoint
+from tests import get_tests_input_path, get_tests_output_path
+from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
+from TTS.tts.utils.generic_utils import setup_model
+from TTS.tts.utils.io import save_checkpoint
+from TTS.utils.io import load_config
 
 
 class DemoServerTest(unittest.TestCase):
diff --git a/speaker_encoder/tests.py b/tests/test_encoder.py
similarity index 97%
rename from speaker_encoder/tests.py
rename to tests/test_encoder.py
index 039833fc..c713a1f1 100644
--- a/speaker_encoder/tests.py
+++ b/tests/test_encoder.py
@@ -2,12 +2,13 @@ import os
 import unittest
 import torch as T
 
+from tests import get_tests_path, get_tests_input_path
 from TTS.speaker_encoder.model import SpeakerEncoder
 from TTS.speaker_encoder.loss import GE2ELoss
 from TTS.utils.io import load_config
 
 
-file_path = os.path.dirname(os.path.realpath(__file__)) + "/../tests/"
+file_path = get_tests_input_path()
 c = load_config(os.path.join(file_path, "test_config.json"))
 
 
diff --git a/tests/test_layers.py b/tests/test_layers.py
index d7c8829f..e9a36e35 100644
--- a/tests/test_layers.py
+++ b/tests/test_layers.py
@@ -1,9 +1,9 @@
 import unittest
 import torch as T
 
-from TTS.layers.tacotron import Prenet, CBHG, Decoder, Encoder
-from TTS.layers.losses import L1LossMasked
-from TTS.utils.generic_utils import sequence_mask
+from TTS.tts.layers.tacotron import Prenet, CBHG, Decoder, Encoder
+from TTS.tts.layers.losses import L1LossMasked
+from TTS.tts.utils.generic_utils import sequence_mask
 
 # pylint: disable=unused-variable
 
diff --git a/tests/test_loader.py b/tests/test_loader.py
index 52d24c7a..978b29b7 100644
--- a/tests/test_loader.py
+++ b/tests/test_loader.py
@@ -4,18 +4,18 @@ import shutil
 import torch
 import numpy as np
 
+from tests import get_tests_path, get_tests_input_path, get_tests_output_path
 from torch.utils.data import DataLoader
 from TTS.utils.io import load_config
 from TTS.utils.audio import AudioProcessor
-from TTS.datasets import TTSDataset
-from TTS.datasets.preprocess import ljspeech
+from TTS.tts.datasets import TTSDataset
+from TTS.tts.datasets.preprocess import ljspeech
 
 #pylint: disable=unused-variable
 
-file_path = os.path.dirname(os.path.realpath(__file__))
-OUTPATH = os.path.join(file_path, "outputs/loader_tests/")
+OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
 os.makedirs(OUTPATH, exist_ok=True)
-c = load_config(os.path.join(file_path, 'test_config.json'))
+c = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 ok_ljspeech = os.path.exists(c.data_path)
 
 DATA_EXIST = True
diff --git a/tests/test_preprocessors.py b/tests/test_preprocessors.py
index 993ee495..56f79402 100644
--- a/tests/test_preprocessors.py
+++ b/tests/test_preprocessors.py
@@ -1,8 +1,8 @@
 import unittest
 import os
-from TTS.tests import get_tests_input_path
+from tests import get_tests_input_path
 
-from TTS.datasets.preprocess import common_voice
+from TTS.tts.datasets.preprocess import common_voice
 
 
 class TestPreprocessors(unittest.TestCase):
diff --git a/__init__.py b/tests/test_stft_torch.py
similarity index 100%
rename from __init__.py
rename to tests/test_stft_torch.py
diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index ae9f20a2..41b5f039 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -1,14 +1,14 @@
-import os
 import copy
-import torch
+import os
 import unittest
-import numpy as np
 
-from torch import optim
-from torch import nn
+import torch
+from tests import get_tests_input_path
+from torch import nn, optim
+
+from TTS.tts.layers.losses import MSELossMasked
+from TTS.tts.models.tacotron2 import Tacotron2
 from TTS.utils.io import load_config
-from TTS.layers.losses import MSELossMasked
-from TTS.models.tacotron2 import Tacotron2
 
 #pylint: disable=unused-variable
 
@@ -16,8 +16,7 @@ torch.manual_seed(1)
 use_cuda = torch.cuda.is_available()
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-file_path = os.path.dirname(os.path.realpath(__file__))
-c = load_config(os.path.join(file_path, 'test_config.json'))
+c = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
 
 class TacotronTrainTest(unittest.TestCase):
diff --git a/tf/tests/test_tacotron2_tf_model.py b/tests/test_tacotron2_tf_model.py
similarity index 95%
rename from tf/tests/test_tacotron2_tf_model.py
rename to tests/test_tacotron2_tf_model.py
index 03db194a..472c1ebf 100644
--- a/tf/tests/test_tacotron2_tf_model.py
+++ b/tests/test_tacotron2_tf_model.py
@@ -5,9 +5,11 @@ import numpy as np
 import tensorflow as tf
 tf.get_logger().setLevel('INFO')
 
+from tests import get_tests_path, get_tests_input_path, get_tests_output_path
+
 from TTS.utils.io import load_config
-from TTS.tf.models.tacotron2 import Tacotron2
-from TTS.tf.utils.tflite import convert_tacotron2_to_tflite, load_tflite_model
+from TTS.tts.tf.models.tacotron2 import Tacotron2
+from TTS.tts.tf.utils.tflite import convert_tacotron2_to_tflite, load_tflite_model
 
 #pylint: disable=unused-variable
 
@@ -15,8 +17,7 @@ torch.manual_seed(1)
 use_cuda = torch.cuda.is_available()
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-file_path = os.path.dirname(os.path.realpath(__file__)).replace('/tf/', '/')
-c = load_config(os.path.join(file_path, 'test_config.json'))
+c = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
 
 class TacotronTFTrainTest(unittest.TestCase):
diff --git a/tests/test_tacotron_model.py b/tests/test_tacotron_model.py
index 2bbb3c8d..f8d4a4d7 100644
--- a/tests/test_tacotron_model.py
+++ b/tests/test_tacotron_model.py
@@ -1,13 +1,14 @@
-import os
 import copy
-import torch
+import os
 import unittest
 
-from torch import optim
-from torch import nn
+import torch
+from tests import get_tests_input_path
+from torch import nn, optim
+
+from TTS.tts.layers.losses import L1LossMasked
+from TTS.tts.models.tacotron import Tacotron
 from TTS.utils.io import load_config
-from TTS.layers.losses import L1LossMasked
-from TTS.models.tacotron import Tacotron
 
 #pylint: disable=unused-variable
 
@@ -15,8 +16,7 @@ torch.manual_seed(1)
 use_cuda = torch.cuda.is_available()
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-file_path = os.path.dirname(os.path.realpath(__file__))
-c = load_config(os.path.join(file_path, 'test_config.json'))
+c = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
 
 def count_parameters(model):
diff --git a/tests/test_text_processing.py b/tests/test_text_processing.py
index 93edabe7..992f0a17 100644
--- a/tests/test_text_processing.py
+++ b/tests/test_text_processing.py
@@ -3,12 +3,12 @@ import os
 # pylint: disable=wildcard-import
 # pylint: disable=unused-import
 import unittest
-from TTS.utils.text import *
-from TTS.tests import get_tests_path
+from tests import get_tests_input_path
+from TTS.tts.utils.text import *
+from tests import get_tests_path
 from TTS.utils.io import load_config
 
-TESTS_PATH = get_tests_path()
-conf = load_config(os.path.join(TESTS_PATH, 'test_config.json'))
+conf = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
 def test_phoneme_to_sequence():
     text = "Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase, the grey matter in the parts of the brain responsible for emotional regulation and learning!"
@@ -19,7 +19,7 @@ def test_phoneme_to_sequence():
     sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
     text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
     gt = "ɹiːsənt ɹɪsɜːtʃ æt hɑːɹvɚd hɐz ʃoʊn mɛdᵻteɪɾɪŋ fɔːɹ æz lɪɾəl æz eɪt wiːks kæn æktʃuːəli ɪnkɹiːs, ðə ɡɹeɪ mæɾɚɹ ɪnðə pɑːɹts ʌvðə bɹeɪn ɹɪspɑːnsəbəl fɔːɹ ɪmoʊʃənəl ɹɛɡjuːleɪʃən ænd lɜːnɪŋ!"
-    assert text_hat == text_hat_with_params == gt 
+    assert text_hat == text_hat_with_params == gt
 
     # multiple punctuations
     text = "Be a voice, not an! echo?"
diff --git a/datasets/__init__.py b/tests/test_train_tts.py
similarity index 100%
rename from datasets/__init__.py
rename to tests/test_train_tts.py
diff --git a/tests/test_tts_train.sh b/tests/test_tts_train.sh
new file mode 100755
index 00000000..55379a1e
--- /dev/null
+++ b/tests/test_tts_train.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+BASEDIR=$(dirname "$0")
+echo "$BASEDIR"
+# run training
+CUDA_VISIBLE_DEVICES="" python TTS/bin/train_tts.py --config_path $BASEDIR/inputs/test_train_config.json
+# find the training folder
+LATEST_FOLDER=$(ls $BASEDIR/train_outputs/| sort | tail -1)
+echo $LATEST_FOLDER
+# continue the previous training
+CUDA_VISIBLE_DEVICES=""  python TTS/bin/train_tts.py --continue_path $BASEDIR/train_outputs/$LATEST_FOLDER
+# remove all the outputs
+rm -rf $BASEDIR/train_outputs/
diff --git a/vocoder/tests/test_datasets.py b/tests/test_vocoder_datasets.py
similarity index 93%
rename from vocoder/tests/test_datasets.py
rename to tests/test_vocoder_datasets.py
index 43d0d3de..2a487d9a 100644
--- a/vocoder/tests/test_datasets.py
+++ b/tests/test_vocoder_datasets.py
@@ -1,24 +1,24 @@
 import os
+
 import numpy as np
+from tests import get_tests_path, get_tests_input_path, get_tests_output_path
 from torch.utils.data import DataLoader
 
-from TTS.vocoder.datasets.gan_dataset import GANDataset
-from TTS.vocoder.datasets.preprocess import load_wav_data
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.io import load_config
-
+from TTS.vocoder.datasets.gan_dataset import GANDataset
+from TTS.vocoder.datasets.preprocess import load_wav_data
 
 file_path = os.path.dirname(os.path.realpath(__file__))
-OUTPATH = os.path.join(file_path, "../../tests/outputs/loader_tests/")
+OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
 os.makedirs(OUTPATH, exist_ok=True)
 
-C = load_config(os.path.join(file_path, 'test_config.json'))
+C = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
-test_data_path = os.path.join(file_path, "../../tests/data/ljspeech/")
+test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
 ok_ljspeech = os.path.exists(test_data_path)
 
 
-
 def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, use_noise_augment, use_cache, num_workers):
     ''' run dataloader with given parameters and check conditions '''
     ap = AudioProcessor(**C.audio)
diff --git a/vocoder/tests/test_losses.py b/tests/test_vocoder_losses.py
similarity index 85%
rename from vocoder/tests/test_losses.py
rename to tests/test_vocoder_losses.py
index 68e42e89..965e68ad 100644
--- a/vocoder/tests/test_losses.py
+++ b/tests/test_vocoder_losses.py
@@ -1,11 +1,11 @@
 import os
+
 import torch
+from tests import get_tests_input_path, get_tests_output_path, get_tests_path
 
-from TTS.vocoder.layers.losses import TorchSTFT, STFTLoss, MultiScaleSTFTLoss
-
-from TTS.tests import get_tests_path, get_tests_input_path, get_tests_output_path
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.io import load_config
+from TTS.vocoder.layers.losses import MultiScaleSTFTLoss, STFTLoss, TorchSTFT
 
 TESTS_PATH = get_tests_path()
 
@@ -14,8 +14,7 @@ os.makedirs(OUT_PATH, exist_ok=True)
 
 WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
 
-file_path = os.path.dirname(os.path.realpath(__file__))
-C = load_config(os.path.join(file_path, 'test_config.json'))
+C = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 ap = AudioProcessor(**C.audio)
 
 
@@ -53,9 +52,3 @@ def test_multiscale_stft_loss():
     loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav))
     assert loss_sc < 1.0
     assert loss_m + loss_sc > 0
-
-
-
-
-
-
diff --git a/vocoder/tests/test_melgan_discriminator.py b/tests/test_vocoder_melgan_discriminator.py
similarity index 100%
rename from vocoder/tests/test_melgan_discriminator.py
rename to tests/test_vocoder_melgan_discriminator.py
diff --git a/vocoder/tests/test_melgan_generator.py b/tests/test_vocoder_melgan_generator.py
similarity index 100%
rename from vocoder/tests/test_melgan_generator.py
rename to tests/test_vocoder_melgan_generator.py
diff --git a/vocoder/tests/test_pqmf.py b/tests/test_vocoder_pqmf.py
similarity index 90%
rename from vocoder/tests/test_pqmf.py
rename to tests/test_vocoder_pqmf.py
index a26bdd59..8924fea8 100644
--- a/vocoder/tests/test_pqmf.py
+++ b/tests/test_vocoder_pqmf.py
@@ -4,7 +4,7 @@ import torch
 import soundfile as sf
 from librosa.core import load
 
-from TTS.tests import get_tests_path, get_tests_input_path
+from tests import get_tests_path, get_tests_input_path
 from TTS.vocoder.layers.pqmf import PQMF
 
 
diff --git a/vocoder/tests/test_rwd.py b/tests/test_vocoder_rwd.py
similarity index 100%
rename from vocoder/tests/test_rwd.py
rename to tests/test_vocoder_rwd.py
diff --git a/tests/test_vocoder_train.sh b/tests/test_vocoder_train.sh
new file mode 100755
index 00000000..b4a9b9fa
--- /dev/null
+++ b/tests/test_vocoder_train.sh
@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+BASEDIR=$(dirname "$0")
+echo "$BASEDIR"
+# create run dir
+mkdir $BASEDIR/train_outputs
+# run training
+CUDA_VISIBLE_DEVICES="" python TTS/bin/train_vocoder.py --config_path $BASEDIR/inputs/test_vocoder_multiband_melgan_config.json
+# find the training folder
+LATEST_FOLDER=$(ls $BASEDIR/outputs/train_outputs/| sort | tail -1)
+echo $LATEST_FOLDER
+# continue the previous training
+CUDA_VISIBLE_DEVICES=""  python TTS/bin/train_vocoder.py --continue_path $BASEDIR/outputs/train_outputs/$LATEST_FOLDER
+# remove all the outputs
+rm -rf $BASEDIR/train_outputs/
diff --git a/tf/README.md b/tf/README.md
deleted file mode 100644
index 0f9d58e9..00000000
--- a/tf/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
-## Utilities to Convert Models to Tensorflow2
-Here there are experimental utilities to convert trained Torch models to Tensorflow (2.2>=). 
-
-Converting Torch models to TF enables all the TF toolkit to be used for better deployment and device specific optimizations.
-
-Note that we do not plan to share training scripts for Tensorflow in near future. But any contribution in that direction would be more than welcome.
-
-To see how you can use TF model at inference, check the notebook.
-
-This is an experimental release. If you encounter an error, please put an issue or in the best send a PR but you are mostly on your own.
-
-
-### Converting a Model
-- Run ```convert_tacotron2_torch_to_tf.py --torch_model_path /path/to/torch/model.pth.tar --config_path /path/to/model/config.json --output_path /path/to/output/tf/model``` with the right arguments.
-
-### Known issues ans limitations
-- We use a custom model load/save mechanism which enables us to store model related information with models weights. (Similar to Torch). However, it is prone to random errors.
-- Current TF model implementation is slightly slower than Torch model. Hopefully, it'll get better with improving TF support for eager mode and ```tf.function```.
-- TF implementation of Tacotron2 only supports regular Tacotron2 as in the paper.
-- You can only convert models trained after TF model implementation since model layers has been updated in Torch model.
diff --git a/tf/__init__.py b/tf/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tf/convert_tacotron2_tflite.py b/tf/convert_tacotron2_tflite.py
deleted file mode 100644
index fc46cc79..00000000
--- a/tf/convert_tacotron2_tflite.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Convert Tensorflow Tacotron2 model to TF-Lite binary
-
-import argparse
-
-from TTS.utils.io import load_config
-from TTS.utils.text.symbols import symbols, phonemes
-from TTS.tf.utils.generic_utils import setup_model
-from TTS.tf.utils.io import load_checkpoint
-from TTS.tf.utils.tflite import convert_tacotron2_to_tflite
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--tf_model',
-                    type=str,
-                    help='Path to target torch model to be converted to TF.')
-parser.add_argument('--config_path',
-                    type=str,
-                    help='Path to config file of torch model.')
-parser.add_argument('--output_path',
-                    type=str,
-                    help='path to tflite output binary.')
-args = parser.parse_args()
-
-# Set constants
-CONFIG = load_config(args.config_path)
-
-# load the model
-c = CONFIG
-num_speakers = 0
-num_chars = len(phonemes) if c.use_phonemes else len(symbols)
-model = setup_model(num_chars, num_speakers, c, enable_tflite=True)
-model.build_inference()
-model = load_checkpoint(model, args.tf_model)
-model.decoder.set_max_decoder_steps(1000)
-
-# create tflite model
-tflite_model = convert_tacotron2_to_tflite(model, output_path=args.output_path)
\ No newline at end of file
diff --git a/tf/convert_tacotron2_torch_to_tf.py b/tf/convert_tacotron2_torch_to_tf.py
deleted file mode 100644
index dfc42250..00000000
--- a/tf/convert_tacotron2_torch_to_tf.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# %%
-import sys
-sys.path.append('/home/erogol/Projects')
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = ''
-# %%
-import argparse
-import numpy as np
-import torch
-import tensorflow as tf
-from fuzzywuzzy import fuzz
-
-from TTS.utils.text.symbols import phonemes, symbols
-from TTS.utils.generic_utils import setup_model
-from TTS.utils.io import load_config
-from TTS.tf.models.tacotron2 import Tacotron2
-from TTS.tf.utils.convert_torch_to_tf_utils import compare_torch_tf, tf_create_dummy_inputs, transfer_weights_torch_to_tf, convert_tf_name
-from TTS.tf.utils.generic_utils import save_checkpoint
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--torch_model_path',
-                    type=str,
-                    help='Path to target torch model to be converted to TF.')
-parser.add_argument('--config_path',
-                    type=str,
-                    help='Path to config file of torch model.')
-parser.add_argument('--output_path',
-                    type=str,
-                    help='path to output file including file name to save TF model.')
-args = parser.parse_args()
-
-# load model config
-config_path = args.config_path
-c = load_config(config_path)
-num_speakers = 0
-
-# init torch model
-num_chars = len(phonemes) if c.use_phonemes else len(symbols)
-model = setup_model(num_chars, num_speakers, c)
-checkpoint = torch.load(args.torch_model_path,
-                        map_location=torch.device('cpu'))
-state_dict = checkpoint['model']
-model.load_state_dict(state_dict)
-
-# init tf model
-model_tf = Tacotron2(num_chars=num_chars,
-                     num_speakers=num_speakers,
-                     r=model.decoder.r,
-                     postnet_output_dim=c.audio['num_mels'],
-                     decoder_output_dim=c.audio['num_mels'],
-                     attn_type=c.attention_type,
-                     attn_win=c.windowing,
-                     attn_norm=c.attention_norm,
-                     prenet_type=c.prenet_type,
-                     prenet_dropout=c.prenet_dropout,
-                     forward_attn=c.use_forward_attn,
-                     trans_agent=c.transition_agent,
-                     forward_attn_mask=c.forward_attn_mask,
-                     location_attn=c.location_attn,
-                     attn_K=c.attention_heads,
-                     separate_stopnet=c.separate_stopnet,
-                     bidirectional_decoder=c.bidirectional_decoder)
-
-# set initial layer mapping - these are not captured by the below heuristic approach
-# TODO: set layer names so that we can remove these manual matching
-common_sufix = '/.ATTRIBUTES/VARIABLE_VALUE'
-var_map = [
-    ('embedding/embeddings:0', 'embedding.weight'),
-    ('encoder/lstm/forward_lstm/lstm_cell_1/kernel:0',
-     'encoder.lstm.weight_ih_l0'),
-    ('encoder/lstm/forward_lstm/lstm_cell_1/recurrent_kernel:0',
-     'encoder.lstm.weight_hh_l0'),
-    ('encoder/lstm/backward_lstm/lstm_cell_2/kernel:0',
-     'encoder.lstm.weight_ih_l0_reverse'),
-    ('encoder/lstm/backward_lstm/lstm_cell_2/recurrent_kernel:0',
-     'encoder.lstm.weight_hh_l0_reverse'),
-    ('encoder/lstm/forward_lstm/lstm_cell_1/bias:0',
-     ('encoder.lstm.bias_ih_l0', 'encoder.lstm.bias_hh_l0')),
-    ('encoder/lstm/backward_lstm/lstm_cell_2/bias:0',
-     ('encoder.lstm.bias_ih_l0_reverse', 'encoder.lstm.bias_hh_l0_reverse')),
-    ('attention/v/kernel:0', 'decoder.attention.v.linear_layer.weight'),
-    ('decoder/linear_projection/kernel:0',
-     'decoder.linear_projection.linear_layer.weight'),
-    ('decoder/stopnet/kernel:0', 'decoder.stopnet.1.linear_layer.weight')
-]
-
-# %%
-# get tf_model graph
-mel_pred = model_tf.build_inference()
-
-# get tf variables
-tf_vars = model_tf.weights
-
-# match variable names with fuzzy logic
-torch_var_names = list(state_dict.keys())
-tf_var_names = [we.name for we in model_tf.weights]
-for tf_name in tf_var_names:
-    # skip re-mapped layer names
-    if tf_name in [name[0] for name in var_map]:
-        continue
-    tf_name_edited = convert_tf_name(tf_name)
-    ratios = [
-        fuzz.ratio(torch_name, tf_name_edited)
-        for torch_name in torch_var_names
-    ]
-    max_idx = np.argmax(ratios)
-    matching_name = torch_var_names[max_idx]
-    del torch_var_names[max_idx]
-    var_map.append((tf_name, matching_name))
-
-# %%
-# print variable match
-from pprint import pprint
-pprint(var_map)
-pprint(torch_var_names)
-
-# pass weights
-tf_vars = transfer_weights_torch_to_tf(tf_vars, dict(var_map), state_dict)
-
-# Compare TF and TORCH models
-# %%
-# check embedding outputs
-model.eval()
-input_ids = torch.randint(0, 24, (1, 128)).long()
-
-o_t = model.embedding(input_ids)
-o_tf = model_tf.embedding(input_ids.detach().numpy())
-assert abs(o_t.detach().numpy() -
-           o_tf.numpy()).sum() < 1e-5, abs(o_t.detach().numpy() -
-                                           o_tf.numpy()).sum()
-
-# compare encoder outputs
-oo_en = model.encoder.inference(o_t.transpose(1, 2))
-ooo_en = model_tf.encoder(o_t.detach().numpy(), training=False)
-assert compare_torch_tf(oo_en, ooo_en) < 1e-5
-
-#pylint: disable=redefined-builtin
-# compare decoder.attention_rnn
-inp = torch.rand([1, 768])
-inp_tf = inp.numpy()
-model.decoder._init_states(oo_en, mask=None)  #pylint: disable=protected-access
-output, cell_state = model.decoder.attention_rnn(inp)
-states = model_tf.decoder.build_decoder_initial_states(1, 512, 128)
-output_tf, memory_state = model_tf.decoder.attention_rnn(inp_tf,
-                                                         states[2],
-                                                         training=False)
-assert compare_torch_tf(output, output_tf).mean() < 1e-5
-
-query = output
-inputs = torch.rand([1, 128, 512])
-query_tf = query.detach().numpy()
-inputs_tf = inputs.numpy()
-
-# compare decoder.attention
-model.decoder.attention.init_states(inputs)
-processes_inputs = model.decoder.attention.preprocess_inputs(inputs)
-loc_attn, proc_query = model.decoder.attention.get_location_attention(
-    query, processes_inputs)
-context = model.decoder.attention(query, inputs, processes_inputs, None)
-
-attention_states = model_tf.decoder.build_decoder_initial_states(1, 512, 128)[-1]
-model_tf.decoder.attention.process_values(tf.convert_to_tensor(inputs_tf))
-loc_attn_tf, proc_query_tf = model_tf.decoder.attention.get_loc_attn(query_tf, attention_states)
-context_tf, attention, attention_states = model_tf.decoder.attention(query_tf, attention_states, training=False)
-
-assert compare_torch_tf(loc_attn, loc_attn_tf).mean() < 1e-5
-assert compare_torch_tf(proc_query, proc_query_tf).mean() < 1e-5
-assert compare_torch_tf(context, context_tf) < 1e-5
-
-# compare decoder.decoder_rnn
-input = torch.rand([1, 1536])
-input_tf = input.numpy()
-model.decoder._init_states(oo_en, mask=None)  #pylint: disable=protected-access
-output, cell_state = model.decoder.decoder_rnn(
-    input, [model.decoder.decoder_hidden, model.decoder.decoder_cell])
-states = model_tf.decoder.build_decoder_initial_states(1, 512, 128)
-output_tf, memory_state = model_tf.decoder.decoder_rnn(input_tf,
-                                                       states[3],
-                                                       training=False)
-assert abs(input - input_tf).mean() < 1e-5
-assert compare_torch_tf(output, output_tf).mean() < 1e-5
-
-# compare decoder.linear_projection
-input = torch.rand([1, 1536])
-input_tf = input.numpy()
-output = model.decoder.linear_projection(input)
-output_tf = model_tf.decoder.linear_projection(input_tf, training=False)
-assert compare_torch_tf(output, output_tf) < 1e-5
-
-# compare decoder outputs
-model.decoder.max_decoder_steps = 100
-model_tf.decoder.set_max_decoder_steps(100)
-output, align, stop = model.decoder.inference(oo_en)
-states = model_tf.decoder.build_decoder_initial_states(1, 512, 128)
-output_tf, align_tf, stop_tf = model_tf.decoder(ooo_en, states, training=False)
-assert compare_torch_tf(output.transpose(1, 2), output_tf) < 1e-4
-
-# compare the whole model output
-outputs_torch = model.inference(input_ids)
-outputs_tf = model_tf(tf.convert_to_tensor(input_ids.numpy()))
-print(abs(outputs_torch[0].numpy()[:, 0] - outputs_tf[0].numpy()[:, 0]).mean())
-assert compare_torch_tf(outputs_torch[2][:, 50, :],
-                        outputs_tf[2][:, 50, :]) < 1e-5
-assert compare_torch_tf(outputs_torch[0], outputs_tf[0]) < 1e-4
-
-# %%
-# save tf model
-save_checkpoint(model_tf, None, checkpoint['step'], checkpoint['epoch'],
-                checkpoint['r'], args.output_path)
-print(' > Model conversion is successfully completed :).')
diff --git a/tf/layers/common_layers.py b/tf/layers/common_layers.py
deleted file mode 100644
index f2353a93..00000000
--- a/tf/layers/common_layers.py
+++ /dev/null
@@ -1,285 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.python.ops import math_ops
-# from tensorflow_addons.seq2seq import BahdanauAttention
-
-
-class Linear(keras.layers.Layer):
-    def __init__(self, units, use_bias, **kwargs):
-        super(Linear, self).__init__(**kwargs)
-        self.linear_layer = keras.layers.Dense(units, use_bias=use_bias, name='linear_layer')
-        self.activation = keras.layers.ReLU()
-
-    def call(self, x):
-        """
-        shapes:
-            x: B x T x C
-        """
-        return self.activation(self.linear_layer(x))
-
-
-class LinearBN(keras.layers.Layer):
-    def __init__(self, units, use_bias, **kwargs):
-        super(LinearBN, self).__init__(**kwargs)
-        self.linear_layer = keras.layers.Dense(units, use_bias=use_bias, name='linear_layer')
-        self.batch_normalization = keras.layers.BatchNormalization(axis=-1, momentum=0.90, epsilon=1e-5, name='batch_normalization')
-        self.activation = keras.layers.ReLU()
-
-    def call(self, x, training=None):
-        """
-        shapes:
-            x: B x T x C
-        """
-        out = self.linear_layer(x)
-        out = self.batch_normalization(out, training=training)
-        return self.activation(out)
-
-
-class Prenet(keras.layers.Layer):
-    def __init__(self,
-                 prenet_type,
-                 prenet_dropout,
-                 units,
-                 bias,
-                 **kwargs):
-        super(Prenet, self).__init__(**kwargs)
-        self.prenet_type = prenet_type
-        self.prenet_dropout = prenet_dropout
-        self.linear_layers = []
-        if prenet_type == "bn":
-            self.linear_layers += [LinearBN(unit, use_bias=bias, name=f'linear_layer_{idx}') for idx, unit in enumerate(units)]
-        elif prenet_type == "original":
-            self.linear_layers += [Linear(unit, use_bias=bias, name=f'linear_layer_{idx}') for idx, unit in enumerate(units)]
-        else:
-            raise RuntimeError(' [!] Unknown prenet type.')
-        if prenet_dropout:
-            self.dropout = keras.layers.Dropout(rate=0.5)
-
-    def call(self, x, training=None):
-        """
-        shapes:
-            x: B x T x C
-        """
-        for linear in self.linear_layers:
-            if self.prenet_dropout:
-                x = self.dropout(linear(x), training=training)
-            else:
-                x = linear(x)
-        return x
-
-
-def _sigmoid_norm(score):
-    attn_weights = tf.nn.sigmoid(score)
-    attn_weights = attn_weights / tf.reduce_sum(attn_weights, axis=1, keepdims=True)
-    return attn_weights
-
-
-class Attention(keras.layers.Layer):
-    """TODO: implement forward_attention
-    TODO: location sensitive attention
-    TODO: implement attention windowing """
-    def __init__(self, attn_dim, use_loc_attn, loc_attn_n_filters,
-                 loc_attn_kernel_size, use_windowing, norm, use_forward_attn,
-                 use_trans_agent, use_forward_attn_mask, **kwargs):
-        super(Attention, self).__init__(**kwargs)
-        self.use_loc_attn = use_loc_attn
-        self.loc_attn_n_filters = loc_attn_n_filters
-        self.loc_attn_kernel_size = loc_attn_kernel_size
-        self.use_windowing = use_windowing
-        self.norm = norm
-        self.use_forward_attn = use_forward_attn
-        self.use_trans_agent = use_trans_agent
-        self.use_forward_attn_mask = use_forward_attn_mask
-        self.query_layer = tf.keras.layers.Dense(attn_dim, use_bias=False, name='query_layer/linear_layer')
-        self.inputs_layer = tf.keras.layers.Dense(attn_dim, use_bias=False, name=f'{self.name}/inputs_layer/linear_layer')
-        self.v = tf.keras.layers.Dense(1, use_bias=True, name='v/linear_layer')
-        if use_loc_attn:
-            self.location_conv1d = keras.layers.Conv1D(
-                filters=loc_attn_n_filters,
-                kernel_size=loc_attn_kernel_size,
-                padding='same',
-                use_bias=False,
-                name='location_layer/location_conv1d')
-            self.location_dense = keras.layers.Dense(attn_dim, use_bias=False, name='location_layer/location_dense')
-        if norm == 'softmax':
-            self.norm_func = tf.nn.softmax
-        elif norm == 'sigmoid':
-            self.norm_func = _sigmoid_norm
-        else:
-            raise ValueError("Unknown value for attention norm type")
-
-    def init_states(self, batch_size, value_length):
-        states = []
-        if self.use_loc_attn:
-            attention_cum = tf.zeros([batch_size, value_length])
-            attention_old = tf.zeros([batch_size, value_length])
-            states = [attention_cum, attention_old]
-        if self.use_forward_attn:
-            alpha = tf.concat([
-                tf.ones([batch_size, 1]),
-                tf.zeros([batch_size, value_length])[:, :-1] + 1e-7
-            ], 1)
-            states.append(alpha)
-        return tuple(states)
-
-    def process_values(self, values):
-        """ cache values for decoder iterations """
-        #pylint: disable=attribute-defined-outside-init
-        self.processed_values = self.inputs_layer(values)
-        self.values = values
-
-    def get_loc_attn(self, query, states):
-        """ compute location attention, query layer and
-        unnorm. attention weights"""
-        attention_cum, attention_old = states[:2]
-        attn_cat = tf.stack([attention_old, attention_cum], axis=2)
-
-        processed_query = self.query_layer(tf.expand_dims(query, 1))
-        processed_attn = self.location_dense(self.location_conv1d(attn_cat))
-        score = self.v(
-            tf.nn.tanh(self.processed_values + processed_query +
-                       processed_attn))
-        score = tf.squeeze(score, axis=2)
-        return score, processed_query
-
-    def get_attn(self, query):
-        """ compute query layer and unnormalized attention weights """
-        processed_query = self.query_layer(tf.expand_dims(query, 1))
-        score = self.v(tf.nn.tanh(self.processed_values + processed_query))
-        score = tf.squeeze(score, axis=2)
-        return score, processed_query
-
-    def apply_score_masking(self, score, mask):  #pylint: disable=no-self-use
-        """ ignore sequence paddings """
-        padding_mask = tf.expand_dims(math_ops.logical_not(mask), 2)
-        # Bias so padding positions do not contribute to attention distribution.
-        score -= 1.e9 * math_ops.cast(padding_mask, dtype=tf.float32)
-        return score
-
-    def apply_forward_attention(self, alignment, alpha):  #pylint: disable=no-self-use
-        # forward attention
-        fwd_shifted_alpha = tf.pad(alpha[:, :-1], ((0, 0), (1, 0)), constant_values=0.0)
-        # compute transition potentials
-        new_alpha = ((1 - 0.5) * alpha + 0.5 * fwd_shifted_alpha + 1e-8) * alignment
-        # renormalize attention weights
-        new_alpha = new_alpha / tf.reduce_sum(new_alpha, axis=1, keepdims=True)
-        return new_alpha
-
-    def update_states(self, old_states, scores_norm, attn_weights, new_alpha=None):
-        states = []
-        if self.use_loc_attn:
-            states = [old_states[0] + scores_norm, attn_weights]
-        if self.use_forward_attn:
-            states.append(new_alpha)
-        return tuple(states)
-
-    def call(self, query, states):
-        """
-        shapes:
-            query: B x D
-        """
-        if self.use_loc_attn:
-            score, _ = self.get_loc_attn(query, states)
-        else:
-            score, _ = self.get_attn(query)
-
-        # TODO: masking
-        # if mask is not None:
-        # self.apply_score_masking(score, mask)
-        # attn_weights shape == (batch_size, max_length, 1)
-
-        # normalize attention scores
-        scores_norm = self.norm_func(score)
-        attn_weights = scores_norm
-
-        # apply forward attention
-        new_alpha = None
-        if self.use_forward_attn:
-            new_alpha = self.apply_forward_attention(attn_weights, states[-1])
-            attn_weights = new_alpha
-
-        # update states tuple
-        # states = (cum_attn_weights, attn_weights, new_alpha)
-        states = self.update_states(states, scores_norm, attn_weights, new_alpha)
-
-        # context_vector shape after sum == (batch_size, hidden_size)
-        context_vector = tf.matmul(tf.expand_dims(attn_weights, axis=2), self.values, transpose_a=True, transpose_b=False)
-        context_vector = tf.squeeze(context_vector, axis=1)
-        return context_vector, attn_weights, states
-
-
-# def _location_sensitive_score(processed_query, keys, processed_loc, attention_v, attention_b):
-#     dtype = processed_query.dtype
-#     num_units = keys.shape[-1].value or array_ops.shape(keys)[-1]
-#     return tf.reduce_sum(attention_v * tf.tanh(keys + processed_query + processed_loc + attention_b), [2])
-
-
-# class LocationSensitiveAttention(BahdanauAttention):
-#     def __init__(self,
-#                  units,
-#                  memory=None,
-#                  memory_sequence_length=None,
-#                  normalize=False,
-#                  probability_fn="softmax",
-#                  kernel_initializer="glorot_uniform",
-#                  dtype=None,
-#                  name="LocationSensitiveAttention",
-#                  location_attention_filters=32,
-#                  location_attention_kernel_size=31):
-
-#         super(LocationSensitiveAttention,
-#                     self).__init__(units=units,
-#                                     memory=memory,
-#                                     memory_sequence_length=memory_sequence_length,
-#                                     normalize=normalize,
-#                                     probability_fn='softmax',  ## parent module default
-#                                     kernel_initializer=kernel_initializer,
-#                                     dtype=dtype,
-#                                     name=name)
-#         if probability_fn == 'sigmoid':
-#             self.probability_fn = lambda score, _: self._sigmoid_normalization(score)
-#         self.location_conv = keras.layers.Conv1D(filters=location_attention_filters, kernel_size=location_attention_kernel_size, padding='same', use_bias=False)
-#         self.location_dense = keras.layers.Dense(units, use_bias=False)
-#         # self.v = keras.layers.Dense(1, use_bias=True)
-
-#     def  _location_sensitive_score(self, processed_query, keys, processed_loc):
-#         processed_query = tf.expand_dims(processed_query, 1)
-#         return tf.reduce_sum(self.attention_v * tf.tanh(keys + processed_query + processed_loc), [2])
-
-#     def _location_sensitive(self, alignment_cum, alignment_old):
-#         alignment_cat = tf.stack([alignment_cum, alignment_old], axis=2)
-#         return self.location_dense(self.location_conv(alignment_cat))
-
-#     def _sigmoid_normalization(self, score):
-#         return tf.nn.sigmoid(score) / tf.reduce_sum(tf.nn.sigmoid(score), axis=-1, keepdims=True)
-
-#     # def _apply_masking(self, score, mask):
-#     #     padding_mask = tf.expand_dims(math_ops.logical_not(mask), 2)
-#     #     # Bias so padding positions do not contribute to attention distribution.
-#     #     score -= 1.e9 * math_ops.cast(padding_mask, dtype=tf.float32)
-#     #     return score
-
-#     def _calculate_attention(self, query, state):
-#         alignment_cum, alignment_old = state[:2]
-#         processed_query = self.query_layer(
-#             query) if self.query_layer else query
-#         processed_loc = self._location_sensitive(alignment_cum, alignment_old)
-#         score = self._location_sensitive_score(
-#             processed_query,
-#             self.keys,
-#             processed_loc)
-#         alignment = self.probability_fn(score, state)
-#         alignment_cum = alignment_cum + alignment
-#         state[0] = alignment_cum
-#         state[1] = alignment
-#         return alignment, state
-
-#     def compute_context(self, alignments):
-#         expanded_alignments = tf.expand_dims(alignments, 1)
-#         context = tf.matmul(expanded_alignments, self.values)
-#         context = tf.squeeze(context, [1])
-#         return context
-
-#     # def call(self, query, state):
-#     #     alignment, next_state = self._calculate_attention(query, state)
-#     #     return alignment, next_state
diff --git a/tf/layers/tacotron2.py b/tf/layers/tacotron2.py
deleted file mode 100644
index 7cef9eac..00000000
--- a/tf/layers/tacotron2.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-from TTS.tf.utils.tf_utils import shape_list
-from TTS.tf.layers.common_layers import Prenet, Attention
-# from tensorflow_addons.seq2seq import AttentionWrapper
-
-
-class ConvBNBlock(keras.layers.Layer):
-    def __init__(self, filters, kernel_size, activation, **kwargs):
-        super(ConvBNBlock, self).__init__(**kwargs)
-        self.convolution1d = keras.layers.Conv1D(filters, kernel_size, padding='same', name='convolution1d')
-        self.batch_normalization = keras.layers.BatchNormalization(axis=2, momentum=0.90, epsilon=1e-5, name='batch_normalization')
-        self.dropout = keras.layers.Dropout(rate=0.5, name='dropout')
-        self.activation = keras.layers.Activation(activation, name='activation')
-
-    def call(self, x, training=None):
-        o = self.convolution1d(x)
-        o = self.batch_normalization(o, training=training)
-        o = self.activation(o)
-        o = self.dropout(o, training=training)
-        return o
-
-
-class Postnet(keras.layers.Layer):
-    def __init__(self, output_filters, num_convs, **kwargs):
-        super(Postnet, self).__init__(**kwargs)
-        self.convolutions = []
-        self.convolutions.append(ConvBNBlock(512, 5, 'tanh', name='convolutions_0'))
-        for idx in range(1, num_convs - 1):
-            self.convolutions.append(ConvBNBlock(512, 5, 'tanh', name=f'convolutions_{idx}'))
-        self.convolutions.append(ConvBNBlock(output_filters, 5, 'linear', name=f'convolutions_{idx+1}'))
-
-    def call(self, x, training=None):
-        o = x
-        for layer in self.convolutions:
-            o = layer(o, training=training)
-        return o
-
-
-class Encoder(keras.layers.Layer):
-    def __init__(self, output_input_dim, **kwargs):
-        super(Encoder, self).__init__(**kwargs)
-        self.convolutions = []
-        for idx in range(3):
-            self.convolutions.append(ConvBNBlock(output_input_dim, 5, 'relu', name=f'convolutions_{idx}'))
-        self.lstm = keras.layers.Bidirectional(keras.layers.LSTM(output_input_dim // 2, return_sequences=True, use_bias=True), name='lstm')
-
-    def call(self, x, training=None):
-        o = x
-        for layer in self.convolutions:
-            o = layer(o, training=training)
-        o = self.lstm(o)
-        return o
-
-
-class Decoder(keras.layers.Layer):
-    #pylint: disable=unused-argument
-    def __init__(self, frame_dim, r, attn_type, use_attn_win, attn_norm, prenet_type,
-                 prenet_dropout, use_forward_attn, use_trans_agent, use_forward_attn_mask,
-                 use_location_attn, attn_K, separate_stopnet, speaker_emb_dim, enable_tflite, **kwargs):
-        super(Decoder, self).__init__(**kwargs)
-        self.frame_dim = frame_dim
-        self.r_init = tf.constant(r, dtype=tf.int32)
-        self.r = tf.constant(r, dtype=tf.int32)
-        self.output_dim = r * self.frame_dim
-        self.separate_stopnet = separate_stopnet
-        self.enable_tflite = enable_tflite
-
-        # layer constants
-        self.max_decoder_steps = tf.constant(1000, dtype=tf.int32)
-        self.stop_thresh = tf.constant(0.5, dtype=tf.float32)
-
-        # model dimensions
-        self.query_dim = 1024
-        self.decoder_rnn_dim = 1024
-        self.prenet_dim = 256
-        self.attn_dim = 128
-        self.p_attention_dropout = 0.1
-        self.p_decoder_dropout = 0.1
-
-        self.prenet = Prenet(prenet_type,
-                             prenet_dropout,
-                             [self.prenet_dim, self.prenet_dim],
-                             bias=False,
-                             name='prenet')
-        self.attention_rnn = keras.layers.LSTMCell(self.query_dim, use_bias=True, name='attention_rnn', )
-        self.attention_rnn_dropout = keras.layers.Dropout(0.5)
-
-        # TODO: implement other attn options
-        self.attention = Attention(attn_dim=self.attn_dim,
-                                   use_loc_attn=True,
-                                   loc_attn_n_filters=32,
-                                   loc_attn_kernel_size=31,
-                                   use_windowing=False,
-                                   norm=attn_norm,
-                                   use_forward_attn=use_forward_attn,
-                                   use_trans_agent=use_trans_agent,
-                                   use_forward_attn_mask=use_forward_attn_mask,
-                                   name='attention')
-        self.decoder_rnn = keras.layers.LSTMCell(self.decoder_rnn_dim, use_bias=True, name='decoder_rnn')
-        self.decoder_rnn_dropout = keras.layers.Dropout(0.5)
-        self.linear_projection = keras.layers.Dense(self.frame_dim * r, name='linear_projection/linear_layer')
-        self.stopnet = keras.layers.Dense(1, name='stopnet/linear_layer')
-
-
-    def set_max_decoder_steps(self, new_max_steps):
-        self.max_decoder_steps = tf.constant(new_max_steps, dtype=tf.int32)
-
-    def set_r(self, new_r):
-        self.r = tf.constant(new_r, dtype=tf.int32)
-        self.output_dim = self.frame_dim * new_r
-
-    def build_decoder_initial_states(self, batch_size, memory_dim, memory_length):
-        zero_frame = tf.zeros([batch_size, self.frame_dim])
-        zero_context = tf.zeros([batch_size, memory_dim])
-        attention_rnn_state = self.attention_rnn.get_initial_state(batch_size=batch_size, dtype=tf.float32)
-        decoder_rnn_state = self.decoder_rnn.get_initial_state(batch_size=batch_size, dtype=tf.float32)
-        attention_states = self.attention.init_states(batch_size, memory_length)
-        return zero_frame, zero_context, attention_rnn_state, decoder_rnn_state, attention_states
-
-    def step(self, prenet_next, states,
-             memory_seq_length=None, training=None):
-        _, context_next, attention_rnn_state, decoder_rnn_state, attention_states = states
-        attention_rnn_input = tf.concat([prenet_next, context_next], -1)
-        attention_rnn_output, attention_rnn_state = \
-                self.attention_rnn(attention_rnn_input,
-                                   attention_rnn_state, training=training)
-        attention_rnn_output = self.attention_rnn_dropout(attention_rnn_output, training=training)
-        context, attention, attention_states = self.attention(attention_rnn_output, attention_states, training=training)
-        decoder_rnn_input = tf.concat([attention_rnn_output, context], -1)
-        decoder_rnn_output, decoder_rnn_state = \
-                self.decoder_rnn(decoder_rnn_input, decoder_rnn_state, training=training)
-        decoder_rnn_output = self.decoder_rnn_dropout(decoder_rnn_output, training=training)
-        linear_projection_input = tf.concat([decoder_rnn_output, context], -1)
-        output_frame = self.linear_projection(linear_projection_input, training=training)
-        stopnet_input = tf.concat([decoder_rnn_output, output_frame], -1)
-        stopnet_output = self.stopnet(stopnet_input, training=training)
-        output_frame = output_frame[:, :self.r * self.frame_dim]
-        states = (output_frame[:, self.frame_dim * (self.r - 1):], context, attention_rnn_state, decoder_rnn_state, attention_states)
-        return output_frame, stopnet_output, states, attention
-
-    def decode(self, memory, states, frames, memory_seq_length=None):
-        B, _, _ = shape_list(memory)
-        num_iter = shape_list(frames)[1] // self.r
-        # init states
-        frame_zero = tf.expand_dims(states[0], 1)
-        frames = tf.concat([frame_zero, frames], axis=1)
-        outputs = tf.TensorArray(dtype=tf.float32, size=num_iter)
-        attentions = tf.TensorArray(dtype=tf.float32, size=num_iter)
-        stop_tokens = tf.TensorArray(dtype=tf.float32, size=num_iter)
-        # pre-computes
-        self.attention.process_values(memory)
-        prenet_output = self.prenet(frames, training=True)
-        step_count = tf.constant(0, dtype=tf.int32)
-
-        def _body(step, memory, prenet_output, states, outputs, stop_tokens, attentions):
-            prenet_next = prenet_output[:, step]
-            output, stop_token, states, attention = self.step(prenet_next,
-                                                              states,
-                                                              memory_seq_length)
-            outputs = outputs.write(step, output)
-            attentions = attentions.write(step, attention)
-            stop_tokens = stop_tokens.write(step, stop_token)
-            return step + 1, memory, prenet_output, states, outputs, stop_tokens, attentions
-        _, memory, _, states, outputs, stop_tokens, attentions = \
-                tf.while_loop(lambda *arg: True,
-                              _body,
-                              loop_vars=(step_count, memory, prenet_output,
-                                         states, outputs, stop_tokens, attentions),
-                              parallel_iterations=32,
-                              swap_memory=True,
-                              maximum_iterations=num_iter)
-
-        outputs = outputs.stack()
-        attentions = attentions.stack()
-        stop_tokens = stop_tokens.stack()
-        outputs = tf.transpose(outputs, [1, 0, 2])
-        attentions = tf.transpose(attentions, [1, 0, 2])
-        stop_tokens = tf.transpose(stop_tokens, [1, 0, 2])
-        stop_tokens = tf.squeeze(stop_tokens, axis=2)
-        outputs = tf.reshape(outputs, [B, -1, self.frame_dim])
-        return outputs, stop_tokens, attentions
-
-    def decode_inference(self, memory, states):
-        B, _, _ = shape_list(memory)
-        # init states
-        outputs = tf.TensorArray(dtype=tf.float32, size=0, clear_after_read=False, dynamic_size=True)
-        attentions = tf.TensorArray(dtype=tf.float32, size=0, clear_after_read=False, dynamic_size=True)
-        stop_tokens = tf.TensorArray(dtype=tf.float32, size=0, clear_after_read=False, dynamic_size=True)
-
-        # pre-computes
-        self.attention.process_values(memory)
-
-        # iter vars
-        stop_flag = tf.constant(False, dtype=tf.bool)
-        step_count = tf.constant(0, dtype=tf.int32)
-
-        def _body(step, memory, states, outputs, stop_tokens, attentions, stop_flag):
-            frame_next = states[0]
-            prenet_next = self.prenet(frame_next, training=False)
-            output, stop_token, states, attention = self.step(prenet_next,
-                                                              states,
-                                                              None,
-                                                              training=False)
-            stop_token = tf.math.sigmoid(stop_token)
-            outputs = outputs.write(step, output)
-            attentions = attentions.write(step, attention)
-            stop_tokens = stop_tokens.write(step, stop_token)
-            stop_flag = tf.greater(stop_token, self.stop_thresh)
-            stop_flag = tf.reduce_all(stop_flag)
-            return step + 1, memory, states, outputs, stop_tokens, attentions, stop_flag
-
-        cond = lambda step, m, s, o, st, a, stop_flag: tf.equal(stop_flag, tf.constant(False, dtype=tf.bool))
-        _, memory, states, outputs, stop_tokens, attentions, stop_flag = \
-                tf.while_loop(cond,
-                              _body,
-                              loop_vars=(step_count, memory, states, outputs,
-                                         stop_tokens, attentions, stop_flag),
-                              parallel_iterations=32,
-                              swap_memory=True,
-                              maximum_iterations=self.max_decoder_steps)
-
-        outputs = outputs.stack()
-        attentions = attentions.stack()
-        stop_tokens = stop_tokens.stack()
-
-        outputs = tf.transpose(outputs, [1, 0, 2])
-        attentions = tf.transpose(attentions, [1, 0, 2])
-        stop_tokens = tf.transpose(stop_tokens, [1, 0, 2])
-        stop_tokens = tf.squeeze(stop_tokens, axis=2)
-        outputs = tf.reshape(outputs, [B, -1, self.frame_dim])
-        return outputs, stop_tokens, attentions
-
-    def decode_inference_tflite(self, memory, states):
-        """Inference with TF-Lite compatibility. It assumes
-        batch_size is 1"""
-        # init states
-        # dynamic_shape is not supported in TFLite
-        outputs = tf.TensorArray(dtype=tf.float32,
-                                 size=self.max_decoder_steps,
-                                 element_shape=tf.TensorShape(
-                                     [self.output_dim]),
-                                 clear_after_read=False,
-                                 dynamic_size=False)
-        # stop_flags = tf.TensorArray(dtype=tf.bool,
-        #                          size=self.max_decoder_steps,
-        #                          element_shape=tf.TensorShape(
-        #                              []),
-        #                          clear_after_read=False,
-        #                          dynamic_size=False)
-        attentions = ()
-        stop_tokens = ()
-
-        # pre-computes
-        self.attention.process_values(memory)
-
-        # iter vars
-        stop_flag = tf.constant(False, dtype=tf.bool)
-        step_count = tf.constant(0, dtype=tf.int32)
-
-        def _body(step, memory, states, outputs, stop_flag):
-            frame_next = states[0]
-            prenet_next = self.prenet(frame_next, training=False)
-            output, stop_token, states, _ = self.step(prenet_next,
-                                                      states,
-                                                      None,
-                                                      training=False)
-            stop_token = tf.math.sigmoid(stop_token)
-            stop_flag = tf.greater(stop_token, self.stop_thresh)
-            stop_flag = tf.reduce_all(stop_flag)
-            # stop_flags = stop_flags.write(step, tf.logical_not(stop_flag))
-
-            outputs = outputs.write(step, tf.reshape(output, [-1]))
-            return step + 1, memory, states, outputs, stop_flag
-
-        cond = lambda step, m, s, o, stop_flag: tf.equal(stop_flag, tf.constant(False, dtype=tf.bool))
-        step_count, memory, states, outputs, stop_flag = \
-                tf.while_loop(cond,
-                              _body,
-                              loop_vars=(step_count, memory, states, outputs,
-                                         stop_flag),
-                              parallel_iterations=32,
-                              swap_memory=True,
-                              maximum_iterations=self.max_decoder_steps)
-
-
-        outputs = outputs.stack()
-        outputs = tf.gather(outputs, tf.range(step_count)) # pylint: disable=no-value-for-parameter
-        outputs = tf.expand_dims(outputs, axis=[0])
-        outputs = tf.transpose(outputs, [1, 0, 2])
-        outputs = tf.reshape(outputs, [1, -1, self.frame_dim])
-        return outputs, stop_tokens, attentions
-
-
-    def call(self, memory, states, frames=None, memory_seq_length=None, training=False):
-        if training:
-            return self.decode(memory, states, frames, memory_seq_length)
-        if self.enable_tflite:
-            return self.decode_inference_tflite(memory, states)
-        return self.decode_inference(memory, states)
diff --git a/tf/models/tacotron2.py b/tf/models/tacotron2.py
deleted file mode 100644
index 70d725e2..00000000
--- a/tf/models/tacotron2.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-
-from TTS.tf.layers.tacotron2 import Encoder, Decoder, Postnet
-from TTS.tf.utils.tf_utils import shape_list
-
-
-#pylint: disable=too-many-ancestors
-class Tacotron2(keras.models.Model):
-    def __init__(self,
-                 num_chars,
-                 num_speakers,
-                 r,
-                 postnet_output_dim=80,
-                 decoder_output_dim=80,
-                 attn_type='original',
-                 attn_win=False,
-                 attn_norm="softmax",
-                 attn_K=4,
-                 prenet_type="original",
-                 prenet_dropout=True,
-                 forward_attn=False,
-                 trans_agent=False,
-                 forward_attn_mask=False,
-                 location_attn=True,
-                 separate_stopnet=True,
-                 bidirectional_decoder=False,
-                 enable_tflite=False):
-        super(Tacotron2, self).__init__()
-        self.r = r
-        self.decoder_output_dim = decoder_output_dim
-        self.postnet_output_dim = postnet_output_dim
-        self.bidirectional_decoder = bidirectional_decoder
-        self.num_speakers = num_speakers
-        self.speaker_embed_dim = 256
-        self.enable_tflite = enable_tflite
-
-        self.embedding = keras.layers.Embedding(num_chars, 512, name='embedding')
-        self.encoder = Encoder(512, name='encoder')
-        # TODO: most of the decoder args have no use at the momment
-        self.decoder = Decoder(decoder_output_dim,
-                               r,
-                               attn_type=attn_type,
-                               use_attn_win=attn_win,
-                               attn_norm=attn_norm,
-                               prenet_type=prenet_type,
-                               prenet_dropout=prenet_dropout,
-                               use_forward_attn=forward_attn,
-                               use_trans_agent=trans_agent,
-                               use_forward_attn_mask=forward_attn_mask,
-                               use_location_attn=location_attn,
-                               attn_K=attn_K,
-                               separate_stopnet=separate_stopnet,
-                               speaker_emb_dim=self.speaker_embed_dim,
-                               name='decoder',
-                               enable_tflite=enable_tflite)
-        self.postnet = Postnet(postnet_output_dim, 5, name='postnet')
-
-    @tf.function(experimental_relax_shapes=True)
-    def call(self, characters, text_lengths=None, frames=None, training=None):
-        if training:
-            return self.training(characters, text_lengths, frames)
-        if not training:
-            return self.inference(characters)
-        raise RuntimeError(' [!] Set model training mode True or False')
-
-    def training(self, characters, text_lengths, frames):
-        B, T = shape_list(characters)
-        embedding_vectors = self.embedding(characters, training=True)
-        encoder_output = self.encoder(embedding_vectors, training=True)
-        decoder_states = self.decoder.build_decoder_initial_states(B, 512, T)
-        decoder_frames, stop_tokens, attentions = self.decoder(encoder_output, decoder_states, frames, text_lengths, training=True)
-        postnet_frames = self.postnet(decoder_frames, training=True)
-        output_frames = decoder_frames + postnet_frames
-        return decoder_frames, output_frames, attentions, stop_tokens
-
-    def inference(self, characters):
-        B, T = shape_list(characters)
-        embedding_vectors = self.embedding(characters, training=False)
-        encoder_output = self.encoder(embedding_vectors, training=False)
-        decoder_states = self.decoder.build_decoder_initial_states(B, 512, T)
-        decoder_frames, stop_tokens, attentions = self.decoder(encoder_output, decoder_states, training=False)
-        postnet_frames = self.postnet(decoder_frames, training=False)
-        output_frames = decoder_frames + postnet_frames
-        print(output_frames.shape)
-        return decoder_frames, output_frames, attentions, stop_tokens
-
-    @tf.function(
-        experimental_relax_shapes=True,
-        input_signature=[
-            tf.TensorSpec([1, None], dtype=tf.int32),
-        ],)
-    def inference_tflite(self, characters):
-        B, T = shape_list(characters)
-        embedding_vectors = self.embedding(characters, training=False)
-        encoder_output = self.encoder(embedding_vectors, training=False)
-        decoder_states = self.decoder.build_decoder_initial_states(B, 512, T)
-        decoder_frames, stop_tokens, attentions = self.decoder(encoder_output, decoder_states, training=False)
-        postnet_frames = self.postnet(decoder_frames, training=False)
-        output_frames = decoder_frames + postnet_frames
-        print(output_frames.shape)
-        return decoder_frames, output_frames, attentions, stop_tokens
-
-    def build_inference(self, ):
-        # TODO: issue https://github.com/PyCQA/pylint/issues/3613
-        input_ids = tf.random.uniform(shape=[1, 4], maxval=10, dtype=tf.int32)  #pylint: disable=unexpected-keyword-arg
-        self(input_ids)
-
diff --git a/tf/notebooks/Benchmark-TTS_tf.ipynb b/tf/notebooks/Benchmark-TTS_tf.ipynb
deleted file mode 100644
index 4a21ae17..00000000
--- a/tf/notebooks/Benchmark-TTS_tf.ipynb
+++ /dev/null
@@ -1,714 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "source": [
-    "This is to test TTS tensorflow models with benchmark sentences.\n",
-    "\n",
-    "Before running this script please DON'T FORGET: \n",
-    "- to set file paths.\n",
-    "- to download related models.\n",
-    "    - Sample TF model: https://www.dropbox.com/sh/3b1fat5oxqab6yn/AADDlNs-9-r7ASbVnFYx3RHHa?dl=0\n",
-    "- download or clone related repos, linked below.\n",
-    "- setup the repositories. ```python setup.py install```\n",
-    "- to checkout right commit versions (given next to the model in the models page).\n",
-    "- to set the file paths below.\n",
-    "\n",
-    "Repositories:\n",
-    "- TTS: https://github.com/mozilla/TTS\n",
-    "- PWGAN: https://github.com/erogol/ParallelWaveGAN (if you like to use a vocoder model)\n",
-    "\n",
-    "Known Issues:\n",
-    "- To load the model second time you need to restart the notebook kernel. \n",
-    "- Some of the advance methods are not yet implemented for Tensorflow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false",
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "import os\n",
-    "\n",
-    "# you may need to change this depending on your system\n",
-    "os.environ['CUDA_VISIBLE_DEVICES']='1'\n",
-    "\n",
-    "import sys\n",
-    "import io\n",
-    "import torch \n",
-    "import tensorflow as tf\n",
-    "print(tf.config.list_physical_devices('GPU'))\n",
-    "\n",
-    "import time\n",
-    "import json\n",
-    "import yaml\n",
-    "import numpy as np\n",
-    "from collections import OrderedDict\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.rcParams[\"figure.figsize\"] = (16,5)\n",
-    "\n",
-    "import librosa\n",
-    "import librosa.display\n",
-    "\n",
-    "from TTS.tf.models.tacotron2 import Tacotron2\n",
-    "from TTS.tf.utils.generic_utils import setup_model, load_checkpoint\n",
-    "from TTS.utils.audio import AudioProcessor\n",
-    "from TTS.utils.io import load_config\n",
-    "from TTS.utils.synthesis import synthesis\n",
-    "from TTS.utils.visual import visualize\n",
-    "\n",
-    "import IPython\n",
-    "from IPython.display import Audio\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n",
-    "    t_1 = time.time()\n",
-    "    waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, None, None, False, CONFIG.enable_eos_bos_chars, use_gl, backend=BACKEND)\n",
-    "    if CONFIG.model == \"Tacotron\" and not use_gl:\n",
-    "        # coorect the normalization differences b/w TTS and the Vocoder.\n",
-    "        mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n",
-    "    print(mel_postnet_spec.shape)\n",
-    "    print(\"max- \", mel_postnet_spec.max(), \" -- min- \", mel_postnet_spec.min())\n",
-    "    if not use_gl:\n",
-    "        waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
-    "    mel_postnet_spec = ap._denormalize(mel_postnet_spec.T).T\n",
-    "    if use_cuda and not use_gl:\n",
-    "        waveform = waveform.cpu()\n",
-    "        waveform = waveform.numpy()\n",
-    "    waveform = waveform.squeeze()\n",
-    "    rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
-    "    print(waveform.shape)\n",
-    "    print(\" > Run-time: {}\".format(time.time() - t_1))\n",
-    "    print(\" > Real-time factor: {}\".format(rtf))\n",
-    "    if figures:  \n",
-    "        visualize(alignment, mel_postnet_spec, stop_tokens, text, ap.hop_length, CONFIG, ap._denormalize(mel_spec.T).T)                                                                       \n",
-    "    IPython.display.display(Audio(waveform, rate=CONFIG.audio['sample_rate'], normalize=True))  \n",
-    "    os.makedirs(OUT_FOLDER, exist_ok=True)\n",
-    "    file_name = text.replace(\" \", \"_\").replace(\".\",\"\") + \".wav\"\n",
-    "    out_path = os.path.join(OUT_FOLDER, file_name)\n",
-    "    ap.save_wav(waveform, out_path)\n",
-    "    return alignment, mel_postnet_spec, stop_tokens, waveform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "# Set constants\n",
-    "ROOT_PATH = '../torch_model/'\n",
-    "MODEL_PATH = ROOT_PATH + '/tts_tf_checkpoint_360000.pkl'\n",
-    "CONFIG_PATH = ROOT_PATH + '/config.json'\n",
-    "OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n",
-    "CONFIG = load_config(CONFIG_PATH)\n",
-    "# Run FLAGs\n",
-    "use_cuda = True  # use the available GPU (only for torch)\n",
-    "# Set the vocoder\n",
-    "use_gl = True  # use GL if True\n",
-    "BACKEND = 'tf'  # set the backend for inference "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false",
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "from TTS.utils.text.symbols import symbols, phonemes, make_symbols\n",
-    "from TTS.tf.utils.convert_torch_to_tf_utils import tf_create_dummy_inputs\n",
-    "c = CONFIG\n",
-    "num_speakers = 0\n",
-    "r = 1\n",
-    "num_chars = len(phonemes) if c.use_phonemes else len(symbols)\n",
-    "model = setup_model(num_chars, num_speakers, c)\n",
-    "\n",
-    "# before loading weights you need to run the model once to generate the variables\n",
-    "input_ids, input_lengths, mel_outputs, mel_lengths = tf_create_dummy_inputs()\n",
-    "mel_pred = model(input_ids, training=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false",
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "model = load_checkpoint(model, MODEL_PATH)\n",
-    "# model = tf.function(model, experimental_relax_shapes=True)\n",
-    "ap = AudioProcessor(**CONFIG.audio)    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "# wrapper class to use tf.function\n",
-    "class ModelInference(tf.keras.Model):\n",
-    "    def __init__(self, model):\n",
-    "        super(ModelInference, self).__init__()\n",
-    "        self.model = model\n",
-    "    \n",
-    "    @tf.function(input_signature=[tf.TensorSpec(shape=(None, None), dtype=tf.int32)])\n",
-    "    def call(self, characters):\n",
-    "        return self.model(characters, training=False)\n",
-    "    \n",
-    "model = ModelInference(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "# LOAD WAVERNN\n",
-    "if use_gl == False:\n",
-    "    from parallel_wavegan.models import ParallelWaveGANGenerator, MelGANGenerator\n",
-    "    \n",
-    "    vocoder_model = MelGANGenerator(**VOCODER_CONFIG[\"generator_params\"])\n",
-    "    vocoder_model.load_state_dict(torch.load(VOCODER_MODEL_PATH, map_location=\"cpu\")[\"model\"][\"generator\"])\n",
-    "    vocoder_model.remove_weight_norm()\n",
-    "    ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio'])    \n",
-    "    if use_cuda:\n",
-    "        vocoder_model.cuda()\n",
-    "    vocoder_model.eval();\n",
-    "    print(count_parameters(vocoder_model))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "source": [
-    "### Comparision with https://mycroft.ai/blog/available-voices/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence =  \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "source": [
-    "### https://espnet.github.io/icassp2020-tts/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"The Commission also recommends\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"As a result of these studies, the planning document submitted by the Secretary of the Treasury to the Bureau of the Budget on August thirty-one.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"The FBI now transmits information on all defectors, a category which would, of course, have included Oswald.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"they seem unduly restrictive in continuing to require some manifestation of animus against a Government official.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"and each agency given clear understanding of the assistance which the Secret Service expects.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "source": [
-    "### Other examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Be a voice, not an echo.\"  # 'echo' is not in training set. \n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"The human voice is the most perfect instrument of all.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"This cake is great. It's so delicious and moist.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "source": [
-    "### Comparison with https://keithito.github.io/audio-samples/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"The buses aren't the problem, they actually provide a solution.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "source": [
-    "### Comparison with https://google.github.io/tacotron/publications/tacotron/index.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Basilar membrane and otolaryngology are not auto-correlations.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \" He has read the whole thing.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"He reads books.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Thisss isrealy awhsome.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"This is your internet browser, Firefox.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"This is your internet browser Firefox.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"The quick brown fox jumps over the lazy dog.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Does the quick brown fox jump over the lazy dog?\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Eren, how are you?\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "source": [
-    "### Hard Sentences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Encouraged, he started with a minute a day.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"His meditation consisted of “body scanning” which involved focusing his mind and energy on each section of the body from head to toe .\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase the grey matter in the parts of the brain responsible for emotional regulation and learning . \"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"If he decided to watch TV he really watched it.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sentence = \"Often we try to bring about change through sheer effort and we put all of our energy into a new initiative .\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "# for twb dataset\n",
-    "sentence = \"In our preparation for Easter, God in his providence offers us each year the season of Lent as a sacramental sign of our conversion.\"\n",
-    "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "wavs = []\n",
-    "model.eval()\n",
-    "model.decoder.prenet.eval()\n",
-    "model.decoder.max_decoder_steps = 2000\n",
-    "# model.decoder.prenet.train()\n",
-    "speaker_id = None\n",
-    "sentence =  '''This is App Store Optimization report.\n",
-    "The first tab on the report is App Details. App details report is updated weekly and Datetime column shows the latest report update date. The widget displays the app icon, respective app version, visual assets on the store, app description, latest app update date on the Appstore/Google PlayStore and what’s new section.\n",
-    "In App Details tab, you can see not only your app but all Delivery Hero apps since we think it can be inspiring to see the other apps, their description and screenshots. \n",
-    "Product name is the actual app name on the AppStore or Google Play Store.\n",
-    "Screenshot URLs column display the actual screenshots on the store for the current version. No resizing is done. If you  click on the screenshot, you can see it in full-size.\n",
-    "Current release date show the latest app update date when the query is run. Here we see that Appetito24 Android is updated to app version 4.6.3.2 on 28th of March.\n",
-    "If the description is too long, clarisights is not able to display the full description; however, if you select description and current_release_date cells to copy and paste it to a text editor, you'll see the full description.\n",
-    "If you scroll down in the widget, you can see the older app versions for the same apps. Or you can filter Datetime to see a specific timeframe and the apps’ Store presence back then.\n",
-    "You can also filter for a specific app using Product Name.\n",
-    "If the description is too long, clarisights is not able to display the full description; however, if you select description and current_release_date cells to copy and paste it to a text editor, you'll see the full description.\n",
-    "'''\n",
-    "\n",
-    "for s in sentence.split('\\n'):\n",
-    "    print(s)\n",
-    "    align, spec, stop_tokens, wav = tts(model, s, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)\n",
-    "    wavs = np.concatenate([wavs, np.zeros(int(ap.sample_rate * 0.5)), wav])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tf/tests/__init__.py b/tf/tests/__init__.py
deleted file mode 100644
index 8b137891..00000000
--- a/tf/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/tf/tests/test_layers_tf.py b/tf/tests/test_layers_tf.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tf/utils/convert_torch_to_tf_utils.py b/tf/utils/convert_torch_to_tf_utils.py
deleted file mode 100644
index e9e1e8a3..00000000
--- a/tf/utils/convert_torch_to_tf_utils.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import numpy as np
-import tensorflow as tf
-
-
-def tf_create_dummy_inputs():
-    """ Create dummy inputs for TF Tacotron2 model """
-    batch_size = 4
-    max_input_length = 32
-    max_mel_length = 128
-    pad = 1
-    n_chars = 24
-    input_ids = tf.random.uniform([batch_size, max_input_length + pad], maxval=n_chars, dtype=tf.int32)
-    input_lengths = np.random.randint(0, high=max_input_length+1 + pad, size=[batch_size])
-    input_lengths[-1] = max_input_length
-    input_lengths = tf.convert_to_tensor(input_lengths, dtype=tf.int32)
-    mel_outputs = tf.random.uniform(shape=[batch_size, max_mel_length + pad, 80])
-    mel_lengths = np.random.randint(0, high=max_mel_length+1 + pad, size=[batch_size])
-    mel_lengths[-1] = max_mel_length
-    mel_lengths = tf.convert_to_tensor(mel_lengths, dtype=tf.int32)
-    return input_ids, input_lengths, mel_outputs, mel_lengths
-
-
-def compare_torch_tf(torch_tensor, tf_tensor):
-    """ Compute the average absolute difference b/w torch and tf tensors """
-    return abs(torch_tensor.detach().numpy() - tf_tensor.numpy()).mean()
-
-
-def convert_tf_name(tf_name):
-    """ Convert certain patterns in TF layer names to Torch patterns """
-    tf_name_tmp = tf_name
-    tf_name_tmp = tf_name_tmp.replace(':0', '')
-    tf_name_tmp = tf_name_tmp.replace('/forward_lstm/lstm_cell_1/recurrent_kernel', '/weight_hh_l0')
-    tf_name_tmp = tf_name_tmp.replace('/forward_lstm/lstm_cell_2/kernel', '/weight_ih_l1')
-    tf_name_tmp = tf_name_tmp.replace('/recurrent_kernel', '/weight_hh')
-    tf_name_tmp = tf_name_tmp.replace('/kernel', '/weight')
-    tf_name_tmp = tf_name_tmp.replace('/gamma', '/weight')
-    tf_name_tmp = tf_name_tmp.replace('/beta', '/bias')
-    tf_name_tmp = tf_name_tmp.replace('/', '.')
-    return tf_name_tmp
-
-
-def transfer_weights_torch_to_tf(tf_vars, var_map_dict, state_dict):
-    """ Transfer weigths from torch state_dict to TF variables """
-    print(" > Passing weights from Torch to TF ...")
-    for tf_var in tf_vars:
-        torch_var_name = var_map_dict[tf_var.name]
-        print(f' | > {tf_var.name} <-- {torch_var_name}')
-        # if tuple, it is a bias variable
-        if not isinstance(torch_var_name, tuple):
-            torch_layer_name = '.'.join(torch_var_name.split('.')[-2:])
-            torch_weight = state_dict[torch_var_name]
-        if 'convolution1d/kernel' in tf_var.name or 'conv1d/kernel' in tf_var.name:
-            # out_dim, in_dim, filter -> filter, in_dim, out_dim
-            numpy_weight = torch_weight.permute([2, 1, 0]).detach().cpu().numpy()
-        elif 'lstm_cell' in tf_var.name and 'kernel' in tf_var.name:
-            numpy_weight = torch_weight.transpose(0, 1).detach().cpu().numpy()
-        # if variable is for bidirectional lstm and it is a bias vector there
-        # needs to be pre-defined two matching torch bias vectors
-        elif '_lstm/lstm_cell_' in tf_var.name and 'bias' in tf_var.name:
-            bias_vectors = [value for key, value in state_dict.items() if key in torch_var_name]
-            assert len(bias_vectors) == 2
-            numpy_weight = bias_vectors[0] + bias_vectors[1]
-        elif 'rnn' in tf_var.name and 'kernel' in tf_var.name:
-            numpy_weight = torch_weight.transpose(0, 1).detach().cpu().numpy()
-        elif 'rnn' in tf_var.name and 'bias' in tf_var.name:
-            bias_vectors = [value for key, value in state_dict.items() if torch_var_name[:-2] in key]
-            assert len(bias_vectors) == 2
-            numpy_weight = bias_vectors[0] + bias_vectors[1]
-        elif 'linear_layer' in torch_layer_name and 'weight' in torch_var_name:
-            numpy_weight = torch_weight.transpose(0, 1).detach().cpu().numpy()
-        else:
-            numpy_weight = torch_weight.detach().cpu().numpy()
-        assert np.all(tf_var.shape == numpy_weight.shape), f" [!] weight shapes does not match: {tf_var.name} vs {torch_var_name} --> {tf_var.shape} vs {numpy_weight.shape}"
-        tf.keras.backend.set_value(tf_var, numpy_weight)
-    return tf_vars
-
-
-def load_tf_vars(model_tf, tf_vars):
-    for tf_var in tf_vars:
-        model_tf.get_layer(tf_var.name).set_weights(tf_var)
-    return model_tf
diff --git a/tf/utils/generic_utils.py b/tf/utils/generic_utils.py
deleted file mode 100644
index 1fea4cbb..00000000
--- a/tf/utils/generic_utils.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os
-import datetime
-import importlib
-import pickle
-import numpy as np
-import tensorflow as tf
-
-
-def save_checkpoint(model, optimizer, current_step, epoch, r, output_path, **kwargs):
-    state = {
-        'model': model.weights,
-        'optimizer': optimizer,
-        'step': current_step,
-        'epoch': epoch,
-        'date': datetime.date.today().strftime("%B %d, %Y"),
-        'r': r
-    }
-    state.update(kwargs)
-    pickle.dump(state, open(output_path, 'wb'))
-
-
-def load_checkpoint(model, checkpoint_path):
-    checkpoint = pickle.load(open(checkpoint_path, 'rb'))
-    chkp_var_dict = {var.name: var.numpy() for var in checkpoint['model']}
-    tf_vars = model.weights
-    for tf_var in tf_vars:
-        layer_name = tf_var.name
-        try:
-            chkp_var_value = chkp_var_dict[layer_name]
-        except KeyError:
-            class_name = list(chkp_var_dict.keys())[0].split("/")[0]
-            layer_name = f"{class_name}/{layer_name}"
-            chkp_var_value = chkp_var_dict[layer_name]
-
-        tf.keras.backend.set_value(tf_var, chkp_var_value)
-    if 'r' in checkpoint.keys():
-        model.decoder.set_r(checkpoint['r'])
-    return model
-
-
-def sequence_mask(sequence_length, max_len=None):
-    if max_len is None:
-        max_len = sequence_length.max()
-    batch_size = sequence_length.size(0)
-    seq_range = np.empty([0, max_len], dtype=np.int8)
-    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
-    if sequence_length.is_cuda:
-        seq_range_expand = seq_range_expand.cuda()
-    seq_length_expand = (
-        sequence_length.unsqueeze(1).expand_as(seq_range_expand))
-    # B x T_max
-    return seq_range_expand < seq_length_expand
-
-
-# @tf.custom_gradient
-def check_gradient(x, grad_clip):
-    x_normed = tf.clip_by_norm(x, grad_clip)
-    grad_norm = tf.norm(grad_clip)
-    return x_normed, grad_norm
-
-
-def count_parameters(model, c):
-    try:
-        return model.count_params()
-    except RuntimeError:
-        input_dummy = tf.convert_to_tensor(np.random.rand(8, 128).astype('int32'))
-        input_lengths = np.random.randint(100, 129, (8, ))
-        input_lengths[-1] = 128
-        input_lengths = tf.convert_to_tensor(input_lengths.astype('int32'))
-        mel_spec = np.random.rand(8, 2 * c.r,
-                                  c.audio['num_mels']).astype('float32')
-        mel_spec = tf.convert_to_tensor(mel_spec)
-        speaker_ids = np.random.randint(
-            0, 5, (8, )) if c.use_speaker_embedding else None
-        _ = model(input_dummy, input_lengths, mel_spec, speaker_ids=speaker_ids)
-        return model.count_params()
-
-
-def setup_model(num_chars, num_speakers, c, enable_tflite=False):
-    print(" > Using model: {}".format(c.model))
-    MyModel = importlib.import_module('TTS.tf.models.' + c.model.lower())
-    MyModel = getattr(MyModel, c.model)
-    if c.model.lower() in "tacotron":
-        raise NotImplementedError(' [!] Tacotron model is not ready.')
-    # tacotron2
-    model = MyModel(num_chars=num_chars,
-                    num_speakers=num_speakers,
-                    r=c.r,
-                    postnet_output_dim=c.audio['num_mels'],
-                    decoder_output_dim=c.audio['num_mels'],
-                    attn_type=c.attention_type,
-                    attn_win=c.windowing,
-                    attn_norm=c.attention_norm,
-                    prenet_type=c.prenet_type,
-                    prenet_dropout=c.prenet_dropout,
-                    forward_attn=c.use_forward_attn,
-                    trans_agent=c.transition_agent,
-                    forward_attn_mask=c.forward_attn_mask,
-                    location_attn=c.location_attn,
-                    attn_K=c.attention_heads,
-                    separate_stopnet=c.separate_stopnet,
-                    bidirectional_decoder=c.bidirectional_decoder,
-                    enable_tflite=enable_tflite)
-    return model
diff --git a/tf/utils/io.py b/tf/utils/io.py
deleted file mode 100644
index 78a56de4..00000000
--- a/tf/utils/io.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import pickle
-import datetime
-import tensorflow as tf
-
-
-def save_checkpoint(model, optimizer, current_step, epoch, r, output_path, **kwargs):
-    state = {
-        'model': model.weights,
-        'optimizer': optimizer,
-        'step': current_step,
-        'epoch': epoch,
-        'date': datetime.date.today().strftime("%B %d, %Y"),
-        'r': r
-    }
-    state.update(kwargs)
-    pickle.dump(state, open(output_path, 'wb'))
-
-
-def load_checkpoint(model, checkpoint_path):
-    checkpoint = pickle.load(open(checkpoint_path, 'rb'))
-    chkp_var_dict = {var.name: var.numpy() for var in checkpoint['model']}
-    tf_vars = model.weights
-    for tf_var in tf_vars:
-        layer_name = tf_var.name
-        try:
-            chkp_var_value = chkp_var_dict[layer_name]
-        except KeyError:
-            class_name = list(chkp_var_dict.keys())[0].split("/")[0]
-            layer_name = f"{class_name}/{layer_name}"
-            chkp_var_value = chkp_var_dict[layer_name]
-
-        tf.keras.backend.set_value(tf_var, chkp_var_value)
-    if 'r' in checkpoint.keys():
-        model.decoder.set_r(checkpoint['r'])
-    return model
-
-
-def load_tflite_model(tflite_path):
-    tflite_model = tf.lite.Interpreter(model_path=tflite_path)
-    tflite_model.allocate_tensors()
-    return tflite_model
-
diff --git a/tf/utils/tf_utils.py b/tf/utils/tf_utils.py
deleted file mode 100644
index 558936d5..00000000
--- a/tf/utils/tf_utils.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import tensorflow as tf
-
-
-def shape_list(x):
-    """Deal with dynamic shape in tensorflow cleanly."""
-    static = x.shape.as_list()
-    dynamic = tf.shape(x)
-    return [dynamic[i] if s is None else s for i, s in enumerate(static)]
diff --git a/tf/utils/tflite.py b/tf/utils/tflite.py
deleted file mode 100644
index 5e684b30..00000000
--- a/tf/utils/tflite.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import tensorflow as tf
-
-
-def convert_tacotron2_to_tflite(model,
-                                output_path=None,
-                                experimental_converter=True):
-    """Convert Tensorflow Tacotron2 model to TFLite. Save a binary file if output_path is
-    provided, else return TFLite model."""
-
-    concrete_function = model.inference_tflite.get_concrete_function()
-    converter = tf.lite.TFLiteConverter.from_concrete_functions(
-        [concrete_function])
-    converter.experimental_new_converter = experimental_converter
-    converter.optimizations = [tf.lite.Optimize.DEFAULT]
-    converter.target_spec.supported_ops = [
-        tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
-    ]
-    tflite_model = converter.convert()
-    print(f'Tflite Model size is {len(tflite_model) / (1024.0 * 1024.0)} MBs.')
-    if output_path is not None:
-        # same model binary if outputpath is provided
-        with open(output_path, 'wb') as f:
-            f.write(tflite_model)
-        return None
-    return tflite_model
-
-
-def load_tflite_model(tflite_path):
-    tflite_model = tf.lite.Interpreter(model_path=tflite_path)
-    tflite_model.allocate_tensors()
-    return tflite_model
\ No newline at end of file
diff --git a/train.py b/train.py
deleted file mode 100644
index 189a6baa..00000000
--- a/train.py
+++ /dev/null
@@ -1,641 +0,0 @@
-import argparse
-import os
-import sys
-import glob
-import time
-import traceback
-
-import numpy as np
-import torch
-from torch.utils.data import DataLoader
-
-from TTS.datasets.TTSDataset import MyDataset
-from distribute import (DistributedSampler, apply_gradient_allreduce,
-                        init_distributed, reduce_tensor)
-from TTS.layers.losses import TacotronLoss
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.generic_utils import (count_parameters, create_experiment_folder, remove_experiment_folder,
-                                     get_git_branch, set_init_dict,
-                                     setup_model, KeepAverage, check_config)
-from TTS.utils.io import (save_best_model, save_checkpoint,
-                          load_config, copy_config_file)
-from TTS.utils.training import (NoamLR, check_update, adam_weight_decay,
-                                gradual_training_scheduler, set_weight_decay,
-                                setup_torch_training_env)
-from TTS.utils.tensorboard_logger import TensorboardLogger
-from TTS.utils.console_logger import ConsoleLogger
-from TTS.utils.speakers import load_speaker_mapping, save_speaker_mapping, \
-    get_speakers
-from TTS.utils.synthesis import synthesis
-from TTS.utils.text.symbols import make_symbols, phonemes, symbols
-from TTS.utils.visual import plot_alignment, plot_spectrogram
-from TTS.datasets.preprocess import load_meta_data
-from TTS.utils.radam import RAdam
-from TTS.utils.measures import alignment_diagonal_score
-
-
-use_cuda, num_gpus = setup_torch_training_env(True, False)
-
-
-def setup_loader(ap, r, is_val=False, verbose=False):
-    if is_val and not c.run_eval:
-        loader = None
-    else:
-        dataset = MyDataset(
-            r,
-            c.text_cleaner,
-            compute_linear_spec=True if c.model.lower() == 'tacotron' else False,
-            meta_data=meta_data_eval if is_val else meta_data_train,
-            ap=ap,
-            tp=c.characters if 'characters' in c.keys() else None,
-            batch_group_size=0 if is_val else c.batch_group_size *
-            c.batch_size,
-            min_seq_len=c.min_seq_len,
-            max_seq_len=c.max_seq_len,
-            phoneme_cache_path=c.phoneme_cache_path,
-            use_phonemes=c.use_phonemes,
-            phoneme_language=c.phoneme_language,
-            enable_eos_bos=c.enable_eos_bos_chars,
-            verbose=verbose)
-        sampler = DistributedSampler(dataset) if num_gpus > 1 else None
-        loader = DataLoader(
-            dataset,
-            batch_size=c.eval_batch_size if is_val else c.batch_size,
-            shuffle=False,
-            collate_fn=dataset.collate_fn,
-            drop_last=False,
-            sampler=sampler,
-            num_workers=c.num_val_loader_workers
-            if is_val else c.num_loader_workers,
-            pin_memory=False)
-    return loader
-
-
-def format_data(data):
-    if c.use_speaker_embedding:
-        speaker_mapping = load_speaker_mapping(OUT_PATH)
-
-    # setup input data
-    text_input = data[0]
-    text_lengths = data[1]
-    speaker_names = data[2]
-    linear_input = data[3] if c.model in ["Tacotron"] else None
-    mel_input = data[4]
-    mel_lengths = data[5]
-    stop_targets = data[6]
-    avg_text_length = torch.mean(text_lengths.float())
-    avg_spec_length = torch.mean(mel_lengths.float())
-
-    if c.use_speaker_embedding:
-        speaker_ids = [
-            speaker_mapping[speaker_name] for speaker_name in speaker_names
-        ]
-        speaker_ids = torch.LongTensor(speaker_ids)
-    else:
-        speaker_ids = None
-
-    # set stop targets view, we predict a single stop token per iteration.
-    stop_targets = stop_targets.view(text_input.shape[0],
-                                     stop_targets.size(1) // c.r, -1)
-    stop_targets = (stop_targets.sum(2) >
-                    0.0).unsqueeze(2).float().squeeze(2)
-
-    # dispatch data to GPU
-    if use_cuda:
-        text_input = text_input.cuda(non_blocking=True)
-        text_lengths = text_lengths.cuda(non_blocking=True)
-        mel_input = mel_input.cuda(non_blocking=True)
-        mel_lengths = mel_lengths.cuda(non_blocking=True)
-        linear_input = linear_input.cuda(non_blocking=True) if c.model in ["Tacotron"] else None
-        stop_targets = stop_targets.cuda(non_blocking=True)
-        if speaker_ids is not None:
-            speaker_ids = speaker_ids.cuda(non_blocking=True)
-    return text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, avg_text_length, avg_spec_length
-
-
-def train(model, criterion, optimizer, optimizer_st, scheduler,
-          ap, global_step, epoch):
-    data_loader = setup_loader(ap, model.decoder.r, is_val=False,
-                               verbose=(epoch == 0))
-    model.train()
-    epoch_time = 0
-    keep_avg = KeepAverage()
-    if use_cuda:
-        batch_n_iter = int(
-            len(data_loader.dataset) / (c.batch_size * num_gpus))
-    else:
-        batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
-    end_time = time.time()
-    c_logger.print_train_start()
-    for num_iter, data in enumerate(data_loader):
-        start_time = time.time()
-
-        # format data
-        text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, avg_text_length, avg_spec_length = format_data(data)
-        loader_time = time.time() - end_time
-
-        global_step += 1
-
-        # setup lr
-        if c.noam_schedule:
-            scheduler.step()
-        optimizer.zero_grad()
-        if optimizer_st:
-            optimizer_st.zero_grad()
-
-        # forward pass model
-        if c.bidirectional_decoder or c.double_decoder_consistency:
-            decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
-                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
-        else:
-            decoder_output, postnet_output, alignments, stop_tokens = model(
-                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
-            decoder_backward_output = None
-            alignments_backward = None
-
-        # set the alignment lengths wrt reduction factor for guided attention
-        if mel_lengths.max() % model.decoder.r != 0:
-            alignment_lengths = (mel_lengths + (model.decoder.r - (mel_lengths.max() % model.decoder.r))) // model.decoder.r
-        else:
-            alignment_lengths = mel_lengths //  model.decoder.r
-
-        # compute loss
-        loss_dict = criterion(postnet_output, decoder_output, mel_input,
-                              linear_input, stop_tokens, stop_targets,
-                              mel_lengths, decoder_backward_output,
-                              alignments, alignment_lengths, alignments_backward,
-                              text_lengths)
-
-        # backward pass
-        loss_dict['loss'].backward()
-        optimizer, current_lr = adam_weight_decay(optimizer)
-        grad_norm, _ = check_update(model, c.grad_clip, ignore_stopnet=True)
-        optimizer.step()
-
-        # compute alignment error (the lower the better )
-        align_error = 1 - alignment_diagonal_score(alignments)
-        loss_dict['align_error'] = align_error
-
-        # backpass and check the grad norm for stop loss
-        if c.separate_stopnet:
-            loss_dict['stopnet_loss'].backward()
-            optimizer_st, _ = adam_weight_decay(optimizer_st)
-            grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
-            optimizer_st.step()
-        else:
-            grad_norm_st = 0
-
-        step_time = time.time() - start_time
-        epoch_time += step_time
-
-        # aggregate losses from processes
-        if num_gpus > 1:
-            loss_dict['postnet_loss'] = reduce_tensor(loss_dict['postnet_loss'].data, num_gpus)
-            loss_dict['decoder_loss'] = reduce_tensor(loss_dict['decoder_loss'].data, num_gpus)
-            loss_dict['loss'] = reduce_tensor(loss_dict['loss'] .data, num_gpus)
-            loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus) if c.stopnet else loss_dict['stopnet_loss']
-
-        # detach loss values
-        loss_dict_new = dict()
-        for key, value in loss_dict.items():
-            if isinstance(value, (int, float)):
-                loss_dict_new[key] = value
-            else:
-                loss_dict_new[key] = value.item()
-        loss_dict = loss_dict_new
-
-        # update avg stats
-        update_train_values = dict()
-        for key, value in loss_dict.items():
-            update_train_values['avg_' + key] = value
-        update_train_values['avg_loader_time'] = loader_time
-        update_train_values['avg_step_time'] = step_time
-        keep_avg.update_values(update_train_values)
-
-        # print training progress
-        if global_step % c.print_step == 0:
-            c_logger.print_train_step(batch_n_iter, num_iter, global_step,
-                                      avg_spec_length, avg_text_length,
-                                      step_time, loader_time, current_lr,
-                                      loss_dict, keep_avg.avg_values)
-
-        if args.rank == 0:
-            # Plot Training Iter Stats
-            # reduce TB load
-            if global_step % c.tb_plot_step == 0:
-                iter_stats = {
-                    "lr": current_lr,
-                    "grad_norm": grad_norm,
-                    "grad_norm_st": grad_norm_st,
-                    "step_time": step_time
-                }
-                iter_stats.update(loss_dict)
-                tb_logger.tb_train_iter_stats(global_step, iter_stats)
-
-            if global_step % c.save_step == 0:
-                if c.checkpoint:
-                    # save model
-                    save_checkpoint(model, optimizer, global_step, epoch, model.decoder.r, OUT_PATH,
-                                    optimizer_st=optimizer_st,
-                                    model_loss=loss_dict['postnet_loss'])
-
-                # Diagnostic visualizations
-                const_spec = postnet_output[0].data.cpu().numpy()
-                gt_spec = linear_input[0].data.cpu().numpy() if c.model in [
-                    "Tacotron", "TacotronGST"
-                ] else mel_input[0].data.cpu().numpy()
-                align_img = alignments[0].data.cpu().numpy()
-
-                figures = {
-                    "prediction": plot_spectrogram(const_spec, ap),
-                    "ground_truth": plot_spectrogram(gt_spec, ap),
-                    "alignment": plot_alignment(align_img),
-                }
-
-                if c.bidirectional_decoder or c.double_decoder_consistency:
-                    figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy())
-
-                tb_logger.tb_train_figures(global_step, figures)
-
-                # Sample audio
-                if c.model in ["Tacotron", "TacotronGST"]:
-                    train_audio = ap.inv_spectrogram(const_spec.T)
-                else:
-                    train_audio = ap.inv_melspectrogram(const_spec.T)
-                tb_logger.tb_train_audios(global_step,
-                                          {'TrainAudio': train_audio},
-                                          c.audio["sample_rate"])
-        end_time = time.time()
-
-    # print epoch stats
-    c_logger.print_train_epoch_end(global_step, epoch, epoch_time, keep_avg)
-
-    # Plot Epoch Stats
-    if args.rank == 0:
-        epoch_stats = {"epoch_time": epoch_time}
-        epoch_stats.update(keep_avg.avg_values)
-        tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
-        if c.tb_model_param_stats:
-            tb_logger.tb_model_weights(model, global_step)
-    return keep_avg.avg_values, global_step
-
-
-@torch.no_grad()
-def evaluate(model, criterion, ap, global_step, epoch):
-    data_loader = setup_loader(ap, model.decoder.r, is_val=True)
-    model.eval()
-    epoch_time = 0
-    keep_avg = KeepAverage()
-    c_logger.print_eval_start()
-    if data_loader is not None:
-        for num_iter, data in enumerate(data_loader):
-            start_time = time.time()
-
-            # format data
-            text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, _, _ = format_data(data)
-            assert mel_input.shape[1] % model.decoder.r == 0
-
-            # forward pass model
-            if c.bidirectional_decoder or c.double_decoder_consistency:
-                decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
-                    text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
-            else:
-                decoder_output, postnet_output, alignments, stop_tokens = model(
-                    text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
-                decoder_backward_output = None
-                alignments_backward = None
-
-            # set the alignment lengths wrt reduction factor for guided attention
-            if mel_lengths.max() % model.decoder.r != 0:
-                alignment_lengths = (mel_lengths + (model.decoder.r - (mel_lengths.max() % model.decoder.r))) // model.decoder.r
-            else:
-                alignment_lengths = mel_lengths //  model.decoder.r
-
-            # compute loss
-            loss_dict = criterion(postnet_output, decoder_output, mel_input,
-                                  linear_input, stop_tokens, stop_targets,
-                                  mel_lengths, decoder_backward_output,
-                                  alignments, alignment_lengths, alignments_backward,
-                                  text_lengths)
-
-            # step time
-            step_time = time.time() - start_time
-            epoch_time += step_time
-
-            # compute alignment score
-            align_error = 1 - alignment_diagonal_score(alignments)
-            loss_dict['align_error'] = align_error
-
-            # aggregate losses from processes
-            if num_gpus > 1:
-                loss_dict['postnet_loss'] = reduce_tensor(loss_dict['postnet_loss'].data, num_gpus)
-                loss_dict['decoder_loss'] = reduce_tensor(loss_dict['decoder_loss'].data, num_gpus)
-                if c.stopnet:
-                    loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus)
-
-            # detach loss values
-            loss_dict_new = dict()
-            for key, value in loss_dict.items():
-                if isinstance(value, (int, float)):
-                    loss_dict_new[key] = value
-                else:
-                    loss_dict_new[key] = value.item()
-            loss_dict = loss_dict_new
-
-            # update avg stats
-            update_train_values = dict()
-            for key, value in loss_dict.items():
-                update_train_values['avg_' + key] = value
-            keep_avg.update_values(update_train_values)
-
-            if c.print_eval:
-                c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
-
-        if args.rank == 0:
-            # Diagnostic visualizations
-            idx = np.random.randint(mel_input.shape[0])
-            const_spec = postnet_output[idx].data.cpu().numpy()
-            gt_spec = linear_input[idx].data.cpu().numpy() if c.model in [
-                "Tacotron", "TacotronGST"
-            ] else mel_input[idx].data.cpu().numpy()
-            align_img = alignments[idx].data.cpu().numpy()
-
-            eval_figures = {
-                "prediction": plot_spectrogram(const_spec, ap),
-                "ground_truth": plot_spectrogram(gt_spec, ap),
-                "alignment": plot_alignment(align_img)
-            }
-
-            # Sample audio
-            if c.model in ["Tacotron", "TacotronGST"]:
-                eval_audio = ap.inv_spectrogram(const_spec.T)
-            else:
-                eval_audio = ap.inv_melspectrogram(const_spec.T)
-            tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio},
-                                     c.audio["sample_rate"])
-
-            # Plot Validation Stats
-
-            if c.bidirectional_decoder or c.double_decoder_consistency:
-                align_b_img = alignments_backward[idx].data.cpu().numpy()
-                eval_figures['alignment2'] = plot_alignment(align_b_img)
-            tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
-            tb_logger.tb_eval_figures(global_step, eval_figures)
-
-    if args.rank == 0 and epoch > c.test_delay_epochs:
-        if c.test_sentences_file is None:
-            test_sentences = [
-                "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
-                "Be a voice, not an echo.",
-                "I'm sorry Dave. I'm afraid I can't do that.",
-                "This cake is great. It's so delicious and moist.",
-                "Prior to November 22, 1963."
-            ]
-        else:
-            with open(c.test_sentences_file, "r") as f:
-                test_sentences = [s.strip() for s in f.readlines()]
-
-        # test sentences
-        test_audios = {}
-        test_figures = {}
-        print(" | > Synthesizing test sentences")
-        speaker_id = 0 if c.use_speaker_embedding else None
-        style_wav = c.get("style_wav_for_test")
-        for idx, test_sentence in enumerate(test_sentences):
-            try:
-                wav, alignment, decoder_output, postnet_output, stop_tokens, inputs = synthesis(
-                    model,
-                    test_sentence,
-                    c,
-                    use_cuda,
-                    ap,
-                    speaker_id=speaker_id,
-                    style_wav=style_wav,
-                    truncated=False,
-                    enable_eos_bos_chars=c.enable_eos_bos_chars, #pylint: disable=unused-argument
-                    use_griffin_lim=True,
-                    do_trim_silence=False)
-
-                file_path = os.path.join(AUDIO_PATH, str(global_step))
-                os.makedirs(file_path, exist_ok=True)
-                file_path = os.path.join(file_path,
-                                         "TestSentence_{}.wav".format(idx))
-                ap.save_wav(wav, file_path)
-                test_audios['{}-audio'.format(idx)] = wav
-                test_figures['{}-prediction'.format(idx)] = plot_spectrogram(
-                    postnet_output, ap)
-                test_figures['{}-alignment'.format(idx)] = plot_alignment(
-                    alignment)
-            except:
-                print(" !! Error creating Test Sentence -", idx)
-                traceback.print_exc()
-        tb_logger.tb_test_audios(global_step, test_audios,
-                                 c.audio['sample_rate'])
-        tb_logger.tb_test_figures(global_step, test_figures)
-    return keep_avg.avg_values
-
-
-# FIXME: move args definition/parsing inside of main?
-def main(args):  # pylint: disable=redefined-outer-name
-    # pylint: disable=global-variable-undefined
-    global meta_data_train, meta_data_eval, symbols, phonemes
-    # Audio processor
-    ap = AudioProcessor(**c.audio)
-    if 'characters' in c.keys():
-        symbols, phonemes = make_symbols(**c.characters)
-
-    # DISTRUBUTED
-    if num_gpus > 1:
-        init_distributed(args.rank, num_gpus, args.group_id,
-                         c.distributed["backend"], c.distributed["url"])
-    num_chars = len(phonemes) if c.use_phonemes else len(symbols)
-
-    # load data instances
-    meta_data_train, meta_data_eval = load_meta_data(c.datasets)
-
-    # parse speakers
-    if c.use_speaker_embedding:
-        speakers = get_speakers(meta_data_train)
-        if args.restore_path:
-            prev_out_path = os.path.dirname(args.restore_path)
-            speaker_mapping = load_speaker_mapping(prev_out_path)
-            assert all([speaker in speaker_mapping
-                        for speaker in speakers]), "As of now you, you cannot " \
-                                                   "introduce new speakers to " \
-                                                   "a previously trained model."
-        else:
-            speaker_mapping = {name: i for i, name in enumerate(speakers)}
-        save_speaker_mapping(OUT_PATH, speaker_mapping)
-        num_speakers = len(speaker_mapping)
-        print("Training with {} speakers: {}".format(num_speakers,
-                                                     ", ".join(speakers)))
-    else:
-        num_speakers = 0
-
-    model = setup_model(num_chars, num_speakers, c)
-
-    params = set_weight_decay(model, c.wd)
-    optimizer = RAdam(params, lr=c.lr, weight_decay=0)
-    if c.stopnet and c.separate_stopnet:
-        optimizer_st = RAdam(model.decoder.stopnet.parameters(),
-                             lr=c.lr,
-                             weight_decay=0)
-    else:
-        optimizer_st = None
-
-    # setup criterion
-    criterion = TacotronLoss(c, stopnet_pos_weight=10.0, ga_sigma=0.4)
-
-    if args.restore_path:
-        checkpoint = torch.load(args.restore_path, map_location='cpu')
-        try:
-            # TODO: fix optimizer init, model.cuda() needs to be called before
-            # optimizer restore
-            # optimizer.load_state_dict(checkpoint['optimizer'])
-            if c.reinit_layers:
-                raise RuntimeError
-            model.load_state_dict(checkpoint['model'])
-        except:
-            print(" > Partial model initialization.")
-            model_dict = model.state_dict()
-            model_dict = set_init_dict(model_dict, checkpoint['model'], c)
-            model.load_state_dict(model_dict)
-            del model_dict
-        for group in optimizer.param_groups:
-            group['lr'] = c.lr
-        print(" > Model restored from step %d" % checkpoint['step'],
-              flush=True)
-        args.restore_step = checkpoint['step']
-    else:
-        args.restore_step = 0
-
-    if use_cuda:
-        model.cuda()
-        criterion.cuda()
-
-    # DISTRUBUTED
-    if num_gpus > 1:
-        model = apply_gradient_allreduce(model)
-
-    if c.noam_schedule:
-        scheduler = NoamLR(optimizer,
-                           warmup_steps=c.warmup_steps,
-                           last_epoch=args.restore_step - 1)
-    else:
-        scheduler = None
-
-    num_params = count_parameters(model)
-    print("\n > Model has {} parameters".format(num_params), flush=True)
-
-    if 'best_loss' not in locals():
-        best_loss = float('inf')
-
-    global_step = args.restore_step
-    for epoch in range(0, c.epochs):
-        c_logger.print_epoch_start(epoch, c.epochs)
-        # set gradual training
-        if c.gradual_training is not None:
-            r, c.batch_size = gradual_training_scheduler(global_step, c)
-            c.r = r
-            model.decoder.set_r(r)
-            if c.bidirectional_decoder:
-                model.decoder_backward.set_r(r)
-            print("\n > Number of output frames:", model.decoder.r)
-        train_avg_loss_dict, global_step = train(model, criterion, optimizer,
-                                                 optimizer_st, scheduler, ap,
-                                                 global_step, epoch)
-        eval_avg_loss_dict = evaluate(model, criterion, ap, global_step, epoch)
-        c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
-        target_loss = train_avg_loss_dict['avg_postnet_loss']
-        if c.run_eval:
-            target_loss = eval_avg_loss_dict['avg_postnet_loss']
-        best_loss = save_best_model(target_loss, best_loss, model, optimizer, global_step, epoch, c.r,
-                                    OUT_PATH)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--continue_path',
-        type=str,
-        help='Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
-        default='',
-        required='--config_path' not in sys.argv)
-    parser.add_argument(
-        '--restore_path',
-        type=str,
-        help='Model file to be restored. Use to finetune a model.',
-        default='')
-    parser.add_argument(
-        '--config_path',
-        type=str,
-        help='Path to config file for training.',
-        required='--continue_path' not in sys.argv
-    )
-    parser.add_argument('--debug',
-                        type=bool,
-                        default=False,
-                        help='Do not verify commit integrity to run training.')
-
-    # DISTRUBUTED
-    parser.add_argument(
-        '--rank',
-        type=int,
-        default=0,
-        help='DISTRIBUTED: process rank for distributed training.')
-    parser.add_argument('--group_id',
-                        type=str,
-                        default="",
-                        help='DISTRIBUTED: process group id.')
-    args = parser.parse_args()
-
-    if args.continue_path != '':
-        args.output_path = args.continue_path
-        args.config_path = os.path.join(args.continue_path, 'config.json')
-        list_of_files = glob.glob(args.continue_path + "/*.pth.tar") # * means all if need specific format then *.csv
-        latest_model_file = max(list_of_files, key=os.path.getctime)
-        args.restore_path = latest_model_file
-        print(f" > Training continues for {args.restore_path}")
-
-    # setup output paths and read configs
-    c = load_config(args.config_path)
-    check_config(c)
-    _ = os.path.dirname(os.path.realpath(__file__))
-
-    OUT_PATH = args.continue_path
-    if args.continue_path == '':
-        OUT_PATH = create_experiment_folder(c.output_path, c.run_name, args.debug)
-
-    AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios')
-
-    c_logger = ConsoleLogger()
-
-    if args.rank == 0:
-        os.makedirs(AUDIO_PATH, exist_ok=True)
-        new_fields = {}
-        if args.restore_path:
-            new_fields["restore_path"] = args.restore_path
-        new_fields["github_branch"] = get_git_branch()
-        copy_config_file(args.config_path,
-                         os.path.join(OUT_PATH, 'config.json'), new_fields)
-        os.chmod(AUDIO_PATH, 0o775)
-        os.chmod(OUT_PATH, 0o775)
-
-        LOG_DIR = OUT_PATH
-        tb_logger = TensorboardLogger(LOG_DIR, model_name='TTS')
-
-        # write model desc to tensorboard
-        tb_logger.tb_add_text('model-description', c['run_description'], 0)
-
-    try:
-        main(args)
-    except KeyboardInterrupt:
-        remove_experiment_folder(OUT_PATH)
-        try:
-            sys.exit(0)
-        except SystemExit:
-            os._exit(0)  # pylint: disable=protected-access
-    except Exception:  # pylint: disable=broad-except
-        remove_experiment_folder(OUT_PATH)
-        traceback.print_exc()
-        sys.exit(1)
diff --git a/tts_namespace/README.md b/tts_namespace/README.md
deleted file mode 100644
index c5b2ddbf..00000000
--- a/tts_namespace/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-This folder contains a symlink called TTS to the parent folder:
-
-    lrwxr-xr-x  TTS -> ..
-
-This is used to appease the distribute/setuptools gods. When the project was
-initially set up, the repository folder itself was considered a namespace, and
-development was done with `sys.path` hacks. This means if you tried to install
-TTS, `setup.py` would see the packages `models`, `utils`, `layers`... instead of
- `TTS.models`, `TTS.utils`...
-
-Installing TTS would then pollute the package namespace with generic names like
-those above. In order to make things installable in both install and development
-modes (`pip install /path/to/TTS` and `pip install -e /path/to/TTS`), we needed
-to add an additional 'TTS' namespace to avoid this pollution. A virtual redirect
-using `packages_dir` in `setup.py` is not enough because it breaks the editable
-installation, which can only handle the simplest of `package_dir` redirects.
-
-Our solution is to use a symlink in order to add the extra `TTS` namespace. In
-`setup.py`, we only look for packages inside `tts_namespace` (this folder),
-which contains a symlink called TTS pointing to the repository root. The final
-result is that `setuptools.find_packages` will find `TTS.models`, `TTS.utils`...
-
-With this hack, `pip install -e` will then add a symlink to the `tts_namespace`
-in your `site-packages` folder, which works properly. It's important not to add
-anything else in this folder because it will pollute the package namespace when
-installing the project.
-
-This does not work if you check out your project on a filesystem that does not
-support symlinks.
\ No newline at end of file
diff --git a/tts_namespace/TTS b/tts_namespace/TTS
deleted file mode 120000
index a96aa0ea..00000000
--- a/tts_namespace/TTS
+++ /dev/null
@@ -1 +0,0 @@
-..
\ No newline at end of file
diff --git a/utils/.generic_utils.py.swo b/utils/.generic_utils.py.swo
deleted file mode 100644
index ab1b3870..00000000
Binary files a/utils/.generic_utils.py.swo and /dev/null differ
diff --git a/utils/.model.py.swp b/utils/.model.py.swp
deleted file mode 100644
index 24a8152e..00000000
Binary files a/utils/.model.py.swp and /dev/null differ
diff --git a/utils/__init__.py b/utils/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/utils/audio.py b/utils/audio.py
deleted file mode 100644
index 5b5b5059..00000000
--- a/utils/audio.py
+++ /dev/null
@@ -1,356 +0,0 @@
-import librosa
-import soundfile as sf
-import numpy as np
-import scipy.io
-import scipy.signal
-
-from TTS.utils.data import StandardScaler
-
-
-class AudioProcessor(object):
-    def __init__(self,
-                 sample_rate=None,
-                 num_mels=None,
-                 min_level_db=None,
-                 frame_shift_ms=None,
-                 frame_length_ms=None,
-                 hop_length=None,
-                 win_length=None,
-                 ref_level_db=None,
-                 fft_size=1024,
-                 power=None,
-                 preemphasis=0.0,
-                 signal_norm=None,
-                 symmetric_norm=None,
-                 max_norm=None,
-                 mel_fmin=None,
-                 mel_fmax=None,
-                 spec_gain=20,
-                 stft_pad_mode='reflect',
-                 clip_norm=True,
-                 griffin_lim_iters=None,
-                 do_trim_silence=False,
-                 trim_db=60,
-                 do_sound_norm=False,
-                 stats_path=None,
-                 **_):
-
-        print(" > Setting up Audio Processor...")
-        # setup class attributed
-        self.sample_rate = sample_rate
-        self.num_mels = num_mels
-        self.min_level_db = min_level_db or 0
-        self.frame_shift_ms = frame_shift_ms
-        self.frame_length_ms = frame_length_ms
-        self.ref_level_db = ref_level_db
-        self.fft_size = fft_size
-        self.power = power
-        self.preemphasis = preemphasis
-        self.griffin_lim_iters = griffin_lim_iters
-        self.signal_norm = signal_norm
-        self.symmetric_norm = symmetric_norm
-        self.mel_fmin = mel_fmin or 0
-        self.mel_fmax = mel_fmax
-        self.spec_gain = float(spec_gain)
-        self.stft_pad_mode = 'reflect'
-        self.max_norm = 1.0 if max_norm is None else float(max_norm)
-        self.clip_norm = clip_norm
-        self.do_trim_silence = do_trim_silence
-        self.trim_db = trim_db
-        self.do_sound_norm = do_sound_norm
-        self.stats_path = stats_path
-        # setup stft parameters
-        if hop_length is None:
-            # compute stft parameters from given time values
-            self.hop_length, self.win_length = self._stft_parameters()
-        else:
-            # use stft parameters from config file
-            self.hop_length = hop_length
-            self.win_length = win_length
-        assert min_level_db != 0.0, " [!] min_level_db is 0"
-        assert self.win_length <= self.fft_size, " [!] win_length cannot be larger than fft_size"
-        members = vars(self)
-        for key, value in members.items():
-            print(" | > {}:{}".format(key, value))
-        # create spectrogram utils
-        self.mel_basis = self._build_mel_basis()
-        self.inv_mel_basis = np.linalg.pinv(self._build_mel_basis())
-        # setup scaler
-        if stats_path:
-            mel_mean, mel_std, linear_mean, linear_std, _ = self.load_stats(stats_path)
-            self.setup_scaler(mel_mean, mel_std, linear_mean, linear_std)
-            self.signal_norm = True
-            self.max_norm = None
-            self.clip_norm = None
-            self.symmetric_norm = None
-
-    ### setting up the parameters ###
-    def _build_mel_basis(self, ):
-        if self.mel_fmax is not None:
-            assert self.mel_fmax <= self.sample_rate // 2
-        return librosa.filters.mel(
-            self.sample_rate,
-            self.fft_size,
-            n_mels=self.num_mels,
-            fmin=self.mel_fmin,
-            fmax=self.mel_fmax)
-
-    def _stft_parameters(self, ):
-        """Compute necessary stft parameters with given time values"""
-        factor = self.frame_length_ms / self.frame_shift_ms
-        assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
-        hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
-        win_length = int(hop_length * factor)
-        return hop_length, win_length
-
-    ### normalization ###
-    def _normalize(self, S):
-        """Put values in [0, self.max_norm] or [-self.max_norm, self.max_norm]"""
-        #pylint: disable=no-else-return
-        S = S.copy()
-        if self.signal_norm:
-            # mean-var scaling
-            if hasattr(self, 'mel_scaler'):
-                if S.shape[0] == self.num_mels:
-                    return self.mel_scaler.transform(S.T).T
-                elif S.shape[0] == self.fft_size / 2:
-                    return self.linear_scaler.transform(S.T).T
-                else:
-                    raise RuntimeError(' [!] Mean-Var stats does not match the given feature dimensions.')
-            # range normalization
-            S -= self.ref_level_db  # discard certain range of DB assuming it is air noise
-            S_norm = ((S - self.min_level_db) / (-self.min_level_db))
-            if self.symmetric_norm:
-                S_norm = ((2 * self.max_norm) * S_norm) - self.max_norm
-                if self.clip_norm:
-                    S_norm = np.clip(S_norm, -self.max_norm, self.max_norm)
-                return S_norm
-            else:
-                S_norm = self.max_norm * S_norm
-                if self.clip_norm:
-                    S_norm = np.clip(S_norm, 0, self.max_norm)
-                return S_norm
-        else:
-            return S
-
-    def _denormalize(self, S):
-        """denormalize values"""
-        #pylint: disable=no-else-return
-        S_denorm = S.copy()
-        if self.signal_norm:
-            # mean-var scaling
-            if hasattr(self, 'mel_scaler'):
-                if S_denorm.shape[0] == self.num_mels:
-                    return self.mel_scaler.inverse_transform(S_denorm.T).T
-                elif S_denorm.shape[0] == self.fft_size / 2:
-                    return self.linear_scaler.inverse_transform(S_denorm.T).T
-                else:
-                    raise RuntimeError(' [!] Mean-Var stats does not match the given feature dimensions.')
-            if self.symmetric_norm:
-                if self.clip_norm:
-                    S_denorm = np.clip(S_denorm, -self.max_norm, self.max_norm)
-                S_denorm = ((S_denorm + self.max_norm) * -self.min_level_db / (2 * self.max_norm)) + self.min_level_db
-                return S_denorm + self.ref_level_db
-            else:
-                if self.clip_norm:
-                    S_denorm = np.clip(S_denorm, 0, self.max_norm)
-                S_denorm = (S_denorm * -self.min_level_db /
-                            self.max_norm) + self.min_level_db
-                return S_denorm + self.ref_level_db
-        else:
-            return S_denorm
-
-    ### Mean-STD scaling ###
-    def load_stats(self, stats_path):
-        stats = np.load(stats_path, allow_pickle=True).item()  #pylint: disable=unexpected-keyword-arg
-        mel_mean = stats['mel_mean']
-        mel_std = stats['mel_std']
-        linear_mean = stats['linear_mean']
-        linear_std = stats['linear_std']
-        stats_config = stats['audio_config']
-        # check all audio parameters used for computing stats
-        skip_parameters = ['griffin_lim_iters', 'stats_path', 'do_trim_silence', 'ref_level_db', 'power']
-        for key in stats_config.keys():
-            if key in skip_parameters:
-                continue
-            assert stats_config[key] == self.__dict__[key],\
-                f" [!] Audio param {key} does not match the value used for computing mean-var stats. {stats_config[key]} vs {self.__dict__[key]}"
-        return mel_mean, mel_std, linear_mean, linear_std, stats_config
-
-    # pylint: disable=attribute-defined-outside-init
-    def setup_scaler(self, mel_mean, mel_std, linear_mean, linear_std):
-        self.mel_scaler = StandardScaler()
-        self.mel_scaler.set_stats(mel_mean, mel_std)
-        self.linear_scaler = StandardScaler()
-        self.linear_scaler.set_stats(linear_mean, linear_std)
-
-    ### DB and AMP conversion ###
-    # pylint: disable=no-self-use
-    def _amp_to_db(self, x):
-        return self.spec_gain * np.log10(np.maximum(1e-5, x))
-
-    # pylint: disable=no-self-use
-    def _db_to_amp(self, x):
-        return np.power(10.0, x / self.spec_gain)
-
-    ### Preemphasis ###
-    def apply_preemphasis(self, x):
-        if self.preemphasis == 0:
-            raise RuntimeError(" [!] Preemphasis is set 0.0.")
-        return scipy.signal.lfilter([1, -self.preemphasis], [1], x)
-
-    def apply_inv_preemphasis(self, x):
-        if self.preemphasis == 0:
-            raise RuntimeError(" [!] Preemphasis is set 0.0.")
-        return scipy.signal.lfilter([1], [1, -self.preemphasis], x)
-
-    ### SPECTROGRAMs ###
-    def _linear_to_mel(self, spectrogram):
-        return np.dot(self.mel_basis, spectrogram)
-
-    def _mel_to_linear(self, mel_spec):
-        return np.maximum(1e-10, np.dot(self.inv_mel_basis, mel_spec))
-
-    def spectrogram(self, y):
-        if self.preemphasis != 0:
-            D = self._stft(self.apply_preemphasis(y))
-        else:
-            D = self._stft(y)
-        S = self._amp_to_db(np.abs(D))
-        return self._normalize(S)
-
-    def melspectrogram(self, y):
-        if self.preemphasis != 0:
-            D = self._stft(self.apply_preemphasis(y))
-        else:
-            D = self._stft(y)
-        S = self._amp_to_db(self._linear_to_mel(np.abs(D)))
-        return self._normalize(S)
-
-    def inv_spectrogram(self, spectrogram):
-        """Converts spectrogram to waveform using librosa"""
-        S = self._denormalize(spectrogram)
-        S = self._db_to_amp(S)
-        # Reconstruct phase
-        if self.preemphasis != 0:
-            return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
-        return self._griffin_lim(S**self.power)
-
-    def inv_melspectrogram(self, mel_spectrogram):
-        '''Converts melspectrogram to waveform using librosa'''
-        D = self._denormalize(mel_spectrogram)
-        S = self._db_to_amp(D)
-        S = self._mel_to_linear(S)  # Convert back to linear
-        if self.preemphasis != 0:
-            return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
-        return self._griffin_lim(S**self.power)
-
-    def out_linear_to_mel(self, linear_spec):
-        S = self._denormalize(linear_spec)
-        S = self._db_to_amp(S)
-        S = self._linear_to_mel(np.abs(S))
-        S = self._amp_to_db(S)
-        mel = self._normalize(S)
-        return mel
-
-    ### STFT and ISTFT ###
-    def _stft(self, y):
-        return librosa.stft(
-            y=y,
-            n_fft=self.fft_size,
-            hop_length=self.hop_length,
-            win_length=self.win_length,
-            pad_mode=self.stft_pad_mode,
-        )
-
-    def _istft(self, y):
-        return librosa.istft(
-            y, hop_length=self.hop_length, win_length=self.win_length)
-
-    def _griffin_lim(self, S):
-        angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
-        S_complex = np.abs(S).astype(np.complex)
-        y = self._istft(S_complex * angles)
-        for _ in range(self.griffin_lim_iters):
-            angles = np.exp(1j * np.angle(self._stft(y)))
-            y = self._istft(S_complex * angles)
-        return y
-
-    def compute_stft_paddings(self, x, pad_sides=1):
-        '''compute right padding (final frame) or both sides padding (first and final frames)
-        '''
-        assert pad_sides in (1, 2)
-        pad = (x.shape[0] // self.hop_length + 1) * self.hop_length - x.shape[0]
-        if pad_sides == 1:
-            return 0, pad
-        return pad // 2, pad // 2 + pad % 2
-
-    ### Audio Processing ###
-    def find_endpoint(self, wav, threshold_db=-40, min_silence_sec=0.8):
-        window_length = int(self.sample_rate * min_silence_sec)
-        hop_length = int(window_length / 4)
-        threshold = self._db_to_amp(threshold_db)
-        for x in range(hop_length, len(wav) - window_length, hop_length):
-            if np.max(wav[x:x + window_length]) < threshold:
-                return x + hop_length
-        return len(wav)
-
-    def trim_silence(self, wav):
-        """ Trim silent parts with a threshold and 0.01 sec margin """
-        margin = int(self.sample_rate * 0.01)
-        wav = wav[margin:-margin]
-        return librosa.effects.trim(
-            wav, top_db=self.trim_db, frame_length=self.win_length, hop_length=self.hop_length)[0]
-
-    @staticmethod
-    def sound_norm(x):
-        return x / abs(x).max() * 0.9
-
-    ### save and load ###
-    def load_wav(self, filename, sr=None):
-        if sr is None:
-            x, sr = sf.read(filename)
-        else:
-            x, sr = librosa.load(filename, sr=sr)
-        if self.do_trim_silence:
-            try:
-                x = self.trim_silence(x)
-            except ValueError:
-                print(f' [!] File cannot be trimmed for silence - {filename}')
-        assert self.sample_rate == sr, "%s vs %s"%(self.sample_rate, sr)
-        if self.do_sound_norm:
-            x = self.sound_norm(x)
-        return x
-
-    def save_wav(self, wav, path):
-        wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
-        scipy.io.wavfile.write(path, self.sample_rate, wav_norm.astype(np.int16))
-
-    @staticmethod
-    def mulaw_encode(wav, qc):
-        mu = 2 ** qc - 1
-        # wav_abs = np.minimum(np.abs(wav), 1.0)
-        signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1. + mu)
-        # Quantize signal to the specified number of levels.
-        signal = (signal + 1) / 2 * mu + 0.5
-        return np.floor(signal,)
-
-    @staticmethod
-    def mulaw_decode(wav, qc):
-        """Recovers waveform from quantized values."""
-        mu = 2 ** qc - 1
-        x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
-        return x
-
-
-    @staticmethod
-    def encode_16bits(x):
-        return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)
-
-    @staticmethod
-    def quantize(x, bits):
-        return (x + 1.) * (2**bits - 1) / 2
-
-    @staticmethod
-    def dequantize(x, bits):
-        return 2 * x / (2**bits - 1) - 1
diff --git a/utils/console_logger.py b/utils/console_logger.py
deleted file mode 100644
index 85d5b376..00000000
--- a/utils/console_logger.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import datetime
-from TTS.utils.io import AttrDict
-
-
-tcolors = AttrDict({
-    'OKBLUE': '\033[94m',
-    'HEADER': '\033[95m',
-    'OKGREEN': '\033[92m',
-    'WARNING': '\033[93m',
-    'FAIL': '\033[91m',
-    'ENDC': '\033[0m',
-    'BOLD': '\033[1m',
-    'UNDERLINE': '\033[4m'
-})
-
-
-class ConsoleLogger():
-    def __init__(self):
-        # TODO: color code for value changes
-        # use these to compare values between iterations
-        self.old_train_loss_dict = None
-        self.old_epoch_loss_dict = None
-        self.old_eval_loss_dict = None
-
-    # pylint: disable=no-self-use
-    def get_time(self):
-        now = datetime.datetime.now()
-        return now.strftime("%Y-%m-%d %H:%M:%S")
-
-    def print_epoch_start(self, epoch, max_epoch):
-        print("\n{}{} > EPOCH: {}/{}{}".format(tcolors.UNDERLINE, tcolors.BOLD,
-                                               epoch, max_epoch, tcolors.ENDC),
-              flush=True)
-
-    def print_train_start(self):
-        print(f"\n{tcolors.BOLD} > TRAINING ({self.get_time()}) {tcolors.ENDC}")
-
-    def print_train_step(self, batch_steps, step, global_step, avg_spec_length,
-                         avg_text_length, step_time, loader_time, lr,
-                         loss_dict, avg_loss_dict):
-        indent = "     | > "
-        print()
-        log_text = "{}   --> STEP: {}/{} -- GLOBAL_STEP: {}{}\n".format(
-            tcolors.BOLD, step, batch_steps, global_step, tcolors.ENDC)
-        for key, value in loss_dict.items():
-            # print the avg value if given
-            if f'avg_{key}' in avg_loss_dict.keys():
-                log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
-            else:
-                log_text += "{}{}: {:.5f} \n".format(indent, key, value)
-        log_text += f"{indent}avg_spec_len: {avg_spec_length}\n{indent}avg_text_len: {avg_text_length}\n{indent}"\
-            f"step_time: {step_time:.2f}\n{indent}loader_time: {loader_time:.2f}\n{indent}lr: {lr:.5f}"
-        print(log_text, flush=True)
-
-    # pylint: disable=unused-argument
-    def print_train_epoch_end(self, global_step, epoch, epoch_time,
-                              print_dict):
-        indent = "     | > "
-        log_text = f"\n{tcolors.BOLD}   --> TRAIN PERFORMACE -- EPOCH TIME: {epoch_time:.2f} sec -- GLOBAL_STEP: {global_step}{tcolors.ENDC}\n"
-        for key, value in print_dict.items():
-            log_text += "{}{}: {:.5f}\n".format(indent, key, value)
-        print(log_text, flush=True)
-
-    def print_eval_start(self):
-        print(f"{tcolors.BOLD} > EVALUATION {tcolors.ENDC}\n")
-
-    def print_eval_step(self, step, loss_dict, avg_loss_dict):
-        indent = "     | > "
-        print()
-        log_text = f"{tcolors.BOLD}   --> STEP: {step}{tcolors.ENDC}\n"
-        for key, value in loss_dict.items():
-            # print the avg value if given
-            if f'avg_{key}' in avg_loss_dict.keys():
-                log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
-            else:
-                log_text += "{}{}: {:.5f} \n".format(indent, key, value)
-        print(log_text, flush=True)
-
-    def print_epoch_end(self, epoch, avg_loss_dict):
-        indent = "     | > "
-        log_text = "  {}--> EVAL PERFORMANCE{}\n".format(
-            tcolors.BOLD, tcolors.ENDC)
-        for key, value in avg_loss_dict.items():
-            # print the avg value if given
-            color = tcolors.FAIL
-            sign = '+'
-            diff = 0
-            if self.old_eval_loss_dict is not None:
-                diff = value - self.old_eval_loss_dict[key]
-                if diff <= 0:
-                    color = tcolors.OKGREEN
-                    sign = ''
-            log_text += "{}{}:{} {:.5f} {}({}{:.5f})\n".format(indent, key, color, value, tcolors.ENDC, sign, diff)
-        self.old_eval_loss_dict = avg_loss_dict
-        print(log_text, flush=True)
diff --git a/utils/data.py b/utils/data.py
deleted file mode 100644
index a83325cb..00000000
--- a/utils/data.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import numpy as np
-
-
-def _pad_data(x, length):
-    _pad = 0
-    assert x.ndim == 1
-    return np.pad(
-        x, (0, length - x.shape[0]), mode='constant', constant_values=_pad)
-
-
-def prepare_data(inputs):
-    max_len = max((len(x) for x in inputs))
-    return np.stack([_pad_data(x, max_len) for x in inputs])
-
-
-def _pad_tensor(x, length):
-    _pad = 0.
-    assert x.ndim == 2
-    x = np.pad(
-        x, [[0, 0], [0, length - x.shape[1]]],
-        mode='constant',
-        constant_values=_pad)
-    return x
-
-
-def prepare_tensor(inputs, out_steps):
-    max_len = max((x.shape[1] for x in inputs))
-    remainder = max_len % out_steps
-    pad_len = max_len + (out_steps - remainder) if remainder > 0 else max_len
-    return np.stack([_pad_tensor(x, pad_len) for x in inputs])
-
-
-def _pad_stop_target(x, length):
-    _pad = 0.
-    assert x.ndim == 1
-    return np.pad(
-        x, (0, length - x.shape[0]), mode='constant', constant_values=_pad)
-
-
-def prepare_stop_target(inputs, out_steps):
-    """ Pad row vectors with 1. """
-    max_len = max((x.shape[0] for x in inputs))
-    remainder = max_len % out_steps
-    pad_len = max_len + (out_steps - remainder) if remainder > 0 else max_len
-    return np.stack([_pad_stop_target(x, pad_len) for x in inputs])
-
-
-def pad_per_step(inputs, pad_len):
-    return np.pad(
-        inputs, [[0, 0], [0, 0], [0, pad_len]],
-        mode='constant',
-        constant_values=0.0)
-
-
-# pylint: disable=attribute-defined-outside-init
-class StandardScaler():
-
-    def set_stats(self, mean, scale):
-        self.mean_ = mean
-        self.scale_ = scale
-
-    def reset_stats(self):
-        delattr(self, 'mean_')
-        delattr(self, 'scale_')
-
-    def transform(self, X):
-        X = np.asarray(X)
-        X -= self.mean_
-        X /= self.scale_
-        return X
-
-    def inverse_transform(self, X):
-        X = np.asarray(X)
-        X *= self.scale_
-        X += self.mean_
-        return X
-
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
deleted file mode 100644
index c806bdf3..00000000
--- a/utils/generic_utils.py
+++ /dev/null
@@ -1,362 +0,0 @@
-import os
-import glob
-import torch
-import shutil
-import datetime
-import subprocess
-import importlib
-import numpy as np
-from collections import Counter
-
-
-def get_git_branch():
-    try:
-        out = subprocess.check_output(["git", "branch"]).decode("utf8")
-        current = next(line for line in out.split("\n")
-                       if line.startswith("*"))
-        current.replace("* ", "")
-    except subprocess.CalledProcessError:
-        current = "inside_docker"
-    return current
-
-
-def get_commit_hash():
-    """https://stackoverflow.com/questions/14989858/get-the-current-git-hash-in-a-python-script"""
-    # try:
-    #     subprocess.check_output(['git', 'diff-index', '--quiet',
-    #                              'HEAD'])  # Verify client is clean
-    # except:
-    #     raise RuntimeError(
-    #         " !! Commit before training to get the commit hash.")
-    try:
-        commit = subprocess.check_output(
-            ['git', 'rev-parse', '--short', 'HEAD']).decode().strip()
-    # Not copying .git folder into docker container
-    except subprocess.CalledProcessError:
-        commit = "0000000"
-    print(' > Git Hash: {}'.format(commit))
-    return commit
-
-
-def create_experiment_folder(root_path, model_name, debug):
-    """ Create a folder with the current date and time """
-    date_str = datetime.datetime.now().strftime("%B-%d-%Y_%I+%M%p")
-    if debug:
-        commit_hash = 'debug'
-    else:
-        commit_hash = get_commit_hash()
-    output_folder = os.path.join(
-        root_path, model_name + '-' + date_str + '-' + commit_hash)
-    os.makedirs(output_folder, exist_ok=True)
-    print(" > Experiment folder: {}".format(output_folder))
-    return output_folder
-
-
-def remove_experiment_folder(experiment_path):
-    """Check folder if there is a checkpoint, otherwise remove the folder"""
-
-    checkpoint_files = glob.glob(experiment_path + "/*.pth.tar")
-    if not checkpoint_files:
-        if os.path.exists(experiment_path):
-            shutil.rmtree(experiment_path, ignore_errors=True)
-            print(" ! Run is removed from {}".format(experiment_path))
-    else:
-        print(" ! Run is kept in {}".format(experiment_path))
-
-
-def count_parameters(model):
-    r"""Count number of trainable parameters in a network"""
-    return sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-
-def split_dataset(items):
-    is_multi_speaker = False
-    speakers = [item[-1] for item in items]
-    is_multi_speaker = len(set(speakers)) > 1
-    eval_split_size = 500 if len(items) * 0.01 > 500 else int(
-        len(items) * 0.01)
-    assert eval_split_size > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
-    np.random.seed(0)
-    np.random.shuffle(items)
-    if is_multi_speaker:
-        items_eval = []
-        # most stupid code ever -- Fix it !
-        while len(items_eval) < eval_split_size:
-            speakers = [item[-1] for item in items]
-            speaker_counter = Counter(speakers)
-            item_idx = np.random.randint(0, len(items))
-            if speaker_counter[items[item_idx][-1]] > 1:
-                items_eval.append(items[item_idx])
-                del items[item_idx]
-        return items_eval, items
-    return items[:eval_split_size], items[eval_split_size:]
-
-
-# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
-def sequence_mask(sequence_length, max_len=None):
-    if max_len is None:
-        max_len = sequence_length.data.max()
-    batch_size = sequence_length.size(0)
-    seq_range = torch.arange(0, max_len).long()
-    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
-    if sequence_length.is_cuda:
-        seq_range_expand = seq_range_expand.to(sequence_length.device)
-    seq_length_expand = (
-        sequence_length.unsqueeze(1).expand_as(seq_range_expand))
-    # B x T_max
-    return seq_range_expand < seq_length_expand
-
-
-def set_init_dict(model_dict, checkpoint_state, c):
-    # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
-    for k, v in checkpoint_state.items():
-        if k not in model_dict:
-            print(" | > Layer missing in the model definition: {}".format(k))
-    # 1. filter out unnecessary keys
-    pretrained_dict = {
-        k: v
-        for k, v in checkpoint_state.items() if k in model_dict
-    }
-    # 2. filter out different size layers
-    pretrained_dict = {
-        k: v
-        for k, v in pretrained_dict.items()
-        if v.numel() == model_dict[k].numel()
-    }
-    # 3. skip reinit layers
-    if c.reinit_layers is not None:
-        for reinit_layer_name in c.reinit_layers:
-            pretrained_dict = {
-                k: v
-                for k, v in pretrained_dict.items()
-                if reinit_layer_name not in k
-            }
-    # 4. overwrite entries in the existing state dict
-    model_dict.update(pretrained_dict)
-    print(" | > {} / {} layers are restored.".format(len(pretrained_dict),
-                                                     len(model_dict)))
-    return model_dict
-
-
-def setup_model(num_chars, num_speakers, c):
-    print(" > Using model: {}".format(c.model))
-    MyModel = importlib.import_module('TTS.models.' + c.model.lower())
-    MyModel = getattr(MyModel, c.model)
-    if c.model.lower() in "tacotron":
-        model = MyModel(num_chars=num_chars,
-                        num_speakers=num_speakers,
-                        r=c.r,
-                        postnet_output_dim=int(c.audio['fft_size'] / 2 + 1),
-                        decoder_output_dim=c.audio['num_mels'],
-                        gst=c.use_gst,
-                        memory_size=c.memory_size,
-                        attn_type=c.attention_type,
-                        attn_win=c.windowing,
-                        attn_norm=c.attention_norm,
-                        prenet_type=c.prenet_type,
-                        prenet_dropout=c.prenet_dropout,
-                        forward_attn=c.use_forward_attn,
-                        trans_agent=c.transition_agent,
-                        forward_attn_mask=c.forward_attn_mask,
-                        location_attn=c.location_attn,
-                        attn_K=c.attention_heads,
-                        separate_stopnet=c.separate_stopnet,
-                        bidirectional_decoder=c.bidirectional_decoder,
-                        double_decoder_consistency=c.double_decoder_consistency,
-                        ddc_r=c.ddc_r)
-    elif c.model.lower() == "tacotron2":
-        model = MyModel(num_chars=num_chars,
-                        num_speakers=num_speakers,
-                        r=c.r,
-                        postnet_output_dim=c.audio['num_mels'],
-                        decoder_output_dim=c.audio['num_mels'],
-                        gst=c.use_gst,
-                        attn_type=c.attention_type,
-                        attn_win=c.windowing,
-                        attn_norm=c.attention_norm,
-                        prenet_type=c.prenet_type,
-                        prenet_dropout=c.prenet_dropout,
-                        forward_attn=c.use_forward_attn,
-                        trans_agent=c.transition_agent,
-                        forward_attn_mask=c.forward_attn_mask,
-                        location_attn=c.location_attn,
-                        attn_K=c.attention_heads,
-                        separate_stopnet=c.separate_stopnet,
-                        bidirectional_decoder=c.bidirectional_decoder,
-                        double_decoder_consistency=c.double_decoder_consistency,
-                        ddc_r=c.ddc_r)
-    return model
-
-class KeepAverage():
-    def __init__(self):
-        self.avg_values = {}
-        self.iters = {}
-
-    def __getitem__(self, key):
-        return self.avg_values[key]
-
-    def items(self):
-        return self.avg_values.items()
-
-    def add_value(self, name, init_val=0, init_iter=0):
-        self.avg_values[name] = init_val
-        self.iters[name] = init_iter
-
-    def update_value(self, name, value, weighted_avg=False):
-        if name not in self.avg_values:
-            # add value if not exist before
-            self.add_value(name, init_val=value)
-        else:
-            # else update existing value
-            if weighted_avg:
-                self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
-                self.iters[name] += 1
-            else:
-                self.avg_values[name] = self.avg_values[name] * \
-                    self.iters[name] + value
-                self.iters[name] += 1
-                self.avg_values[name] /= self.iters[name]
-
-    def add_values(self, name_dict):
-        for key, value in name_dict.items():
-            self.add_value(key, init_val=value)
-
-    def update_values(self, value_dict):
-        for key, value in value_dict.items():
-            self.update_value(key, value)
-
-
-def _check_argument(name, c, enum_list=None, max_val=None, min_val=None, restricted=False, val_type=None, alternative=None):
-    if alternative in c.keys() and c[alternative] is not None:
-        return
-    if restricted:
-        assert name in c.keys(), f' [!] {name} not defined in config.json'
-    if name in c.keys():
-        if max_val:
-            assert c[name] <= max_val, f' [!] {name} is larger than max value {max_val}'
-        if min_val:
-            assert c[name] >= min_val, f' [!] {name} is smaller than min value {min_val}'
-        if enum_list:
-            assert c[name].lower() in enum_list, f' [!] {name} is not a valid value'
-        if val_type:
-            assert isinstance(c[name], val_type) or c[name] is None, f' [!] {name} has wrong type - {type(c[name])} vs {val_type}'
-
-
-def check_config(c):
-    _check_argument('model', c, enum_list=['tacotron', 'tacotron2'], restricted=True, val_type=str)
-    _check_argument('run_name', c, restricted=True, val_type=str)
-    _check_argument('run_description', c, val_type=str)
-
-    # AUDIO
-    _check_argument('audio', c, restricted=True, val_type=dict)
-
-    # audio processing parameters
-    _check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
-    _check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
-    _check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
-    _check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
-    _check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
-    _check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
-    _check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
-    _check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)
-    _check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
-    _check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
-
-    # vocabulary parameters
-    _check_argument('characters', c, restricted=False, val_type=dict)
-    _check_argument('pad', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
-    _check_argument('eos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
-    _check_argument('bos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
-    _check_argument('characters', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
-    _check_argument('phonemes', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
-    _check_argument('punctuations', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
-
-    # normalization parameters
-    _check_argument('signal_norm', c['audio'], restricted=True, val_type=bool)
-    _check_argument('symmetric_norm', c['audio'], restricted=True, val_type=bool)
-    _check_argument('max_norm', c['audio'], restricted=True, val_type=float, min_val=0.1, max_val=1000)
-    _check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
-    _check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
-    _check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
-    _check_argument('spec_gain', c['audio'], restricted=True, val_type=float, min_val=1, max_val=100)
-    _check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
-    _check_argument('trim_db', c['audio'], restricted=True, val_type=int)
-
-    # training parameters
-    _check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
-    _check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)
-    _check_argument('r', c, restricted=True, val_type=int, min_val=1)
-    _check_argument('gradual_training', c, restricted=False, val_type=list)
-    _check_argument('loss_masking', c, restricted=True, val_type=bool)
-    # _check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100)
-
-    # validation parameters
-    _check_argument('run_eval', c, restricted=True, val_type=bool)
-    _check_argument('test_delay_epochs', c, restricted=True, val_type=int, min_val=0)
-    _check_argument('test_sentences_file', c, restricted=False, val_type=str)
-
-    # optimizer
-    _check_argument('noam_schedule', c, restricted=False, val_type=bool)
-    _check_argument('grad_clip', c, restricted=True, val_type=float, min_val=0.0)
-    _check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
-    _check_argument('lr', c, restricted=True, val_type=float, min_val=0)
-    _check_argument('wd', c, restricted=True, val_type=float, min_val=0)
-    _check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
-    _check_argument('seq_len_norm', c, restricted=True, val_type=bool)
-
-    # tacotron prenet
-    _check_argument('memory_size', c, restricted=True, val_type=int, min_val=-1)
-    _check_argument('prenet_type', c, restricted=True, val_type=str, enum_list=['original', 'bn'])
-    _check_argument('prenet_dropout', c, restricted=True, val_type=bool)
-
-    # attention
-    _check_argument('attention_type', c, restricted=True, val_type=str, enum_list=['graves', 'original'])
-    _check_argument('attention_heads', c, restricted=True, val_type=int)
-    _check_argument('attention_norm', c, restricted=True, val_type=str, enum_list=['sigmoid', 'softmax'])
-    _check_argument('windowing', c, restricted=True, val_type=bool)
-    _check_argument('use_forward_attn', c, restricted=True, val_type=bool)
-    _check_argument('forward_attn_mask', c, restricted=True, val_type=bool)
-    _check_argument('transition_agent', c, restricted=True, val_type=bool)
-    _check_argument('transition_agent', c, restricted=True, val_type=bool)
-    _check_argument('location_attn', c, restricted=True, val_type=bool)
-    _check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
-    _check_argument('double_decoder_consistency', c, restricted=True, val_type=bool)
-    _check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int)
-
-    # stopnet
-    _check_argument('stopnet', c, restricted=True, val_type=bool)
-    _check_argument('separate_stopnet', c, restricted=True, val_type=bool)
-
-    # tensorboard
-    _check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
-    _check_argument('tb_plot_step', c, restricted=True, val_type=int, min_val=1)
-    _check_argument('save_step', c, restricted=True, val_type=int, min_val=1)
-    _check_argument('checkpoint', c, restricted=True, val_type=bool)
-    _check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
-
-    # dataloading
-    # pylint: disable=import-outside-toplevel
-    from TTS.utils.text import cleaners
-    _check_argument('text_cleaner', c, restricted=True, val_type=str, enum_list=dir(cleaners))
-    _check_argument('enable_eos_bos_chars', c, restricted=True, val_type=bool)
-    _check_argument('num_loader_workers', c, restricted=True, val_type=int, min_val=0)
-    _check_argument('num_val_loader_workers', c, restricted=True, val_type=int, min_val=0)
-    _check_argument('batch_group_size', c, restricted=True, val_type=int, min_val=0)
-    _check_argument('min_seq_len', c, restricted=True, val_type=int, min_val=0)
-    _check_argument('max_seq_len', c, restricted=True, val_type=int, min_val=10)
-
-    # paths
-    _check_argument('output_path', c, restricted=True, val_type=str)
-
-    # multi-speaker gst
-    _check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
-    _check_argument('style_wav_for_test', c, restricted=True, val_type=str)
-    _check_argument('use_gst', c, restricted=True, val_type=bool)
-
-    # datasets - checking only the first entry
-    _check_argument('datasets', c, restricted=True, val_type=list)
-    for dataset_entry in c['datasets']:
-        _check_argument('name', dataset_entry, restricted=True, val_type=str)
-        _check_argument('path', dataset_entry, restricted=True, val_type=str)
-        _check_argument('meta_file_train', dataset_entry, restricted=True, val_type=str)
-        _check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)
diff --git a/utils/io.py b/utils/io.py
deleted file mode 100644
index faf00195..00000000
--- a/utils/io.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-import json
-import re
-import torch
-import datetime
-
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
-def load_config(config_path):
-    config = AttrDict()
-    with open(config_path, "r") as f:
-        input_str = f.read()
-    input_str = re.sub(r'\\\n', '', input_str)
-    input_str = re.sub(r'//.*\n', '\n', input_str)
-    data = json.loads(input_str)
-    config.update(data)
-    return config
-
-
-def copy_config_file(config_file, out_path, new_fields):
-    config_lines = open(config_file, "r").readlines()
-    # add extra information fields
-    for key, value in new_fields.items():
-        if isinstance(value, str):
-            new_line = '"{}":"{}",\n'.format(key, value)
-        else:
-            new_line = '"{}":{},\n'.format(key, value)
-        config_lines.insert(1, new_line)
-    config_out_file = open(out_path, "w")
-    config_out_file.writelines(config_lines)
-    config_out_file.close()
-
-
-def load_checkpoint(model, checkpoint_path, use_cuda=False):
-    state = torch.load(checkpoint_path, map_location=torch.device('cpu'))
-    model.load_state_dict(state['model'])
-    if use_cuda:
-        model.cuda()
-    # set model stepsize
-    if 'r' in state.keys():
-        model.decoder.set_r(state['r'])
-    return model, state
-
-
-def save_model(model, optimizer, current_step, epoch, r, output_path, **kwargs):
-    new_state_dict = model.state_dict()
-    state = {
-        'model': new_state_dict,
-        'optimizer': optimizer.state_dict() if optimizer is not None else None,
-        'step': current_step,
-        'epoch': epoch,
-        'date': datetime.date.today().strftime("%B %d, %Y"),
-        'r': r
-    }
-    state.update(kwargs)
-    torch.save(state, output_path)
-
-
-def save_checkpoint(model, optimizer, current_step, epoch, r, output_folder, **kwargs):
-    file_name = 'checkpoint_{}.pth.tar'.format(current_step)
-    checkpoint_path = os.path.join(output_folder, file_name)
-    print(" > CHECKPOINT : {}".format(checkpoint_path))
-    save_model(model, optimizer, current_step, epoch, r, checkpoint_path, **kwargs)
-
-
-def save_best_model(target_loss, best_loss, model, optimizer, current_step, epoch, r, output_folder, **kwargs):
-    if target_loss < best_loss:
-        file_name = 'best_model.pth.tar'
-        checkpoint_path = os.path.join(output_folder, file_name)
-        print(" > BEST MODEL : {}".format(checkpoint_path))
-        save_model(model, optimizer, current_step, epoch, r, checkpoint_path, model_loss=target_loss, **kwargs)
-        best_loss = target_loss
-    return best_loss
diff --git a/utils/measures.py b/utils/measures.py
deleted file mode 100644
index 01d25695..00000000
--- a/utils/measures.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import torch
-
-
-def alignment_diagonal_score(alignments, binary=False):
-    """
-    Compute how diagonal alignment predictions are. It is useful
-    to measure the alignment consistency of a model
-    Args:
-        alignments (torch.Tensor): batch of alignments.
-        binary (bool): if True, ignore scores and consider attention
-        as a binary mask.
-    Shape:
-        alignments : batch x decoder_steps x encoder_steps
-    """
-    maxs = alignments.max(dim=1)[0]
-    if binary:
-        maxs[maxs > 0] = 1
-    return maxs.mean(dim=1).mean(dim=0).item()
diff --git a/utils/radam.py b/utils/radam.py
deleted file mode 100644
index 4724b705..00000000
--- a/utils/radam.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# from https://github.com/LiyuanLucasLiu/RAdam
-
-import math
-import torch
-from torch.optim.optimizer import Optimizer, required
-
-
-class RAdam(Optimizer):
-
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
-        if lr < 0.0:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if eps < 0.0:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-
-        self.degenerated_to_sgd = degenerated_to_sgd
-        if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
-            for param in params:
-                if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
-                    param['buffer'] = [[None, None, None] for _ in range(10)]
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, buffer=[[None, None, None] for _ in range(10)])
-        super(RAdam, self).__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super(RAdam, self).__setstate__(state)
-
-    def step(self, closure=None):
-
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data.float()
-                if grad.is_sparse:
-                    raise RuntimeError('RAdam does not support sparse gradients')
-
-                p_data_fp32 = p.data.float()
-
-                state = self.state[p]
-
-                if len(state) == 0:
-                    state['step'] = 0
-                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
-                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
-                else:
-                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
-                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
-
-                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-                beta1, beta2 = group['betas']
-
-                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
-                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
-
-                state['step'] += 1
-                buffered = group['buffer'][int(state['step'] % 10)]
-                if state['step'] == buffered[0]:
-                    N_sma, step_size = buffered[1], buffered[2]
-                else:
-                    buffered[0] = state['step']
-                    beta2_t = beta2 ** state['step']
-                    N_sma_max = 2 / (1 - beta2) - 1
-                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
-                    buffered[1] = N_sma
-
-                    # more conservative since it's an approximated value
-                    if N_sma >= 5:
-                        step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
-                    elif self.degenerated_to_sgd:
-                        step_size = 1.0 / (1 - beta1 ** state['step'])
-                    else:
-                        step_size = -1
-                    buffered[2] = step_size
-
-                # more conservative since it's an approximated value
-                if N_sma >= 5:
-                    if group['weight_decay'] != 0:
-                        p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr'])
-                    denom = exp_avg_sq.sqrt().add_(group['eps'])
-                    p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
-                    p.data.copy_(p_data_fp32)
-                elif step_size > 0:
-                    if group['weight_decay'] != 0:
-                        p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr'])
-                    p_data_fp32.add_(exp_avg, alpha=-step_size * group['lr'])
-                    p.data.copy_(p_data_fp32)
-
-        return loss
diff --git a/utils/speakers.py b/utils/speakers.py
deleted file mode 100644
index 8aa612a8..00000000
--- a/utils/speakers.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import os
-import json
-
-from TTS.datasets.preprocess import get_preprocessor_by_name
-
-
-def make_speakers_json_path(out_path):
-    """Returns conventional speakers.json location."""
-    return os.path.join(out_path, "speakers.json")
-
-
-def load_speaker_mapping(out_path):
-    """Loads speaker mapping if already present."""
-    try:
-        with open(make_speakers_json_path(out_path)) as f:
-            return json.load(f)
-    except FileNotFoundError:
-        return {}
-
-
-def save_speaker_mapping(out_path, speaker_mapping):
-    """Saves speaker mapping if not yet present."""
-    speakers_json_path = make_speakers_json_path(out_path)
-    with open(speakers_json_path, "w") as f:
-        json.dump(speaker_mapping, f, indent=4)
-
-
-def get_speakers(items):
-    """Returns a sorted, unique list of speakers in a given dataset."""
-    speakers = {e[2] for e in items}
-    return sorted(speakers)
diff --git a/utils/synthesis.py b/utils/synthesis.py
deleted file mode 100644
index ce76b0ec..00000000
--- a/utils/synthesis.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import pkg_resources
-installed = {pkg.key for pkg in pkg_resources.working_set}  #pylint: disable=not-an-iterable
-if 'tensorflow' in installed or 'tensorflow-gpu' in installed:
-    import tensorflow as tf
-import torch
-import numpy as np
-from .text import text_to_sequence, phoneme_to_sequence
-
-
-def text_to_seqvec(text, CONFIG):
-    text_cleaner = [CONFIG.text_cleaner]
-    # text ot phonemes to sequence vector
-    if CONFIG.use_phonemes:
-        seq = np.asarray(
-            phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language,
-                                CONFIG.enable_eos_bos_chars,
-                                tp=CONFIG.characters if 'characters' in CONFIG.keys() else None),
-            dtype=np.int32)
-    else:
-        seq = np.asarray(text_to_sequence(text, text_cleaner, tp=CONFIG.characters if 'characters' in CONFIG.keys() else None), dtype=np.int32)
-    return seq
-
-
-def numpy_to_torch(np_array, dtype, cuda=False):
-    if np_array is None:
-        return None
-    tensor = torch.as_tensor(np_array, dtype=dtype)
-    if cuda:
-        return tensor.cuda()
-    return tensor
-
-
-def numpy_to_tf(np_array, dtype):
-    if np_array is None:
-        return None
-    tensor = tf.convert_to_tensor(np_array, dtype=dtype)
-    return tensor
-
-
-def compute_style_mel(style_wav, ap):
-    style_mel = ap.melspectrogram(
-        ap.load_wav(style_wav)).expand_dims(0)
-    return style_mel
-
-
-def run_model_torch(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
-    if CONFIG.use_gst:
-        decoder_output, postnet_output, alignments, stop_tokens = model.inference(
-            inputs, style_mel=style_mel, speaker_ids=speaker_id)
-    else:
-        if truncated:
-            decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
-                inputs, speaker_ids=speaker_id)
-        else:
-            decoder_output, postnet_output, alignments, stop_tokens = model.inference(
-                inputs, speaker_ids=speaker_id)
-    return decoder_output, postnet_output, alignments, stop_tokens
-
-
-def run_model_tf(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
-    if CONFIG.use_gst and style_mel is not None:
-        raise NotImplementedError(' [!] GST inference not implemented for TF')
-    if truncated:
-        raise NotImplementedError(' [!] Truncated inference not implemented for TF')
-    if speaker_id is not None:
-        raise NotImplementedError(' [!] Multi-Speaker not implemented for TF')
-    # TODO: handle multispeaker case
-    decoder_output, postnet_output, alignments, stop_tokens = model(
-        inputs, training=False)
-    return decoder_output, postnet_output, alignments, stop_tokens
-
-
-def run_model_tflite(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
-    if CONFIG.use_gst and style_mel is not None:
-        raise NotImplementedError(' [!] GST inference not implemented for TfLite')
-    if truncated:
-        raise NotImplementedError(' [!] Truncated inference not implemented for TfLite')
-    if speaker_id is not None:
-        raise NotImplementedError(' [!] Multi-Speaker not implemented for TfLite')
-    # get input and output details
-    input_details = model.get_input_details()
-    output_details = model.get_output_details()
-    # reshape input tensor for the new input shape
-    model.resize_tensor_input(input_details[0]['index'], inputs.shape)
-    model.allocate_tensors()
-    detail = input_details[0]
-    # input_shape = detail['shape']
-    model.set_tensor(detail['index'], inputs)
-    # run the model
-    model.invoke()
-    # collect outputs
-    decoder_output = model.get_tensor(output_details[0]['index'])
-    postnet_output = model.get_tensor(output_details[1]['index'])
-    # tflite model only returns feature frames
-    return decoder_output, postnet_output, None, None
-
-
-def parse_outputs_torch(postnet_output, decoder_output, alignments, stop_tokens):
-    postnet_output = postnet_output[0].data.cpu().numpy()
-    decoder_output = decoder_output[0].data.cpu().numpy()
-    alignment = alignments[0].cpu().data.numpy()
-    stop_tokens = stop_tokens[0].cpu().numpy()
-    return postnet_output, decoder_output, alignment, stop_tokens
-
-
-def parse_outputs_tf(postnet_output, decoder_output, alignments, stop_tokens):
-    postnet_output = postnet_output[0].numpy()
-    decoder_output = decoder_output[0].numpy()
-    alignment = alignments[0].numpy()
-    stop_tokens = stop_tokens[0].numpy()
-    return postnet_output, decoder_output, alignment, stop_tokens
-
-
-def parse_outputs_tflite(postnet_output, decoder_output):
-    postnet_output = postnet_output[0]
-    decoder_output = decoder_output[0]
-    return postnet_output, decoder_output
-
-
-def trim_silence(wav, ap):
-    return wav[:ap.find_endpoint(wav)]
-
-
-def inv_spectrogram(postnet_output, ap, CONFIG):
-    if CONFIG.model.lower() in ["tacotron"]:
-        wav = ap.inv_spectrogram(postnet_output.T)
-    else:
-        wav = ap.inv_melspectrogram(postnet_output.T)
-    return wav
-
-
-def id_to_torch(speaker_id):
-    if speaker_id is not None:
-        speaker_id = np.asarray(speaker_id)
-        speaker_id = torch.from_numpy(speaker_id).unsqueeze(0)
-    return speaker_id
-
-
-# TODO: perform GL with pytorch for batching
-def apply_griffin_lim(inputs, input_lens, CONFIG, ap):
-    '''Apply griffin-lim to each sample iterating throught the first dimension.
-    Args:
-        inputs (Tensor or np.Array): Features to be converted by GL. First dimension is the batch size.
-        input_lens (Tensor or np.Array): 1D array of sample lengths.
-        CONFIG (Dict): TTS config.
-        ap (AudioProcessor): TTS audio processor.
-    '''
-    wavs = []
-    for idx, spec in enumerate(inputs):
-        wav_len = (input_lens[idx] * ap.hop_length) - ap.hop_length  # inverse librosa padding
-        wav = inv_spectrogram(spec, ap, CONFIG)
-        # assert len(wav) == wav_len, f" [!] wav lenght: {len(wav)} vs expected: {wav_len}"
-        wavs.append(wav[:wav_len])
-    return wavs
-
-
-def synthesis(model,
-              text,
-              CONFIG,
-              use_cuda,
-              ap,
-              speaker_id=None,
-              style_wav=None,
-              truncated=False,
-              enable_eos_bos_chars=False, #pylint: disable=unused-argument
-              use_griffin_lim=False,
-              do_trim_silence=False,
-              backend='torch'):
-    """Synthesize voice for the given text.
-
-        Args:
-            model (TTS.models): model to synthesize.
-            text (str): target text
-            CONFIG (dict): config dictionary to be loaded from config.json.
-            use_cuda (bool): enable cuda.
-            ap (TTS.utils.audio.AudioProcessor): audio processor to process
-                model outputs.
-            speaker_id (int): id of speaker
-            style_wav (str): Uses for style embedding of GST.
-            truncated (bool): keep model states after inference. It can be used
-                for continuous inference at long texts.
-            enable_eos_bos_chars (bool): enable special chars for end of sentence and start of sentence.
-            do_trim_silence (bool): trim silence after synthesis.
-            backend (str): tf or torch
-    """
-    # GST processing
-    style_mel = None
-    if CONFIG.model == "TacotronGST" and style_wav is not None:
-        style_mel = compute_style_mel(style_wav, ap)
-    # preprocess the given text
-    inputs = text_to_seqvec(text, CONFIG)
-    # pass tensors to backend
-    if backend == 'torch':
-        speaker_id = id_to_torch(speaker_id)
-        style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
-        inputs = numpy_to_torch(inputs, torch.long, cuda=use_cuda)
-        inputs = inputs.unsqueeze(0)
-    elif backend == 'tf':
-        # TODO: handle speaker id for tf model
-        style_mel = numpy_to_tf(style_mel, tf.float32)
-        inputs = numpy_to_tf(inputs, tf.int32)
-        inputs = tf.expand_dims(inputs, 0)
-    elif backend == 'tflite':
-        style_mel = numpy_to_tf(style_mel, tf.float32)
-        inputs = numpy_to_tf(inputs, tf.int32)
-        inputs = tf.expand_dims(inputs, 0)
-    # synthesize voice
-    if backend == 'torch':
-        decoder_output, postnet_output, alignments, stop_tokens = run_model_torch(
-            model, inputs, CONFIG, truncated, speaker_id, style_mel)
-        postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_torch(
-            postnet_output, decoder_output, alignments, stop_tokens)
-    elif backend == 'tf':
-        decoder_output, postnet_output, alignments, stop_tokens = run_model_tf(
-            model, inputs, CONFIG, truncated, speaker_id, style_mel)
-        postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_tf(
-            postnet_output, decoder_output, alignments, stop_tokens)
-    elif backend == 'tflite':
-        decoder_output, postnet_output, alignment, stop_tokens = run_model_tflite(
-            model, inputs, CONFIG, truncated, speaker_id, style_mel)
-        postnet_output, decoder_output = parse_outputs_tflite(
-            postnet_output, decoder_output)
-    # convert outputs to numpy
-    # plot results
-    wav = None
-    if use_griffin_lim:
-        wav = inv_spectrogram(postnet_output, ap, CONFIG)
-        # trim silence
-        if do_trim_silence:
-            wav = trim_silence(wav, ap)
-    return wav, alignment, decoder_output, postnet_output, stop_tokens, inputs
diff --git a/utils/tensorboard_logger.py b/utils/tensorboard_logger.py
deleted file mode 100644
index cbf68ad6..00000000
--- a/utils/tensorboard_logger.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import traceback
-from tensorboardX import SummaryWriter
-
-
-class TensorboardLogger(object):
-    def __init__(self, log_dir, model_name):
-        self.model_name = model_name
-        self.writer = SummaryWriter(log_dir)
-        self.train_stats = {}
-        self.eval_stats = {}
-
-    def tb_model_weights(self, model, step):
-        layer_num = 1
-        for name, param in model.named_parameters():
-            if param.numel() == 1:
-                self.writer.add_scalar(
-                    "layer{}-{}/value".format(layer_num, name),
-                    param.max(), step)
-            else:
-                self.writer.add_scalar(
-                    "layer{}-{}/max".format(layer_num, name),
-                    param.max(), step)
-                self.writer.add_scalar(
-                    "layer{}-{}/min".format(layer_num, name),
-                    param.min(), step)
-                self.writer.add_scalar(
-                    "layer{}-{}/mean".format(layer_num, name),
-                    param.mean(), step)
-                self.writer.add_scalar(
-                    "layer{}-{}/std".format(layer_num, name),
-                    param.std(), step)
-                self.writer.add_histogram(
-                    "layer{}-{}/param".format(layer_num, name), param, step)
-                self.writer.add_histogram(
-                    "layer{}-{}/grad".format(layer_num, name), param.grad, step)
-            layer_num += 1
-
-    def dict_to_tb_scalar(self, scope_name, stats, step):
-        for key, value in stats.items():
-            self.writer.add_scalar('{}/{}'.format(scope_name, key), value, step)
-
-    def dict_to_tb_figure(self, scope_name, figures, step):
-        for key, value in figures.items():
-            self.writer.add_figure('{}/{}'.format(scope_name, key), value, step)
-
-    def dict_to_tb_audios(self, scope_name, audios, step, sample_rate):
-        for key, value in audios.items():
-            try:
-                self.writer.add_audio('{}/{}'.format(scope_name, key), value, step, sample_rate=sample_rate)
-            except:
-                traceback.print_exc()
-
-    def tb_train_iter_stats(self, step, stats):
-        self.dict_to_tb_scalar(f"{self.model_name}_TrainIterStats", stats, step)
-
-    def tb_train_epoch_stats(self, step, stats):
-        self.dict_to_tb_scalar(f"{self.model_name}_TrainEpochStats", stats, step)
-
-    def tb_train_figures(self, step, figures):
-        self.dict_to_tb_figure(f"{self.model_name}_TrainFigures", figures, step)
-
-    def tb_train_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios(f"{self.model_name}_TrainAudios", audios, step, sample_rate)
-
-    def tb_eval_stats(self, step, stats):
-        self.dict_to_tb_scalar(f"{self.model_name}_EvalStats", stats, step)
-
-    def tb_eval_figures(self, step, figures):
-        self.dict_to_tb_figure(f"{self.model_name}_EvalFigures", figures, step)
-
-    def tb_eval_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios(f"{self.model_name}_EvalAudios", audios, step, sample_rate)
-
-    def tb_test_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios(f"{self.model_name}_TestAudios", audios, step, sample_rate)
-
-    def tb_test_figures(self, step, figures):
-        self.dict_to_tb_figure(f"{self.model_name}_TestFigures", figures, step)
-
-    def tb_add_text(self, title, text, step):
-        self.writer.add_text(title, text, step)
diff --git a/utils/text/__init__.py b/utils/text/__init__.py
deleted file mode 100644
index 41aa6778..00000000
--- a/utils/text/__init__.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import re
-from packaging import version
-import phonemizer
-from phonemizer.phonemize import phonemize
-from TTS.utils.text import cleaners
-from TTS.utils.text.symbols import make_symbols, symbols, phonemes, _phoneme_punctuations, _bos, \
-    _eos
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-_phonemes_to_id = {s: i for i, s in enumerate(phonemes)}
-_id_to_phonemes = {i: s for i, s in enumerate(phonemes)}
-
-# Regular expression matching text enclosed in curly braces:
-_CURLY_RE = re.compile(r'(.*?)\{(.+?)\}(.*)')
-
-# Regular expression matching punctuations, ignoring empty space
-PHONEME_PUNCTUATION_PATTERN = r'['+_phoneme_punctuations+']+'
-
-
-def text2phone(text, language):
-    '''
-    Convert graphemes to phonemes.
-    '''
-    seperator = phonemizer.separator.Separator(' |', '', '|')
-    #try:
-    punctuations = re.findall(PHONEME_PUNCTUATION_PATTERN, text)
-    if version.parse(phonemizer.__version__) < version.parse('2.1'):
-        ph = phonemize(text, separator=seperator, strip=False, njobs=1, backend='espeak', language=language)
-        ph = ph[:-1].strip() # skip the last empty character
-        # phonemizer does not tackle punctuations. Here we do.
-        # Replace \n with matching punctuations.
-        if punctuations:
-            # if text ends with a punctuation.
-            if text[-1] == punctuations[-1]:
-                for punct in punctuations[:-1]:
-                    ph = ph.replace('| |\n', '|'+punct+'| |', 1)
-                    ph = ph + punctuations[-1]
-            else:
-                for punct in punctuations:
-                    ph = ph.replace('| |\n', '|'+punct+'| |', 1)
-    elif version.parse(phonemizer.__version__) >= version.parse('2.1'):
-        ph = phonemize(text, separator=seperator, strip=False, njobs=1, backend='espeak', language=language, preserve_punctuation=True)
-        # this is a simple fix for phonemizer.
-        # https://github.com/bootphon/phonemizer/issues/32
-        if punctuations:
-            for punctuation in punctuations:
-                ph = ph.replace(f"| |{punctuation} ", f"|{punctuation}| |").replace(f"| |{punctuation}", f"|{punctuation}| |")
-            ph = ph[:-3]
-    else:
-        raise RuntimeError(" [!] Use 'phonemizer' version 2.1 or older.")
-
-    return ph
-
-
-def pad_with_eos_bos(phoneme_sequence, tp=None):
-    # pylint: disable=global-statement
-    global _phonemes_to_id, _bos, _eos
-    if tp:
-        _bos = tp['bos']
-        _eos = tp['eos']
-        _, _phonemes = make_symbols(**tp)
-        _phonemes_to_id = {s: i for i, s in enumerate(_phonemes)}
-
-    return [_phonemes_to_id[_bos]] + list(phoneme_sequence) + [_phonemes_to_id[_eos]]
-
-
-def phoneme_to_sequence(text, cleaner_names, language, enable_eos_bos=False, tp=None):
-    # pylint: disable=global-statement
-    global _phonemes_to_id
-    if tp:
-        _, _phonemes = make_symbols(**tp)
-        _phonemes_to_id = {s: i for i, s in enumerate(_phonemes)}
-
-    sequence = []
-    clean_text = _clean_text(text, cleaner_names)
-    to_phonemes = text2phone(clean_text, language)
-    if to_phonemes is None:
-        print("!! After phoneme conversion the result is None. -- {} ".format(clean_text))
-    # iterate by skipping empty strings - NOTE: might be useful to keep it to have a better intonation.
-    for phoneme in filter(None, to_phonemes.split('|')):
-        sequence += _phoneme_to_sequence(phoneme)
-    # Append EOS char
-    if enable_eos_bos:
-        sequence = pad_with_eos_bos(sequence, tp=tp)
-    return sequence
-
-
-def sequence_to_phoneme(sequence, tp=None):
-    # pylint: disable=global-statement
-    '''Converts a sequence of IDs back to a string'''
-    global _id_to_phonemes
-    result = ''
-    if tp:
-        _, _phonemes = make_symbols(**tp)
-        _id_to_phonemes = {i: s for i, s in enumerate(_phonemes)}
-
-    for symbol_id in sequence:
-        if symbol_id in _id_to_phonemes:
-            s = _id_to_phonemes[symbol_id]
-            result += s
-    return result.replace('}{', ' ')
-
-
-def text_to_sequence(text, cleaner_names, tp=None):
-    '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-
-      The text can optionally have ARPAbet sequences enclosed in curly braces embedded
-      in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
-
-      Args:
-        text: string to convert to a sequence
-        cleaner_names: names of the cleaner functions to run the text through
-
-      Returns:
-        List of integers corresponding to the symbols in the text
-    '''
-    # pylint: disable=global-statement
-    global _symbol_to_id
-    if tp:
-        _symbols, _ = make_symbols(**tp)
-        _symbol_to_id = {s: i for i, s in enumerate(_symbols)}
-
-    sequence = []
-    # Check for curly braces and treat their contents as ARPAbet:
-    while text:
-        m = _CURLY_RE.match(text)
-        if not m:
-            sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
-            break
-        sequence += _symbols_to_sequence(
-            _clean_text(m.group(1), cleaner_names))
-        sequence += _arpabet_to_sequence(m.group(2))
-        text = m.group(3)
-    return sequence
-
-
-def sequence_to_text(sequence, tp=None):
-    '''Converts a sequence of IDs back to a string'''
-    # pylint: disable=global-statement
-    global _id_to_symbol
-    if tp:
-        _symbols, _ = make_symbols(**tp)
-        _id_to_symbol = {i: s for i, s in enumerate(_symbols)}
-
-    result = ''
-    for symbol_id in sequence:
-        if symbol_id in _id_to_symbol:
-            s = _id_to_symbol[symbol_id]
-            # Enclose ARPAbet back in curly braces:
-            if len(s) > 1 and s[0] == '@':
-                s = '{%s}' % s[1:]
-            result += s
-    return result.replace('}{', ' ')
-
-
-def _clean_text(text, cleaner_names):
-    for name in cleaner_names:
-        cleaner = getattr(cleaners, name)
-        if not cleaner:
-            raise Exception('Unknown cleaner: %s' % name)
-        text = cleaner(text)
-    return text
-
-
-def _symbols_to_sequence(syms):
-    return [_symbol_to_id[s] for s in syms if _should_keep_symbol(s)]
-
-
-def _phoneme_to_sequence(phons):
-    return [_phonemes_to_id[s] for s in list(phons) if _should_keep_phoneme(s)]
-
-
-def _arpabet_to_sequence(text):
-    return _symbols_to_sequence(['@' + s for s in text.split()])
-
-
-def _should_keep_symbol(s):
-    return s in _symbol_to_id and s not in ['~', '^', '_']
-
-
-def _should_keep_phoneme(p):
-    return p in _phonemes_to_id and p not in ['~', '^', '_']
diff --git a/utils/text/cleaners.py b/utils/text/cleaners.py
deleted file mode 100644
index f0a66f57..00000000
--- a/utils/text/cleaners.py
+++ /dev/null
@@ -1,123 +0,0 @@
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-from .number_norm import normalize_numbers
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1])
-                  for x in [
-                      ('mrs', 'misess'),
-                      ('mr', 'mister'),
-                      ('dr', 'doctor'),
-                      ('st', 'saint'),
-                      ('co', 'company'),
-                      ('jr', 'junior'),
-                      ('maj', 'major'),
-                      ('gen', 'general'),
-                      ('drs', 'doctors'),
-                      ('rev', 'reverend'),
-                      ('lt', 'lieutenant'),
-                      ('hon', 'honorable'),
-                      ('sgt', 'sergeant'),
-                      ('capt', 'captain'),
-                      ('esq', 'esquire'),
-                      ('ltd', 'limited'),
-                      ('col', 'colonel'),
-                      ('ft', 'fort'),
-                  ]]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def expand_numbers(text):
-    return normalize_numbers(text)
-
-
-def lowercase(text):
-    return text.lower()
-
-
-def collapse_whitespace(text):
-    return re.sub(_whitespace_re, ' ', text).strip()
-
-
-def convert_to_ascii(text):
-    return unidecode(text)
-
-
-def remove_aux_symbols(text):
-    text = re.sub(r'[\<\>\(\)\[\]\"]+', '', text)
-    return text
-
-
-def replace_symbols(text):
-    text = text.replace(';', ',')
-    text = text.replace('-', ' ')
-    text = text.replace(':', ',')
-    text = text.replace('&', 'and')
-    return text
-
-
-def basic_cleaners(text):
-    '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def transliteration_cleaners(text):
-    '''Pipeline for non-English text that transliterates to ASCII.'''
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-# TODO: elaborate it
-def basic_turkish_cleaners(text):
-    '''Pipeline for Turkish text'''
-    text = text.replace("I", "ı")
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def english_cleaners(text):
-    '''Pipeline for English text, including number and abbreviation expansion.'''
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = expand_numbers(text)
-    text = expand_abbreviations(text)
-    text = replace_symbols(text)
-    text = remove_aux_symbols(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def phoneme_cleaners(text):
-    '''Pipeline for phonemes mode, including number and abbreviation expansion.'''
-    text = convert_to_ascii(text)
-    text = expand_numbers(text)
-    text = expand_abbreviations(text)
-    text = replace_symbols(text)
-    text = remove_aux_symbols(text)
-    text = collapse_whitespace(text)
-    return text
diff --git a/utils/text/cmudict.py b/utils/text/cmudict.py
deleted file mode 100644
index c0f23406..00000000
--- a/utils/text/cmudict.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import re
-
-VALID_SYMBOLS = [
-    'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1',
-    'AH2', 'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0',
-    'AY1', 'AY2', 'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0',
-    'ER1', 'ER2', 'EY', 'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0',
-    'IH1', 'IH2', 'IY', 'IY0', 'IY1', 'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG',
-    'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0', 'OY1', 'OY2', 'P', 'R', 'S', 'SH',
-    'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW', 'UW0', 'UW1', 'UW2', 'V', 'W',
-    'Y', 'Z', 'ZH'
-]
-
-
-class CMUDict:
-    '''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict'''
-
-    def __init__(self, file_or_path, keep_ambiguous=True):
-        if isinstance(file_or_path, str):
-            with open(file_or_path, encoding='latin-1') as f:
-                entries = _parse_cmudict(f)
-        else:
-            entries = _parse_cmudict(file_or_path)
-        if not keep_ambiguous:
-            entries = {
-                word: pron
-                for word, pron in entries.items() if len(pron) == 1
-            }
-        self._entries = entries
-
-    def __len__(self):
-        return len(self._entries)
-
-    def lookup(self, word):
-        '''Returns list of ARPAbet pronunciations of the given word.'''
-        return self._entries.get(word.upper())
-
-    @staticmethod
-    def get_arpabet(word, cmudict, punctuation_symbols):
-        first_symbol, last_symbol = '', ''
-        if word and word[0] in punctuation_symbols:
-            first_symbol = word[0]
-            word = word[1:]
-        if word and word[-1] in punctuation_symbols:
-            last_symbol = word[-1]
-            word = word[:-1]
-        arpabet = cmudict.lookup(word)
-        if arpabet is not None:
-            return first_symbol + '{%s}' % arpabet[0] + last_symbol
-        return first_symbol + word + last_symbol
-
-
-_alt_re = re.compile(r'\([0-9]+\)')
-
-
-def _parse_cmudict(file):
-    cmudict = {}
-    for line in file:
-        if line and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"):
-            parts = line.split('  ')
-            word = re.sub(_alt_re, '', parts[0])
-            pronunciation = _get_pronunciation(parts[1])
-            if pronunciation:
-                if word in cmudict:
-                    cmudict[word].append(pronunciation)
-                else:
-                    cmudict[word] = [pronunciation]
-    return cmudict
-
-
-def _get_pronunciation(s):
-    parts = s.strip().split(' ')
-    for part in parts:
-        if part not in VALID_SYMBOLS:
-            return None
-    return ' '.join(parts)
diff --git a/utils/text/number_norm.py b/utils/text/number_norm.py
deleted file mode 100644
index 7b539bff..00000000
--- a/utils/text/number_norm.py
+++ /dev/null
@@ -1,71 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-import inflect
-import re
-
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-
-def _remove_commas(m):
-    return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split('.')
-    if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
-    elif dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
-    elif cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
-    else:
-        return 'zero dollars'
-
-
-def _expand_ordinal(m):
-    return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-    num = int(m.group(0))
-    if 1000 < num < 3000:
-        if num == 2000:
-            return 'two thousand'
-        if 2000 < num < 2010:
-            return 'two thousand ' + _inflect.number_to_words(num % 100)
-        if num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + ' hundred'
-        return _inflect.number_to_words(num,
-                                        andword='',
-                                        zero='oh',
-                                        group=2).replace(', ', ' ')
-    return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r'\1 pounds', text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
diff --git a/utils/text/symbols.py b/utils/text/symbols.py
deleted file mode 100644
index 544277c5..00000000
--- a/utils/text/symbols.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# -*- coding: utf-8 -*-
-'''
-Defines the set of symbols used in text input to the model.
-
-The default is a set of ASCII characters that works well for English or text that has been run
-through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details.
-'''
-def make_symbols(characters, phonemes, punctuations='!\'(),-.:;? ', pad='_', eos='~', bos='^'):# pylint: disable=redefined-outer-name
-    ''' Function to create symbols and phonemes '''
-    _phonemes_sorted = sorted(list(phonemes))
-
-    # Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
-    _arpabet = ['@' + s for s in _phonemes_sorted]
-
-    # Export all symbols:
-    _symbols = [pad, eos, bos] + list(characters) + _arpabet
-    _phonemes = [pad, eos, bos] + list(_phonemes_sorted) + list(punctuations)
-
-    return _symbols, _phonemes
-
-_pad = '_'
-_eos = '~'
-_bos = '^'
-_characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!\'(),-.:;? '
-_punctuations = '!\'(),-.:;? '
-_phoneme_punctuations = '.!;:,?'
-
-# Phonemes definition
-_vowels = 'iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ'
-_non_pulmonic_consonants = 'ʘɓǀɗǃʄǂɠǁʛ'
-_pulmonic_consonants = 'pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ'
-_suprasegmentals = 'ˈˌːˑ'
-_other_symbols = 'ʍwɥʜʢʡɕʑɺɧ'
-_diacrilics = 'ɚ˞ɫ'
-_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics
-
-symbols, phonemes = make_symbols(_characters, _phonemes, _punctuations, _pad, _eos, _bos)
-
-# Generate ALIEN language
-# from random import shuffle
-# shuffle(phonemes)
-
-if __name__ == '__main__':
-    print(" > TTS symbols {}".format(len(symbols)))
-    print(symbols)
-    print(" > TTS phonemes {}".format(len(phonemes)))
-    print(phonemes)
diff --git a/utils/training.py b/utils/training.py
deleted file mode 100644
index 9046f9e0..00000000
--- a/utils/training.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import torch
-import numpy as np
-
-
-def setup_torch_training_env(cudnn_enable, cudnn_benchmark):
-    torch.backends.cudnn.enabled = cudnn_enable
-    torch.backends.cudnn.benchmark = cudnn_benchmark
-    torch.manual_seed(54321)
-    use_cuda = torch.cuda.is_available()
-    num_gpus = torch.cuda.device_count()
-    print(" > Using CUDA: ", use_cuda)
-    print(" > Number of GPUs: ", num_gpus)
-    return use_cuda, num_gpus
-
-
-def check_update(model, grad_clip, ignore_stopnet=False):
-    r'''Check model gradient against unexpected jumps and failures'''
-    skip_flag = False
-    if ignore_stopnet:
-        grad_norm = torch.nn.utils.clip_grad_norm_([param for name, param in model.named_parameters() if 'stopnet' not in name], grad_clip)
-    else:
-        grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
-    # compatibility with different torch versions
-    if isinstance(grad_norm, float):
-        if np.isinf(grad_norm):
-            print(" | > Gradient is INF !!")
-            skip_flag = True
-    else:
-        if torch.isinf(grad_norm):
-            print(" | > Gradient is INF !!")
-            skip_flag = True
-    return grad_norm, skip_flag
-
-
-def lr_decay(init_lr, global_step, warmup_steps):
-    r'''from https://github.com/r9y9/tacotron_pytorch/blob/master/train.py'''
-    warmup_steps = float(warmup_steps)
-    step = global_step + 1.
-    lr = init_lr * warmup_steps**0.5 * np.minimum(step * warmup_steps**-1.5,
-                                                  step**-0.5)
-    return lr
-
-
-def adam_weight_decay(optimizer):
-    """
-    Custom weight decay operation, not effecting grad values.
-    """
-    for group in optimizer.param_groups:
-        for param in group['params']:
-            current_lr = group['lr']
-            weight_decay = group['weight_decay']
-            factor = -weight_decay * group['lr']
-            param.data = param.data.add(param.data,
-                                        alpha=factor)
-    return optimizer, current_lr
-
-# pylint: disable=dangerous-default-value
-def set_weight_decay(model, weight_decay, skip_list={"decoder.attention.v", "rnn", "lstm", "gru", "embedding"}):
-    """
-    Skip biases, BatchNorm parameters, rnns.
-    and attention projection layer v
-    """
-    decay = []
-    no_decay = []
-    for name, param in model.named_parameters():
-        if not param.requires_grad:
-            continue
-
-        if len(param.shape) == 1 or any([skip_name in name for skip_name in skip_list]):
-            no_decay.append(param)
-        else:
-            decay.append(param)
-    return [{
-        'params': no_decay,
-        'weight_decay': 0.
-    }, {
-        'params': decay,
-        'weight_decay': weight_decay
-    }]
-
-
-# pylint: disable=protected-access
-class NoamLR(torch.optim.lr_scheduler._LRScheduler):
-    def __init__(self, optimizer, warmup_steps=0.1, last_epoch=-1):
-        self.warmup_steps = float(warmup_steps)
-        super(NoamLR, self).__init__(optimizer, last_epoch)
-
-    def get_lr(self):
-        step = max(self.last_epoch, 1)
-        return [
-            base_lr * self.warmup_steps**0.5 *
-            min(step * self.warmup_steps**-1.5, step**-0.5)
-            for base_lr in self.base_lrs
-        ]
-
-
-def gradual_training_scheduler(global_step, config):
-    """Setup the gradual training schedule wrt number
-    of active GPUs"""
-    num_gpus = torch.cuda.device_count()
-    if num_gpus == 0:
-        num_gpus = 1
-    new_values = None
-    # we set the scheduling wrt num_gpus
-    for values in config.gradual_training:
-        if global_step * num_gpus >= values[0]:
-            new_values = values
-    return new_values[1], new_values[2]
diff --git a/utils/visual.py b/utils/visual.py
deleted file mode 100644
index b4ebec9a..00000000
--- a/utils/visual.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-import librosa
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-from TTS.utils.text import phoneme_to_sequence, sequence_to_phoneme
-
-
-def plot_alignment(alignment, info=None, fig_size=(16, 10), title=None):
-    if isinstance(alignment, torch.Tensor):
-        alignment_ = alignment.detach().cpu().numpy().squeeze()
-    else:
-        alignment_ = alignment
-    fig, ax = plt.subplots(figsize=fig_size)
-    im = ax.imshow(
-        alignment_.T, aspect='auto', origin='lower', interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    # plt.yticks(range(len(text)), list(text))
-    plt.tight_layout()
-    if title is not None:
-        plt.title(title)
-    return fig
-
-
-def plot_spectrogram(spectrogram, ap=None, fig_size=(16, 10)):
-    if isinstance(spectrogram, torch.Tensor):
-        spectrogram_ = spectrogram.detach().cpu().numpy().squeeze().T
-    else:
-        spectrogram_ = spectrogram.T
-    if ap is not None:
-        spectrogram_ = ap._denormalize(spectrogram_)  # pylint: disable=protected-access
-    fig = plt.figure(figsize=fig_size)
-    plt.imshow(spectrogram_, aspect="auto", origin="lower")
-    plt.colorbar()
-    plt.tight_layout()
-    return fig
-
-
-def visualize(alignment, postnet_output, stop_tokens, text, hop_length, CONFIG, decoder_output=None, output_path=None, figsize=(8, 24)):
-    if decoder_output is not None:
-        num_plot = 4
-    else:
-        num_plot = 3
-
-    label_fontsize = 16
-    fig = plt.figure(figsize=figsize)
-
-    plt.subplot(num_plot, 1, 1)
-    plt.imshow(alignment.T, aspect="auto", origin="lower", interpolation=None)
-    plt.xlabel("Decoder timestamp", fontsize=label_fontsize)
-    plt.ylabel("Encoder timestamp", fontsize=label_fontsize)
-    # compute phoneme representation and back
-    if CONFIG.use_phonemes:
-        seq = phoneme_to_sequence(text, [CONFIG.text_cleaner], CONFIG.phoneme_language, CONFIG.enable_eos_bos_chars, tp=CONFIG.characters if 'characters' in CONFIG.keys() else None)
-        text = sequence_to_phoneme(seq, tp=CONFIG.characters if 'characters' in CONFIG.keys() else None)
-        print(text)
-    plt.yticks(range(len(text)), list(text))
-    plt.colorbar()
-    # plot stopnet predictions
-    plt.subplot(num_plot, 1, 2)
-    plt.plot(range(len(stop_tokens)), list(stop_tokens))
-    # plot postnet spectrogram
-    plt.subplot(num_plot, 1, 3)
-    librosa.display.specshow(postnet_output.T, sr=CONFIG.audio['sample_rate'],
-                             hop_length=hop_length, x_axis="time", y_axis="linear",
-                             fmin=CONFIG.audio['mel_fmin'],
-                             fmax=CONFIG.audio['mel_fmax'])
-
-    plt.xlabel("Time", fontsize=label_fontsize)
-    plt.ylabel("Hz", fontsize=label_fontsize)
-    plt.tight_layout()
-    plt.colorbar()
-
-    if decoder_output is not None:
-        plt.subplot(num_plot, 1, 4)
-        librosa.display.specshow(decoder_output.T, sr=CONFIG.audio['sample_rate'],
-                                 hop_length=hop_length, x_axis="time", y_axis="linear",
-                                 fmin=CONFIG.audio['mel_fmin'],
-                                 fmax=CONFIG.audio['mel_fmax'])
-        plt.xlabel("Time", fontsize=label_fontsize)
-        plt.ylabel("Hz", fontsize=label_fontsize)
-        plt.tight_layout()
-        plt.colorbar()
-
-    if output_path:
-        print(output_path)
-        fig.savefig(output_path)
-        plt.close()
diff --git a/vocoder/README.md b/vocoder/README.md
deleted file mode 100644
index e3baf1f9..00000000
--- a/vocoder/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Mozilla TTS Vocoders (Experimental)
-
-We provide here different vocoder implementations which can be combined with our TTS models to enable "FASTER THAN REAL-TIME" end-to-end TTS stack.
-
-Currently, there are implementations of the following models.
-
-- Melgan
-- MultiBand-Melgan
-- GAN-TTS (Discriminator Only)
-
-It is also very easy to adapt different vocoder models as we provide here a flexible and modular (but not too modular) framework.
-
-## Training a model
-
-You can see here an example (Soon)[Colab Notebook]() training MelGAN with LJSpeech dataset.
-
-In order to train a new model, you need to collecto all your wav files under a common parent folder and give this path to `data_path` field in '''config.json'''
-
-You need to define other relevant parameters in your ```config.json``` and then start traning with the following command from Mozilla TTS root path, where '0' is the Id of the GPU you wish to use.
-
-```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --config_path path/to/config.json```
-
-Exampled config files can be found under `vocoder/configs/` folder.
-
-You can continue a previous training by the following command.
-
-```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --continue_path path/to/your/model/folder```
-
-You can fine-tune a pre-trained model by the following command.
-
-```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --restore_path path/to/your/model.pth.tar```
-
-Restoring a model starts a new training in a different output folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same conditions the previous training run left off.
-
-You can also follow your training runs on Tensorboard as you do with our TTS models.
-
-## Acknowledgement
-Thanks to @kan-bayashi for his [repository](https://github.com/kan-bayashi/ParallelWaveGAN) being the start point of our work.
diff --git a/vocoder/__init__.py b/vocoder/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
deleted file mode 100644
index 0b751854..00000000
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ /dev/null
@@ -1,151 +0,0 @@
-{
-    "run_name": "multiband-melgan-rwd",
-    "run_description": "multiband melgan with random window discriminator from https://arxiv.org/pdf/1909.11646.pdf",
-
-    // AUDIO PARAMETERS
-    "audio":{
-        // stft parameters
-        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
-        "win_length": 1024,      // stft window length in ms.
-        "hop_length": 256,       // stft window hop-lengh in ms.
-        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
-        // Audio processing parameters
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-
-        // Silence trimming
-        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
-
-        // Griffin-Lim
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
-        // MelSpectrogram parameters
-        "num_mels": 80,         // size of the mel spec frame.
-        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
-
-        // Normalization parameters
-        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
-        "min_level_db": -100,   // lower bound for normalization
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
-    },
-
-    // DISTRIBUTED TRAINING
-    // "distributed":{
-    //     "backend": "nccl",
-    //     "url": "tcp:\/\/localhost:54321"
-    // },
-
-    // MODEL PARAMETERS
-    "use_pqmf": true,
-
-    // LOSS PARAMETERS
-    "use_stft_loss": true,
-    "use_subband_stft_loss": true,
-    "use_mse_gan_loss": true,
-    "use_hinge_gan_loss": false,
-    "use_feat_match_loss": false,  // use only with melgan discriminators
-
-    // loss weights
-    "stft_loss_weight": 0.5,
-    "subband_stft_loss_weight": 0.5,
-    "mse_G_loss_weight": 2.5,
-    "hinge_G_loss_weight": 2.5,
-    "feat_match_loss_weight": 25,
-
-    // multiscale stft loss parameters
-    "stft_loss_params": {
-        "n_ffts": [1024, 2048, 512],
-        "hop_lengths": [120, 240, 50],
-        "win_lengths": [600, 1200, 240]
-    },
-
-    // subband multiscale stft loss parameters
-    "subband_stft_loss_params":{
-        "n_ffts": [384, 683, 171],
-        "hop_lengths": [30, 60, 10],
-        "win_lengths": [150, 300, 60]
-    },
-
-    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
-
-    // DISCRIMINATOR
-    "discriminator_model": "random_window_discriminator",
-    "discriminator_model_params":{
-        "uncond_disc_donwsample_factors": [8, 4],
-        "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
-        "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
-        "window_sizes": [512, 1024, 2048, 4096, 8192]
-    },
-    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
-
-    // GENERATOR
-    "generator_model": "multiband_melgan_generator",
-    "generator_model_params": {
-        "upsample_factors":[8, 4, 2],
-        "num_res_blocks": 4
-    },
-
-    // DATASET
-    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
-    "seq_len": 16384,
-    "pad_short": 2000,
-    "conv_pad": 0,
-    "use_noise_augment": false,
-    "use_cache": true,
-
-    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
-    // TRAINING
-    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-
-    // VALIDATION
-    "run_eval": true,
-    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
-    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
-    // OPTIMIZER
-    "noam_schedule": false,        // use noam warmup and lr schedule.
-    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "warmup_steps_disc": 4000,
-    "epochs": 10000,                // total number of epochs to train.
-    "wd": 0.0,                // Weight decay weight.
-    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
-    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
-    "lr_scheduler_gen": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
-    "lr_scheduler_gen_params": {
-        "gamma": 0.5,
-        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
-    },
-    "lr_scheduler_disc": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
-    "lr_scheduler_disc_params": {
-        "gamma": 0.5,
-        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
-    },
-    "lr_gen": 1e-4,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_disc": 1e-4,
-
-    // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log traning on console.
-    "print_eval": false,     // If True, it prints loss values for each step in eval run.
-    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
-    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
-    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
-    // DATA LOADING
-    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
-    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
-    "eval_split_size": 10,
-
-    // PATHS
-    "output_path": "/home/erogol/Models/LJSpeech/"
-}
-
diff --git a/vocoder/datasets/__init__.py b/vocoder/datasets/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/vocoder/datasets/gan_dataset.py b/vocoder/datasets/gan_dataset.py
deleted file mode 100644
index af23fbf2..00000000
--- a/vocoder/datasets/gan_dataset.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import os
-import glob
-import torch
-import random
-import numpy as np
-from torch.utils.data import Dataset
-from multiprocessing import Manager
-
-
-class GANDataset(Dataset):
-    """
-    GAN Dataset searchs for all the wav files under root path
-    and converts them to acoustic features on the fly and returns
-    random segments of (audio, feature) couples.
-    """
-    def __init__(self,
-                 ap,
-                 items,
-                 seq_len,
-                 hop_len,
-                 pad_short,
-                 conv_pad=2,
-                 is_training=True,
-                 return_segments=True,
-                 use_noise_augment=False,
-                 use_cache=False,
-                 verbose=False):
-
-        self.ap = ap
-        self.item_list = items
-        self.compute_feat = not isinstance(items[0], (tuple, list))
-        self.seq_len = seq_len
-        self.hop_len = hop_len
-        self.pad_short = pad_short
-        self.conv_pad = conv_pad
-        self.is_training = is_training
-        self.return_segments = return_segments
-        self.use_cache = use_cache
-        self.use_noise_augment = use_noise_augment
-        self.verbose = verbose
-
-        assert seq_len % hop_len == 0, " [!] seq_len has to be a multiple of hop_len."
-        self.feat_frame_len = seq_len // hop_len + (2 * conv_pad)
-
-        # map G and D instances
-        self.G_to_D_mappings = list(range(len(self.item_list)))
-        self.shuffle_mapping()
-
-        # cache acoustic features
-        if use_cache:
-            self.create_feature_cache()
-
-    def create_feature_cache(self):
-        self.manager = Manager()
-        self.cache = self.manager.list()
-        self.cache += [None for _ in range(len(self.item_list))]
-
-    @staticmethod
-    def find_wav_files(path):
-        return glob.glob(os.path.join(path, '**', '*.wav'), recursive=True)
-
-    def __len__(self):
-        return len(self.item_list)
-
-    def __getitem__(self, idx):
-        """ Return different items for Generator and Discriminator and
-        cache acoustic features """
-        if self.return_segments:
-            idx2 = self.G_to_D_mappings[idx]
-            item1 = self.load_item(idx)
-            item2 = self.load_item(idx2)
-            return item1, item2
-        item1 = self.load_item(idx)
-        return item1
-
-    def shuffle_mapping(self):
-        random.shuffle(self.G_to_D_mappings)
-
-    def load_item(self, idx):
-        """ load (audio, feat) couple """
-        if self.compute_feat:
-            # compute features from wav
-            wavpath = self.item_list[idx]
-            # print(wavpath)
-
-            if self.use_cache and self.cache[idx] is not None:
-                audio, mel = self.cache[idx]
-            else:
-                audio = self.ap.load_wav(wavpath)
-
-                if len(audio) < self.seq_len + self.pad_short:
-                    audio = np.pad(audio, (0, self.seq_len + self.pad_short - len(audio)), \
-                            mode='constant', constant_values=0.0)
-
-                mel = self.ap.melspectrogram(audio)
-        else:
-
-            # load precomputed features
-            wavpath, feat_path = self.item_list[idx]
-
-            if self.use_cache and self.cache[idx] is not None:
-                audio, mel = self.cache[idx]
-            else:
-                audio = self.ap.load_wav(wavpath)
-                mel = np.load(feat_path)
-
-        # correct the audio length wrt padding applied in stft
-        audio = np.pad(audio, (0, self.hop_len), mode="edge")
-        audio = audio[:mel.shape[-1] * self.hop_len]
-        assert mel.shape[-1] * self.hop_len == audio.shape[-1], f' [!] {mel.shape[-1] * self.hop_len} vs {audio.shape[-1]}'
-
-        audio = torch.from_numpy(audio).float().unsqueeze(0)
-        mel = torch.from_numpy(mel).float().squeeze(0)
-
-        if self.return_segments:
-            max_mel_start = mel.shape[1] - self.feat_frame_len
-            mel_start = random.randint(0, max_mel_start)
-            mel_end = mel_start + self.feat_frame_len
-            mel = mel[:, mel_start:mel_end]
-
-            audio_start = mel_start * self.hop_len
-            audio = audio[:, audio_start:audio_start +
-                          self.seq_len]
-
-        if self.use_noise_augment and self.is_training and self.return_segments:
-            audio = audio + (1 / 32768) * torch.randn_like(audio)
-        return (mel, audio)
diff --git a/vocoder/datasets/preprocess.py b/vocoder/datasets/preprocess.py
deleted file mode 100644
index be60c13a..00000000
--- a/vocoder/datasets/preprocess.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import glob
-import os
-from pathlib import Path
-
-import numpy as np
-
-
-def find_wav_files(data_path):
-    wav_paths = glob.glob(os.path.join(data_path, '**', '*.wav'), recursive=True)
-    return wav_paths
-
-
-def find_feat_files(data_path):
-    feat_paths = glob.glob(os.path.join(data_path, '**', '*.npy'), recursive=True)
-    return feat_paths
-
-
-def load_wav_data(data_path, eval_split_size):
-    wav_paths = find_wav_files(data_path)
-    np.random.seed(0)
-    np.random.shuffle(wav_paths)
-    return wav_paths[:eval_split_size], wav_paths[eval_split_size:]
-
-
-def load_wav_feat_data(data_path, feat_path, eval_split_size):
-    wav_paths = sorted(find_wav_files(data_path))
-    feat_paths = sorted(find_feat_files(feat_path))
-    assert len(wav_paths) == len(feat_paths)
-    for wav, feat in zip(wav_paths, feat_paths):
-        wav_name = Path(wav).stem
-        feat_name = Path(feat).stem
-        assert wav_name == feat_name
-
-    items = list(zip(wav_paths, feat_paths))
-    np.random.seed(0)
-    np.random.shuffle(items)
-    return items[:eval_split_size], items[eval_split_size:]
diff --git a/vocoder/layers/__init__.py b/vocoder/layers/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
deleted file mode 100644
index 431f7f45..00000000
--- a/vocoder/layers/losses.py
+++ /dev/null
@@ -1,309 +0,0 @@
-import torch
-
-from torch import nn
-from torch.nn import functional as F
-
-
-class TorchSTFT():
-    def __init__(self, n_fft, hop_length, win_length, window='hann_window'):
-        """ Torch based STFT operation """
-        self.n_fft = n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.window = getattr(torch, window)(win_length)
-
-    def __call__(self, x):
-        # B x D x T x 2
-        o = torch.stft(x,
-                       self.n_fft,
-                       self.hop_length,
-                       self.win_length,
-                       self.window,
-                       center=True,
-                       pad_mode="reflect",  # compatible with audio.py
-                       normalized=False,
-                       onesided=True)
-        M = o[:, :, :, 0]
-        P = o[:, :, :, 1]
-        return torch.sqrt(torch.clamp(M ** 2 + P ** 2, min=1e-8))
-
-
-#################################
-# GENERATOR LOSSES
-#################################
-
-
-class STFTLoss(nn.Module):
-    """ Single scale  STFT Loss """
-    def __init__(self, n_fft, hop_length, win_length):
-        super(STFTLoss, self).__init__()
-        self.n_fft = n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.stft = TorchSTFT(n_fft, hop_length, win_length)
-
-    def forward(self, y_hat, y):
-        y_hat_M = self.stft(y_hat)
-        y_M = self.stft(y)
-        # magnitude loss
-        loss_mag = F.l1_loss(torch.log(y_M), torch.log(y_hat_M))
-        # spectral convergence loss
-        loss_sc = torch.norm(y_M - y_hat_M, p="fro") / torch.norm(y_M, p="fro")
-        return loss_mag, loss_sc
-
-class MultiScaleSTFTLoss(torch.nn.Module):
-    """ Multi scale STFT loss """
-    def __init__(self,
-                 n_ffts=(1024, 2048, 512),
-                 hop_lengths=(120, 240, 50),
-                 win_lengths=(600, 1200, 240)):
-        super(MultiScaleSTFTLoss, self).__init__()
-        self.loss_funcs = torch.nn.ModuleList()
-        for n_fft, hop_length, win_length in zip(n_ffts, hop_lengths, win_lengths):
-            self.loss_funcs.append(STFTLoss(n_fft, hop_length, win_length))
-
-    def forward(self, y_hat, y):
-        N = len(self.loss_funcs)
-        loss_sc = 0
-        loss_mag = 0
-        for f in self.loss_funcs:
-            lm, lsc = f(y_hat, y)
-            loss_mag += lm
-            loss_sc += lsc
-        loss_sc /= N
-        loss_mag /= N
-        return loss_mag, loss_sc
-
-
-class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
-    """ Multiscale STFT loss for multi band model outputs """
-    # pylint: disable=no-self-use
-    def forward(self, y_hat, y):
-        y_hat = y_hat.view(-1, 1, y_hat.shape[2])
-        y = y.view(-1, 1, y.shape[2])
-        return super().forward(y_hat.squeeze(1), y.squeeze(1))
-
-
-class MSEGLoss(nn.Module):
-    """ Mean Squared Generator Loss """
-    # pylint: disable=no-self-use
-    def forward(self, score_real):
-        loss_fake = F.mse_loss(score_real, score_real.new_ones(score_real.shape))
-        return loss_fake
-
-
-class HingeGLoss(nn.Module):
-    """ Hinge Discriminator Loss """
-    # pylint: disable=no-self-use
-    def forward(self, score_real):
-        # TODO: this might be wrong
-        loss_fake = torch.mean(F.relu(1. - score_real))
-        return loss_fake
-
-
-##################################
-# DISCRIMINATOR LOSSES
-##################################
-
-
-class MSEDLoss(nn.Module):
-    """ Mean Squared Discriminator Loss """
-    def __init__(self,):
-        super(MSEDLoss, self).__init__()
-        self.loss_func = nn.MSELoss()
-
-    # pylint: disable=no-self-use
-    def forward(self, score_fake, score_real):
-        loss_real = self.loss_func(score_real, score_real.new_ones(score_real.shape))
-        loss_fake = self.loss_func(score_fake, score_fake.new_zeros(score_fake.shape))
-        loss_d = loss_real + loss_fake
-        return loss_d, loss_real, loss_fake
-
-
-class HingeDLoss(nn.Module):
-    """ Hinge Discriminator Loss """
-    # pylint: disable=no-self-use
-    def forward(self, score_fake, score_real):
-        loss_real = torch.mean(F.relu(1. - score_real))
-        loss_fake = torch.mean(F.relu(1. + score_fake))
-        loss_d = loss_real + loss_fake
-        return loss_d, loss_real, loss_fake
-
-
-class MelganFeatureLoss(nn.Module):
-    def __init__(self,):
-        super(MelganFeatureLoss, self).__init__()
-        self.loss_func = nn.L1Loss()
-
-    # pylint: disable=no-self-use
-    def forward(self, fake_feats, real_feats):
-        loss_feats = 0
-        for fake_feat, real_feat in zip(fake_feats, real_feats):
-            loss_feats += self.loss_func(fake_feat, real_feat)
-        loss_feats /= len(fake_feats) + len(real_feats)
-        return loss_feats
-
-
-#####################################
-# LOSS WRAPPERS
-#####################################
-
-
-def _apply_G_adv_loss(scores_fake, loss_func):
-    """ Compute G adversarial loss function
-    and normalize values """
-    adv_loss = 0
-    if isinstance(scores_fake, list):
-        for score_fake in scores_fake:
-            fake_loss = loss_func(score_fake)
-            adv_loss += fake_loss
-        adv_loss /= len(scores_fake)
-    else:
-        fake_loss = loss_func(scores_fake)
-        adv_loss = fake_loss
-    return adv_loss
-
-
-def _apply_D_loss(scores_fake, scores_real, loss_func):
-    """ Compute D loss func and normalize loss values """
-    loss = 0
-    real_loss = 0
-    fake_loss = 0
-    if isinstance(scores_fake, list):
-        # multi-scale loss
-        for score_fake, score_real in zip(scores_fake, scores_real):
-            total_loss, real_loss, fake_loss = loss_func(score_fake=score_fake, score_real=score_real)
-            loss += total_loss
-            real_loss += real_loss
-            fake_loss += fake_loss
-        # normalize loss values with number of scales
-        loss /= len(scores_fake)
-        real_loss /= len(scores_real)
-        fake_loss /= len(scores_fake)
-    else:
-        # single scale loss
-        total_loss, real_loss, fake_loss = loss_func(scores_fake, scores_real)
-        loss = total_loss
-    return loss, real_loss, fake_loss
-
-
-##################################
-# MODEL LOSSES
-##################################
-
-
-class GeneratorLoss(nn.Module):
-    def __init__(self, C):
-        """ Compute Generator Loss values depending on training
-        configuration """
-        super(GeneratorLoss, self).__init__()
-        assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
-            " [!] Cannot use HingeGANLoss and MSEGANLoss together."
-
-        self.use_stft_loss = C.use_stft_loss
-        self.use_subband_stft_loss = C.use_subband_stft_loss
-        self.use_mse_gan_loss = C.use_mse_gan_loss
-        self.use_hinge_gan_loss = C.use_hinge_gan_loss
-        self.use_feat_match_loss = C.use_feat_match_loss
-
-        self.stft_loss_weight = C.stft_loss_weight
-        self.subband_stft_loss_weight = C.subband_stft_loss_weight
-        self.mse_gan_loss_weight = C.mse_G_loss_weight
-        self.hinge_gan_loss_weight = C.hinge_G_loss_weight
-        self.feat_match_loss_weight = C.feat_match_loss_weight
-
-        if C.use_stft_loss:
-            self.stft_loss = MultiScaleSTFTLoss(**C.stft_loss_params)
-        if C.use_subband_stft_loss:
-            self.subband_stft_loss = MultiScaleSubbandSTFTLoss(**C.subband_stft_loss_params)
-        if C.use_mse_gan_loss:
-            self.mse_loss = MSEGLoss()
-        if C.use_hinge_gan_loss:
-            self.hinge_loss = HingeGLoss()
-        if C.use_feat_match_loss:
-            self.feat_match_loss = MelganFeatureLoss()
-
-    def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None, y_hat_sub=None, y_sub=None):
-        gen_loss = 0
-        adv_loss = 0
-        return_dict = {}
-
-        # STFT Loss
-        if self.use_stft_loss:
-            stft_loss_mg, stft_loss_sc = self.stft_loss(y_hat.squeeze(1), y.squeeze(1))
-            return_dict['G_stft_loss_mg'] = stft_loss_mg
-            return_dict['G_stft_loss_sc'] = stft_loss_sc
-            gen_loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
-
-        # subband STFT Loss
-        if self.use_subband_stft_loss:
-            subband_stft_loss_mg, subband_stft_loss_sc = self.subband_stft_loss(y_hat_sub, y_sub)
-            return_dict['G_subband_stft_loss_mg'] = subband_stft_loss_mg
-            return_dict['G_subband_stft_loss_sc'] = subband_stft_loss_sc
-            gen_loss += self.subband_stft_loss_weight * (subband_stft_loss_mg + subband_stft_loss_sc)
-
-        # multiscale MSE adversarial loss
-        if self.use_mse_gan_loss and scores_fake is not None:
-            mse_fake_loss = _apply_G_adv_loss(scores_fake, self.mse_loss)
-            return_dict['G_mse_fake_loss'] = mse_fake_loss
-            adv_loss += self.mse_gan_loss_weight * mse_fake_loss
-
-        # multiscale Hinge adversarial loss
-        if self.use_hinge_gan_loss and not scores_fake is not None:
-            hinge_fake_loss = _apply_G_adv_loss(scores_fake, self.hinge_loss)
-            return_dict['G_hinge_fake_loss'] = hinge_fake_loss
-            adv_loss += self.hinge_gan_loss_weight * hinge_fake_loss
-
-        # Feature Matching Loss
-        if self.use_feat_match_loss and not feats_fake:
-            feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
-            return_dict['G_feat_match_loss'] = feat_match_loss
-            adv_loss += self.feat_match_loss_weight * feat_match_loss
-        return_dict['G_loss'] = gen_loss + adv_loss
-        return_dict['G_gen_loss'] = gen_loss
-        return_dict['G_adv_loss'] = adv_loss
-        return return_dict
-
-
-class DiscriminatorLoss(nn.Module):
-    """ Compute Discriminator Loss values depending on training
-    configuration """
-    def __init__(self, C):
-        super(DiscriminatorLoss, self).__init__()
-        assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
-            " [!] Cannot use HingeGANLoss and MSEGANLoss together."
-
-        self.use_mse_gan_loss = C.use_mse_gan_loss
-        self.use_hinge_gan_loss = C.use_hinge_gan_loss
-
-        if C.use_mse_gan_loss:
-            self.mse_loss = MSEDLoss()
-        if C.use_hinge_gan_loss:
-            self.hinge_loss = HingeDLoss()
-
-    def forward(self, scores_fake, scores_real):
-        loss = 0
-        return_dict = {}
-
-        if self.use_mse_gan_loss:
-            mse_D_loss, mse_D_real_loss, mse_D_fake_loss = _apply_D_loss(
-                scores_fake=scores_fake,
-                scores_real=scores_real,
-                loss_func=self.mse_loss)
-            return_dict['D_mse_gan_loss'] = mse_D_loss
-            return_dict['D_mse_gan_real_loss'] = mse_D_real_loss
-            return_dict['D_mse_gan_fake_loss'] = mse_D_fake_loss
-            loss += mse_D_loss
-
-        if self.use_hinge_gan_loss:
-            hinge_D_loss, hinge_D_real_loss, hinge_D_fake_loss = _apply_D_loss(
-                scores_fake=scores_fake,
-                scores_real=scores_real,
-                loss_func=self.hinge_loss)
-            return_dict['D_hinge_gan_loss'] = hinge_D_loss
-            return_dict['D_hinge_gan_real_loss'] = hinge_D_real_loss
-            return_dict['D_hinge_gan_fake_loss'] = hinge_D_fake_loss
-            loss += hinge_D_loss
-
-        return_dict['D_loss'] = loss
-        return return_dict
\ No newline at end of file
diff --git a/vocoder/layers/melgan.py b/vocoder/layers/melgan.py
deleted file mode 100644
index 58c12a2e..00000000
--- a/vocoder/layers/melgan.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from torch import nn
-from torch.nn.utils import weight_norm
-
-
-class ResidualStack(nn.Module):
-    def __init__(self, channels, num_res_blocks, kernel_size):
-        super(ResidualStack, self).__init__()
-
-        assert (kernel_size - 1) % 2 == 0, " [!] kernel_size has to be odd."
-        base_padding = (kernel_size - 1) // 2
-
-        self.blocks = nn.ModuleList()
-        for idx in range(num_res_blocks):
-            layer_kernel_size = kernel_size
-            layer_dilation = layer_kernel_size**idx
-            layer_padding = base_padding * layer_dilation
-            self.blocks += [nn.Sequential(
-                nn.LeakyReLU(0.2),
-                nn.ReflectionPad1d(layer_padding),
-                weight_norm(
-                    nn.Conv1d(channels,
-                              channels,
-                              kernel_size=kernel_size,
-                              dilation=layer_dilation,
-                              bias=True)),
-                nn.LeakyReLU(0.2),
-                weight_norm(
-                    nn.Conv1d(channels, channels, kernel_size=1, bias=True)),
-            )]
-
-        self.shortcuts = nn.ModuleList([
-            weight_norm(nn.Conv1d(channels, channels, kernel_size=1,
-                                  bias=True)) for i in range(num_res_blocks)
-        ])
-
-    def forward(self, x):
-        for block, shortcut in zip(self.blocks, self.shortcuts):
-            x = shortcut(x) + block(x)
-        return x
-
-    def remove_weight_norm(self):
-        for block, shortcut in zip(self.blocks, self.shortcuts):
-            nn.utils.remove_weight_norm(block[2])
-            nn.utils.remove_weight_norm(block[4])
-            nn.utils.remove_weight_norm(shortcut)
diff --git a/vocoder/layers/pqmf.py b/vocoder/layers/pqmf.py
deleted file mode 100644
index ef5a3507..00000000
--- a/vocoder/layers/pqmf.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from scipy import signal as sig
-
-
-# adapted from
-# https://github.com/kan-bayashi/ParallelWaveGAN/tree/master/parallel_wavegan
-class PQMF(torch.nn.Module):
-    def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
-        super(PQMF, self).__init__()
-
-        self.N = N
-        self.taps = taps
-        self.cutoff = cutoff
-        self.beta = beta
-
-        QMF = sig.firwin(taps + 1, cutoff, window=('kaiser', beta))
-        H = np.zeros((N, len(QMF)))
-        G = np.zeros((N, len(QMF)))
-        for k in range(N):
-            constant_factor = (2 * k + 1) * (np.pi /
-                                             (2 * N)) * (np.arange(taps + 1) -
-                                                         ((taps - 1) / 2))
-            phase = (-1)**k * np.pi / 4
-            H[k] = 2 * QMF * np.cos(constant_factor + phase)
-
-            G[k] = 2 * QMF * np.cos(constant_factor - phase)
-
-        H = torch.from_numpy(H[:, None, :]).float()
-        G = torch.from_numpy(G[None, :, :]).float()
-
-        self.register_buffer("H", H)
-        self.register_buffer("G", G)
-
-        updown_filter = torch.zeros((N, N, N)).float()
-        for k in range(N):
-            updown_filter[k, k, 0] = 1.0
-        self.register_buffer("updown_filter", updown_filter)
-        self.N = N
-
-        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
-
-    def forward(self, x):
-        return self.analysis(x)
-
-    def analysis(self, x):
-        return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N)
-
-    def synthesis(self, x):
-        x = F.conv_transpose1d(x,
-                               self.updown_filter * self.N,
-                               stride=self.N)
-        x = F.conv1d(x, self.G, padding=self.taps // 2)
-        return x
diff --git a/vocoder/layers/qmf.dat b/vocoder/layers/qmf.dat
deleted file mode 100644
index 17eab137..00000000
--- a/vocoder/layers/qmf.dat
+++ /dev/null
@@ -1,640 +0,0 @@
-  0.0000000e+000
- -5.5252865e-004
- -5.6176926e-004
- -4.9475181e-004
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diff --git a/vocoder/models/__init__.py b/vocoder/models/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/vocoder/models/melgan_discriminator.py b/vocoder/models/melgan_discriminator.py
deleted file mode 100644
index 3847babb..00000000
--- a/vocoder/models/melgan_discriminator.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import numpy as np
-from torch import nn
-from torch.nn.utils import weight_norm
-
-
-class MelganDiscriminator(nn.Module):
-    def __init__(self,
-                 in_channels=1,
-                 out_channels=1,
-                 kernel_sizes=(5, 3),
-                 base_channels=16,
-                 max_channels=1024,
-                 downsample_factors=(4, 4, 4, 4)):
-        super(MelganDiscriminator, self).__init__()
-        self.layers = nn.ModuleList()
-
-        layer_kernel_size = np.prod(kernel_sizes)
-        layer_padding = (layer_kernel_size - 1) // 2
-
-        # initial layer
-        self.layers += [
-            nn.Sequential(
-                nn.ReflectionPad1d(layer_padding),
-                weight_norm(
-                    nn.Conv1d(in_channels,
-                              base_channels,
-                              layer_kernel_size,
-                              stride=1)), nn.LeakyReLU(0.2, inplace=True))
-        ]
-
-        # downsampling layers
-        layer_in_channels = base_channels
-        for downsample_factor in downsample_factors:
-            layer_out_channels = min(layer_in_channels * downsample_factor,
-                                     max_channels)
-            layer_kernel_size = downsample_factor * 10 + 1
-            layer_padding = (layer_kernel_size - 1) // 2
-            layer_groups = layer_in_channels // 4
-            self.layers += [
-                nn.Sequential(
-                    weight_norm(
-                        nn.Conv1d(layer_in_channels,
-                                  layer_out_channels,
-                                  kernel_size=layer_kernel_size,
-                                  stride=downsample_factor,
-                                  padding=layer_padding,
-                                  groups=layer_groups)),
-                    nn.LeakyReLU(0.2, inplace=True))
-            ]
-            layer_in_channels = layer_out_channels
-
-        # last 2 layers
-        layer_padding1 = (kernel_sizes[0] - 1) // 2
-        layer_padding2 = (kernel_sizes[1] - 1) // 2
-        self.layers += [
-            nn.Sequential(
-                weight_norm(
-                    nn.Conv1d(layer_out_channels,
-                              layer_out_channels,
-                              kernel_size=kernel_sizes[0],
-                              stride=1,
-                              padding=layer_padding1)),
-                nn.LeakyReLU(0.2, inplace=True),
-            ),
-            weight_norm(
-                nn.Conv1d(layer_out_channels,
-                          out_channels,
-                          kernel_size=kernel_sizes[1],
-                          stride=1,
-                          padding=layer_padding2)),
-        ]
-
-    def forward(self, x):
-        feats = []
-        for layer in self.layers:
-            x = layer(x)
-            feats.append(x)
-        return x, feats
diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
deleted file mode 100644
index 01b52ea8..00000000
--- a/vocoder/models/melgan_generator.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import torch
-from torch import nn
-from torch.nn.utils import weight_norm
-
-from TTS.vocoder.layers.melgan import ResidualStack
-
-
-class MelganGenerator(nn.Module):
-    def __init__(self,
-                 in_channels=80,
-                 out_channels=1,
-                 proj_kernel=7,
-                 base_channels=512,
-                 upsample_factors=(8, 8, 2, 2),
-                 res_kernel=3,
-                 num_res_blocks=3):
-        super(MelganGenerator, self).__init__()
-
-        # assert model parameters
-        assert (proj_kernel -
-                1) % 2 == 0, " [!] proj_kernel should be an odd number."
-
-        # setup additional model parameters
-        base_padding = (proj_kernel - 1) // 2
-        act_slope = 0.2
-        self.inference_padding = 2
-
-        # initial layer
-        layers = []
-        layers += [
-            nn.ReflectionPad1d(base_padding),
-            weight_norm(
-                nn.Conv1d(in_channels,
-                          base_channels,
-                          kernel_size=proj_kernel,
-                          stride=1,
-                          bias=True))
-        ]
-
-        # upsampling layers and residual stacks
-        for idx, upsample_factor in enumerate(upsample_factors):
-            layer_in_channels = base_channels // (2**idx)
-            layer_out_channels = base_channels // (2**(idx + 1))
-            layer_filter_size = upsample_factor * 2
-            layer_stride = upsample_factor
-            layer_output_padding = upsample_factor % 2
-            layer_padding = upsample_factor // 2 + layer_output_padding
-            layers += [
-                nn.LeakyReLU(act_slope),
-                weight_norm(
-                    nn.ConvTranspose1d(layer_in_channels,
-                                       layer_out_channels,
-                                       layer_filter_size,
-                                       stride=layer_stride,
-                                       padding=layer_padding,
-                                       output_padding=layer_output_padding,
-                                       bias=True)),
-                ResidualStack(
-                    channels=layer_out_channels,
-                    num_res_blocks=num_res_blocks,
-                    kernel_size=res_kernel
-                )
-            ]
-
-        layers += [nn.LeakyReLU(act_slope)]
-
-        # final layer
-        layers += [
-            nn.ReflectionPad1d(base_padding),
-            weight_norm(
-                nn.Conv1d(layer_out_channels,
-                          out_channels,
-                          proj_kernel,
-                          stride=1,
-                          bias=True)),
-            nn.Tanh()
-        ]
-        self.layers = nn.Sequential(*layers)
-
-    def forward(self, c):
-        return self.layers(c)
-
-    def inference(self, c):
-        c = c.to(self.layers[1].weight.device)
-        c = torch.nn.functional.pad(
-            c,
-            (self.inference_padding, self.inference_padding),
-            'replicate')
-        return self.layers(c)
-
-    def remove_weight_norm(self):
-        for _, layer in enumerate(self.layers):
-            if len(layer.state_dict()) != 0:
-                try:
-                    nn.utils.remove_weight_norm(layer)
-                except ValueError:
-                    layer.remove_weight_norm()
-
diff --git a/vocoder/models/melgan_multiscale_discriminator.py b/vocoder/models/melgan_multiscale_discriminator.py
deleted file mode 100644
index dbcc1f30..00000000
--- a/vocoder/models/melgan_multiscale_discriminator.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from torch import nn
-
-from TTS.vocoder.models.melgan_discriminator import MelganDiscriminator
-
-
-class MelganMultiscaleDiscriminator(nn.Module):
-    def __init__(self,
-                 in_channels=1,
-                 out_channels=1,
-                 num_scales=3,
-                 kernel_sizes=(5, 3),
-                 base_channels=16,
-                 max_channels=1024,
-                 downsample_factors=(4, 4, 4),
-                 pooling_kernel_size=4,
-                 pooling_stride=2,
-                 pooling_padding=1):
-        super(MelganMultiscaleDiscriminator, self).__init__()
-
-        self.discriminators = nn.ModuleList([
-            MelganDiscriminator(in_channels=in_channels,
-                                out_channels=out_channels,
-                                kernel_sizes=kernel_sizes,
-                                base_channels=base_channels,
-                                max_channels=max_channels,
-                                downsample_factors=downsample_factors)
-            for _ in range(num_scales)
-        ])
-
-        self.pooling = nn.AvgPool1d(kernel_size=pooling_kernel_size, stride=pooling_stride, padding=pooling_padding, count_include_pad=False)
-
-
-    def forward(self, x):
-        scores = list()
-        feats = list()
-        for disc in self.discriminators:
-            score, feat = disc(x)
-            scores.append(score)
-            feats.append(feat)
-            x = self.pooling(x)
-        return scores, feats
\ No newline at end of file
diff --git a/vocoder/models/multiband_melgan_generator.py b/vocoder/models/multiband_melgan_generator.py
deleted file mode 100644
index 15e7426e..00000000
--- a/vocoder/models/multiband_melgan_generator.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import torch
-
-from TTS.vocoder.models.melgan_generator import MelganGenerator
-from TTS.vocoder.layers.pqmf import PQMF
-
-
-class MultibandMelganGenerator(MelganGenerator):
-    def __init__(self,
-                 in_channels=80,
-                 out_channels=4,
-                 proj_kernel=7,
-                 base_channels=384,
-                 upsample_factors=(2, 8, 2, 2),
-                 res_kernel=3,
-                 num_res_blocks=3):
-        super(MultibandMelganGenerator,
-              self).__init__(in_channels=in_channels,
-                             out_channels=out_channels,
-                             proj_kernel=proj_kernel,
-                             base_channels=base_channels,
-                             upsample_factors=upsample_factors,
-                             res_kernel=res_kernel,
-                             num_res_blocks=num_res_blocks)
-        self.pqmf_layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
-
-    def pqmf_analysis(self, x):
-        return self.pqmf_layer.analysis(x)
-
-    def pqmf_synthesis(self, x):
-        return self.pqmf_layer.synthesis(x)
-
-    @torch.no_grad()
-    def inference(self, cond_features):
-        cond_features = cond_features.to(self.layers[1].weight.device)
-        cond_features = torch.nn.functional.pad(
-            cond_features,
-            (self.inference_padding, self.inference_padding),
-            'replicate')
-        return self.pqmf_synthesis(self.layers(cond_features))
diff --git a/vocoder/models/random_window_discriminator.py b/vocoder/models/random_window_discriminator.py
deleted file mode 100644
index 3efd395e..00000000
--- a/vocoder/models/random_window_discriminator.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import numpy as np
-from torch import nn
-
-
-class GBlock(nn.Module):
-    def __init__(self, in_channels, cond_channels, downsample_factor):
-        super(GBlock, self).__init__()
-
-        self.in_channels = in_channels
-        self.cond_channels = cond_channels
-        self.downsample_factor = downsample_factor
-
-        self.start = nn.Sequential(
-            nn.AvgPool1d(downsample_factor, stride=downsample_factor),
-            nn.ReLU(),
-            nn.Conv1d(in_channels, in_channels * 2, kernel_size=3, padding=1))
-        self.lc_conv1d = nn.Conv1d(cond_channels,
-                                   in_channels * 2,
-                                   kernel_size=1)
-        self.end = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv1d(in_channels * 2,
-                      in_channels * 2,
-                      kernel_size=3,
-                      dilation=2,
-                      padding=2))
-        self.residual = nn.Sequential(
-            nn.Conv1d(in_channels, in_channels * 2, kernel_size=1),
-            nn.AvgPool1d(downsample_factor, stride=downsample_factor))
-
-    def forward(self, inputs, conditions):
-        outputs = self.start(inputs) + self.lc_conv1d(conditions)
-        outputs = self.end(outputs)
-        residual_outputs = self.residual(inputs)
-        outputs = outputs + residual_outputs
-
-        return outputs
-
-
-class DBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, downsample_factor):
-        super(DBlock, self).__init__()
-
-        self.in_channels = in_channels
-        self.downsample_factor = downsample_factor
-        self.out_channels = out_channels
-
-        self.donwsample_layer = nn.AvgPool1d(downsample_factor,
-                                             stride=downsample_factor)
-        self.layers = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1),
-            nn.ReLU(),
-            nn.Conv1d(out_channels,
-                      out_channels,
-                      kernel_size=3,
-                      dilation=2,
-                      padding=2))
-        self.residual = nn.Sequential(
-            nn.Conv1d(in_channels, out_channels, kernel_size=1), )
-
-    def forward(self, inputs):
-        if self.downsample_factor > 1:
-            outputs = self.layers(self.donwsample_layer(inputs))\
-                + self.donwsample_layer(self.residual(inputs))
-        else:
-            outputs = self.layers(inputs) + self.residual(inputs)
-        return outputs
-
-
-class ConditionalDiscriminator(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 cond_channels,
-                 downsample_factors=(2, 2, 2),
-                 out_channels=(128, 256)):
-        super(ConditionalDiscriminator, self).__init__()
-
-        assert len(downsample_factors) == len(out_channels) + 1
-
-        self.in_channels = in_channels
-        self.cond_channels = cond_channels
-        self.downsample_factors = downsample_factors
-        self.out_channels = out_channels
-
-        self.pre_cond_layers = nn.ModuleList()
-        self.post_cond_layers = nn.ModuleList()
-
-        # layers before condition features
-        self.pre_cond_layers += [DBlock(in_channels, 64, 1)]
-        in_channels = 64
-        for (i, channel) in enumerate(out_channels):
-            self.pre_cond_layers.append(
-                DBlock(in_channels, channel, downsample_factors[i]))
-            in_channels = channel
-
-        # condition block
-        self.cond_block = GBlock(in_channels, cond_channels,
-                                 downsample_factors[-1])
-
-        # layers after condition block
-        self.post_cond_layers += [
-            DBlock(in_channels * 2, in_channels * 2, 1),
-            DBlock(in_channels * 2, in_channels * 2, 1),
-            nn.AdaptiveAvgPool1d(1),
-            nn.Conv1d(in_channels * 2, 1, kernel_size=1),
-        ]
-
-    def forward(self, inputs, conditions):
-        batch_size = inputs.size()[0]
-        outputs = inputs.view(batch_size, self.in_channels, -1)
-        for layer in self.pre_cond_layers:
-            outputs = layer(outputs)
-        outputs = self.cond_block(outputs, conditions)
-        for layer in self.post_cond_layers:
-            outputs = layer(outputs)
-
-        return outputs
-
-
-class UnconditionalDiscriminator(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 base_channels=64,
-                 downsample_factors=(8, 4),
-                 out_channels=(128, 256)):
-        super(UnconditionalDiscriminator, self).__init__()
-
-        self.downsample_factors = downsample_factors
-        self.in_channels = in_channels
-        self.downsample_factors = downsample_factors
-        self.out_channels = out_channels
-
-        self.layers = nn.ModuleList()
-        self.layers += [DBlock(self.in_channels, base_channels, 1)]
-        in_channels = base_channels
-        for (i, factor) in enumerate(downsample_factors):
-            self.layers.append(DBlock(in_channels, out_channels[i], factor))
-            in_channels *= 2
-        self.layers += [
-            DBlock(in_channels, in_channels, 1),
-            DBlock(in_channels, in_channels, 1),
-            nn.AdaptiveAvgPool1d(1),
-            nn.Conv1d(in_channels, 1, kernel_size=1),
-        ]
-
-    def forward(self, inputs):
-        batch_size = inputs.size()[0]
-        outputs = inputs.view(batch_size, self.in_channels, -1)
-        for layer in self.layers:
-            outputs = layer(outputs)
-        return outputs
-
-
-class RandomWindowDiscriminator(nn.Module):
-    """Random Window Discriminator as described in
-    http://arxiv.org/abs/1909.11646"""
-    def __init__(self,
-                 cond_channels,
-                 hop_length,
-                 uncond_disc_donwsample_factors=(8, 4),
-                 cond_disc_downsample_factors=((8, 4, 2, 2, 2), (8, 4, 2, 2),
-                                               (8, 4, 2), (8, 4), (4, 2, 2)),
-                 cond_disc_out_channels=((128, 128, 256, 256), (128, 256, 256),
-                                         (128, 256), (256, ), (128, 256)),
-                 window_sizes=(512, 1024, 2048, 4096, 8192)):
-
-        super(RandomWindowDiscriminator, self).__init__()
-        self.cond_channels = cond_channels
-        self.window_sizes = window_sizes
-        self.hop_length = hop_length
-        self.base_window_size = self.hop_length * 2
-        self.ks = [ws // self.base_window_size for ws in window_sizes]
-
-        # check arguments
-        assert len(cond_disc_downsample_factors) == len(
-            cond_disc_out_channels) == len(window_sizes)
-        for ws in window_sizes:
-            assert ws % hop_length == 0
-
-        for idx, cf in enumerate(cond_disc_downsample_factors):
-            assert np.prod(cf) == hop_length // self.ks[idx]
-
-        # define layers
-        self.unconditional_discriminators = nn.ModuleList([])
-        for k in self.ks:
-            layer = UnconditionalDiscriminator(
-                in_channels=k,
-                base_channels=64,
-                downsample_factors=uncond_disc_donwsample_factors)
-            self.unconditional_discriminators.append(layer)
-
-        self.conditional_discriminators = nn.ModuleList([])
-        for idx, k in enumerate(self.ks):
-            layer = ConditionalDiscriminator(
-                in_channels=k,
-                cond_channels=cond_channels,
-                downsample_factors=cond_disc_downsample_factors[idx],
-                out_channels=cond_disc_out_channels[idx])
-            self.conditional_discriminators.append(layer)
-
-    def forward(self, x, c):
-        scores = []
-        feats = []
-        # unconditional pass
-        for (window_size, layer) in zip(self.window_sizes,
-                                        self.unconditional_discriminators):
-            index = np.random.randint(x.shape[-1] - window_size)
-
-            score = layer(x[:, :, index:index + window_size])
-            scores.append(score)
-
-        # conditional pass
-        for (window_size, layer) in zip(self.window_sizes,
-                                        self.conditional_discriminators):
-            frame_size = window_size // self.hop_length
-            lc_index = np.random.randint(c.shape[-1] - frame_size)
-            sample_index = lc_index * self.hop_length
-            x_sub = x[:, :,
-                      sample_index:(lc_index + frame_size) * self.hop_length]
-            c_sub = c[:, :, lc_index:lc_index + frame_size]
-
-            score = layer(x_sub, c_sub)
-            scores.append(score)
-        return scores, feats
diff --git a/vocoder/notebooks/Untitled.ipynb b/vocoder/notebooks/Untitled.ipynb
deleted file mode 100644
index ce49d6fa..00000000
--- a/vocoder/notebooks/Untitled.ipynb
+++ /dev/null
@@ -1,678 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "#function example with several unknowns (variables) for optimization\n",
-    "#Gerald Schuller, Nov. 2016\n",
-    "import numpy as np\n",
-    "\n",
-    "def functionexamp(x):\n",
-    "    #x: array with 2 variables\n",
-    "    \n",
-    "    y=np.sin(x[0])+np.cos(x[1])\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     fun: -1.9999999999888387\n",
-      "     jac: array([4.7236681e-06, 0.0000000e+00])\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 12\n",
-      "     nit: 2\n",
-      "    njev: 3\n",
-      "  status: 0\n",
-      " success: True\n",
-      "       x: array([-1.5707916 , -3.14159265])\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Optimization example, see also:\n",
-    "#https://docs.scipy.org/doc/scipy-0.18.1/reference/optimize.html\n",
-    "#Gerald Schuller, Nov. 2016\n",
-    "#run it with \"python optimizationExample.py\" in a termina shell\n",
-    "#or type \"ipython\" in a termina shell and copy lines below:\n",
-    "\n",
-    "import numpy as np\n",
-    "import scipy.optimize as optimize\n",
-    "\n",
-    "#Example for 2 unknowns, args: function-name, starting point, method:\n",
-    "xmin = optimize.minimize(functionexamp, [-1.0, -3.0], method='CG')\n",
-    "print(xmin)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "function [p,passedge] = opt_filter(filtorder,N)\n",
-    "\n",
-    "% opt_filter      Create Lowpass Prototype Filter for the Pseudo-QMF \n",
-    "%                 Filter Bank with N Subbands\n",
-    "%\n",
-    "% Adapted from the paper by C. D. Creusere and S. K. Mitra, titled \n",
-    "%   \"A simple method for designing high-quality prototype filters for \n",
-    "%   M-band pseudo-QMF banks,\" IEEE Trans. Signal Processing,vol. 43, \n",
-    "%   pp. 1005-1007, Apr. 1995 and the book by S. K. Mitra titled \"\n",
-    "%   Digital Signal Processing: A Computer-Based Approach, McGraw-Hill, 2001\n",
-    "%\n",
-    "% Arguments:\n",
-    "% filtorder     Filter order (i.e., filter length - 1)\n",
-    "% N             Number of subbands\n",
-    "\n",
-    "stopedge = 1/N;       % Stopband edge fixed at (1/N)pi\n",
-    "passedge = 1/(4*N);   % Start value for passband edge\n",
-    "tol = 0.000001;       % Tolerance\n",
-    "step = 0.1*passedge;  % Step size for searching the passband edge\n",
-    "way = -1;             % Search direction, increase or reduce the passband edge\n",
-    "tcost = 0;            % Current error calculated with the cost function\n",
-    "pcost = 10;           % Previous error calculated with the cost function\n",
-    "flag = 0;             % Set to 1 to stop the search\n",
-    "\n",
-    "while flag == 0\n",
-    "    \n",
-    "% Design the lowpass filter using Parks-McClellan algorithm\n",
-    " \n",
-    "    p = remez(filtorder,[0,passedge,stopedge,1],[1,1,0,0],[5,1]);\n",
-    "    \n",
-    "% Calculates the cost function according to Eq. (2.36)\n",
-    "\n",
-    "    P = fft(p,4096);\n",
-    "    OptRange = floor(2048/N);           % 0 to pi/N\n",
-    "    phi = zeros(OptRange,1);            % Initialize to zeros\n",
-    "\n",
-    "% Compute the flatness in the range from 0 to pi/N\n",
-    "\n",
-    "\tfor k = 1:OptRange\n",
-    "          phi(k) = abs(P(OptRange-k+2))^2 + abs(P(k))^2;\n",
-    "\tend\n",
-    "\ttcost = max(abs(phi - ones(max(size(phi)),1)));\n",
-    "   \t\n",
-    "\tif tcost > pcost                % If search in wrong direction\n",
-    "\t\tstep = step/2;          % Reduce step size by half \n",
-    "\t\tway = -way;             % Change the search direction \n",
-    "\tend\n",
-    "\t\n",
-    "\tif abs(pcost - tcost) < tol     % If improvement is below tol         \n",
-    "\t\tflag = 1;               % Stop the search                    \n",
-    "\tend\n",
-    "\t\n",
-    "\tpcost = tcost;\n",
-    "\tpassedge = passedge + way*step; % Adjust the passband edge\n",
-    "   \n",
-    "end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "sig.remez"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0125"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 / 4. / 20.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Band edges should be less than 1/2 the sampling frequency",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-90-fb4445f5709b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremez\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m16.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/scipy/signal/fir_filter_design.py\u001b[0m in \u001b[0;36mremez\u001b[0;34m(numtaps, bands, desired, weight, Hz, type, maxiter, grid_density, fs)\u001b[0m\n\u001b[1;32m    854\u001b[0m     \u001b[0mbands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m     return sigtools._remez(numtaps, bands, desired, weight, tnum, fs,\n\u001b[0;32m--> 856\u001b[0;31m                            maxiter, grid_density)\n\u001b[0m\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Band edges should be less than 1/2 the sampling frequency"
-     ]
-    }
-   ],
-   "source": [
-    "p = sig.remez(65, [0, 1/16.0, 1/4.0, 1], [1, 0], [5, 1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "def create_pqmf_filter(filter_len=64, N=4):\n",
-    "    stop_edge = 1 / N\n",
-    "    pass_edge = 1 / (4 * N)\n",
-    "    tol = 1e-8\n",
-    "    cutoff = 0.1 * pass_edge\n",
-    "    cost = 0\n",
-    "    cost_prev = float('inf')\n",
-    "    \n",
-    "    p = sig.remez(filter_len, [0, pass_edge, stop_edge, 1], [1, 1, 0, 0], [5, 1])\n",
-    "    \n",
-    "    P = sig.freqz(p, workN=2048)\n",
-    "    opt_range = 2048 // N\n",
-    "    phi = np.zeros(opt_range)\n",
-    "    \n",
-    "    H = np.abs(P)\n",
-    "    phi = H[opt_range  + 2] \n",
-    "    for i in range(opt_range):\n",
-    "        phi[i] = abs(P(opt_range - i + 2)) ** 2 + abs(P[i]) ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import scipy as sp\n",
-    "import scipy.signal as sig\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "\n",
-    "def optimfuncQMF(x):\n",
-    "    \"\"\"Optimization function for a PQMF Filterbank\n",
-    "    x: coefficients to optimize (first half of prototype h because of symmetry)\n",
-    "    err: resulting total error\n",
-    "    \"\"\"\n",
-    "    K = ntaps * N \n",
-    "    h = np.append(x, np.flipud(x))\n",
-    "    cutoff = 0.15\n",
-    "    \n",
-    "#     breakpoint()\n",
-    "    f, H_im = sig.freqz(h, worN=K)\n",
-    "    H = np.abs(H_im) #only keeping the real part\n",
-    "    \n",
-    "    posfreq = np.square(H[0:K//N])\n",
-    "    \n",
-    "    #Negative frequencies are symmetric around 0:\n",
-    "    negfreq = np.flipud(np.square(H[0:K//N]))\n",
-    "    \n",
-    "    #Sum of magnitude squared frequency responses should be closed to unity (or N)\n",
-    "    unitycond = np.sum(np.abs(posfreq + negfreq - 2*(N*N)*np.ones(K//N)))/K\n",
-    "    \n",
-    "    #plt.plot(posfreq+negfreq)\n",
-    "    \n",
-    "    #High attenuation after the next subband:\n",
-    "    att = np.sum(np.abs(H[int(cutoff*K//N):]))/K\n",
-    "    \n",
-    "    #Total (weighted) error:\n",
-    "    err = unitycond + 100*att\n",
-    "    return err"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(32,)"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xmin.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8.684549400499243\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import scipy.optimize as opt\n",
-    "import scipy.signal as sig\n",
-    "\n",
-    "ntaps = 64\n",
-    "N = 4\n",
-    "\n",
-    "#optimize for 16 filter coefficients:\n",
-    "xmin = opt.minimize(optimfuncQMF, ntaps*np.ones(ntaps), method='SLSQP', tol=1e-8)\n",
-    "xmin = xmin[\"x\"]\n",
-    "\n",
-    "err = optimfuncQMF(xmin)\n",
-    "print(err)\n",
-    "\n",
-    "#Restore symmetric upper half of window:\n",
-    "h = np.concatenate((xmin, np.flipud(xmin)))\n",
-    "plt.plot(h)\n",
-    "plt.title('Resulting PQMF Window Function')\n",
-    "plt.xlabel('Sample')\n",
-    "plt.ylabel('Value')\n",
-    "plt.show()\n",
-    "\n",
-    "f, H = sig.freqz(h)\n",
-    "plt.plot(f, 20*np.log10(np.abs(H)))\n",
-    "plt.title('Resulting PQMF Magnitude Response')\n",
-    "plt.xlabel('Normalized Frequency')\n",
-    "plt.ylabel('dB')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "N = 4\n",
-    "f, H_im = sig.freqz(h)\n",
-    "posfreq = np.square(H[0:512//N])\n",
-    "negfreq = np.flipud(np.square(H[0:512//N]))\n",
-    "plt.plot((np.abs(posfreq) + np.abs(negfreq)))\n",
-    "plt.xlabel('Frequency (512 is Nyquist)')\n",
-    "plt.ylabel('Magnitude')\n",
-    "plt.title('Unity Condition, Sum of Squared Magnitude of 2 Neighboring Subbands')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "b = sig.firwin(80, 0.5, window=('kaiser', 8))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, H_im = sig.freqz(h)\n",
-    "posfreq = np.square(H[0:512//N])\n",
-    "negfreq = np.flipud(np.square(H[0:512//N]))\n",
-    "plt.plot((np.abs(posfreq) + np.abs(negfreq)))\n",
-    "plt.xlabel('Frequency (512 is Nyquist)')\n",
-    "plt.ylabel('Magnitude')\n",
-    "plt.title('Unity Condition, Sum of Squared Magnitude of 2 Neighboring Subbands')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(63,)"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cutoff = 0.15\n",
-    "beta = 9\n",
-    "ntaps = 63\n",
-    "N = 4\n",
-    "\n",
-    "b = sig.firwin(ntaps, cutoff, window=('kaiser', beta))\n",
-    "w, h = sig.freqz(b)\n",
-    "\n",
-    "plt.plot(b)\n",
-    "plt.title('Resulting PQMF Window Function')\n",
-    "plt.xlabel('Sample')\n",
-    "plt.ylabel('Value')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "fig, ax1 = plt.subplots()\n",
-    "ax1.set_title('Digital filter frequency response')\n",
-    "\n",
-    "ax1.plot(w / (2 * np.pi), 20 * np.log10(abs(h)), 'b')\n",
-    "ax1.set_ylabel('Amplitude [dB]', color='b')\n",
-    "ax1.set_xlabel('Frequency [rad/sample]')\n",
-    "\n",
-    "ax2 = ax1.twinx()\n",
-    "angles = np.unwrap(np.angle(h))\n",
-    "ax2.plot(w / (2 * np.pi), angles, 'g')\n",
-    "ax2.set_ylabel('Angle (radians)', color='g')\n",
-    "ax2.grid()\n",
-    "ax2.axis('tight')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(63,)"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": [
-    "def optimfuncQMF(x):\n",
-    "    \"\"\"Optimization function for a PQMF Filterbank\n",
-    "    x: coefficients to optimize (first half of prototype h because of symmetry)\n",
-    "    err: resulting total error\n",
-    "    \"\"\"\n",
-    "    N = 2 #4 subbands\n",
-    "    cutoff = 1.5 #1.5\n",
-    "    h = np.append(x, np.flipud(x))\n",
-    "    f, H_im = sig.freqz(h)\n",
-    "    H = np.abs(H_im) #only keeping the real part\n",
-    "    \n",
-    "    posfreq = np.square(H[0:512//N])\n",
-    "    \n",
-    "    #Negative frequencies are symmetric around 0:\n",
-    "    negfreq = np.flipud(np.square(H[0:512//N]))\n",
-    "    \n",
-    "    #Sum of magnitude squared frequency responses should be closed to unity (or N)\n",
-    "    unitycond = np.sum(np.abs(posfreq + negfreq - 2*(N*N)*np.ones(512//N)))//512\n",
-    "    \n",
-    "    #plt.plot(posfreq+negfreq)\n",
-    "    \n",
-    "    #High attenuation after the next subband:\n",
-    "    att = np.sum(np.abs(H[int(cutoff*512//N):]))//512\n",
-    "    \n",
-    "    #Total (weighted) error:\n",
-    "    err = unitycond + 100*att\n",
-    "    return err"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "err = optimfuncQMF(b)\n",
-    "print(err)\n",
-    "\n",
-    "#Restore symmetric upper half of window:\n",
-    "h = np.concatenate((xmin, np.flipud(xmin)))\n",
-    "plt.plot(h)\n",
-    "plt.title('Resulting PQMF Window Function')\n",
-    "plt.xlabel('Sample')\n",
-    "plt.ylabel('Value')\n",
-    "plt.show()\n",
-    "\n",
-    "f, H = sig.freqz(h)\n",
-    "plt.plot(f, 20*np.log10(np.abs(H)))\n",
-    "plt.title('Resulting PQMF Magnitude Response')\n",
-    "plt.xlabel('Normalized Frequency')\n",
-    "plt.ylabel('dB')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "Collapsed": "false"
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.7.4 64-bit ('base': conda)",
-   "language": "python",
-   "name": "python37464bitbaseconda58faf23c4b5f4fef93406f29a1005f35"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/vocoder/notebooks/Untitled1.ipynb b/vocoder/notebooks/Untitled1.ipynb
deleted file mode 100644
index 7fec5150..00000000
--- a/vocoder/notebooks/Untitled1.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/vocoder/pqmf_output.wav b/vocoder/pqmf_output.wav
deleted file mode 100644
index 8a77747b..00000000
Binary files a/vocoder/pqmf_output.wav and /dev/null differ
diff --git a/vocoder/tests/__init__.py b/vocoder/tests/__init__.py
deleted file mode 100644
index 8b137891..00000000
--- a/vocoder/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/vocoder/tf/convert_melgan_tflite.py b/vocoder/tf/convert_melgan_tflite.py
deleted file mode 100644
index 9a652b57..00000000
--- a/vocoder/tf/convert_melgan_tflite.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Convert Tensorflow Tacotron2 model to TF-Lite binary
-
-import argparse
-
-from TTS.utils.io import load_config
-from TTS.vocoder.tf.utils.generic_utils import setup_generator
-from TTS.vocoder.tf.utils.io import load_checkpoint
-from TTS.vocoder.tf.utils.tflite import convert_melgan_to_tflite
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--tf_model',
-                    type=str,
-                    help='Path to target torch model to be converted to TF.')
-parser.add_argument('--config_path',
-                    type=str,
-                    help='Path to config file of torch model.')
-parser.add_argument('--output_path',
-                    type=str,
-                    help='path to tflite output binary.')
-args = parser.parse_args()
-
-# Set constants
-CONFIG = load_config(args.config_path)
-
-# load the model
-model = setup_generator(CONFIG)
-model.build_inference()
-model = load_checkpoint(model, args.tf_model)
-
-# create tflite model
-tflite_model = convert_melgan_to_tflite(model, output_path=args.output_path)
-
diff --git a/vocoder/tf/convert_melgan_torch_to_tf.py b/vocoder/tf/convert_melgan_torch_to_tf.py
deleted file mode 100644
index 4c8515d9..00000000
--- a/vocoder/tf/convert_melgan_torch_to_tf.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import argparse
-import os
-
-import numpy as np
-import tensorflow as tf
-import torch
-from fuzzywuzzy import fuzz
-
-from TTS.utils.io import load_config
-from TTS.vocoder.tf.utils.convert_torch_to_tf_utils import (
-    compare_torch_tf, convert_tf_name, transfer_weights_torch_to_tf)
-from TTS.vocoder.tf.utils.generic_utils import \
-    setup_generator as setup_tf_generator
-from TTS.vocoder.tf.utils.io import save_checkpoint
-from TTS.vocoder.utils.generic_utils import setup_generator
-
-# prevent GPU use
-os.environ['CUDA_VISIBLE_DEVICES'] = ''
-
-# define args
-parser = argparse.ArgumentParser()
-parser.add_argument('--torch_model_path',
-                    type=str,
-                    help='Path to target torch model to be converted to TF.')
-parser.add_argument('--config_path',
-                    type=str,
-                    help='Path to config file of torch model.')
-parser.add_argument(
-    '--output_path',
-    type=str,
-    help='path to output file including file name to save TF model.')
-args = parser.parse_args()
-
-# load model config
-config_path = args.config_path
-c = load_config(config_path)
-num_speakers = 0
-
-# init torch model
-model = setup_generator(c)
-checkpoint = torch.load(args.torch_model_path,
-                        map_location=torch.device('cpu'))
-state_dict = checkpoint['model']
-model.load_state_dict(state_dict)
-model.remove_weight_norm()
-state_dict = model.state_dict()
-
-# init tf model
-model_tf = setup_tf_generator(c)
-
-common_sufix = '/.ATTRIBUTES/VARIABLE_VALUE'
-# get tf_model graph by passing an input
-# B x D x T
-dummy_input = tf.random.uniform((7, 80, 64), dtype=tf.float32)
-mel_pred = model_tf(dummy_input, training=False)
-
-# get tf variables
-tf_vars = model_tf.weights
-
-# match variable names with fuzzy logic
-torch_var_names = list(state_dict.keys())
-tf_var_names = [we.name for we in model_tf.weights]
-var_map = []
-for tf_name in tf_var_names:
-    # skip re-mapped layer names
-    if tf_name in [name[0] for name in var_map]:
-        continue
-    tf_name_edited = convert_tf_name(tf_name)
-    ratios = [
-        fuzz.ratio(torch_name, tf_name_edited)
-        for torch_name in torch_var_names
-    ]
-    max_idx = np.argmax(ratios)
-    matching_name = torch_var_names[max_idx]
-    del torch_var_names[max_idx]
-    var_map.append((tf_name, matching_name))
-
-# pass weights
-tf_vars = transfer_weights_torch_to_tf(tf_vars, dict(var_map), state_dict)
-
-# Compare TF and TORCH models
-# check embedding outputs
-model.eval()
-dummy_input_torch = torch.ones((1, 80, 10))
-dummy_input_tf = tf.convert_to_tensor(dummy_input_torch.numpy())
-dummy_input_tf = tf.transpose(dummy_input_tf, perm=[0, 2, 1])
-dummy_input_tf = tf.expand_dims(dummy_input_tf, 2)
-
-out_torch = model.layers[0](dummy_input_torch)
-out_tf = model_tf.model_layers[0](dummy_input_tf)
-out_tf_ = tf.transpose(out_tf, perm=[0, 3, 2, 1])[:, :, 0, :]
-
-assert compare_torch_tf(out_torch, out_tf_) < 1e-5
-
-for i in range(1, len(model.layers)):
-    print(f"{i} -> {model.layers[i]} vs {model_tf.model_layers[i]}")
-    out_torch = model.layers[i](out_torch)
-    out_tf = model_tf.model_layers[i](out_tf)
-    out_tf_ = tf.transpose(out_tf, perm=[0, 3, 2, 1])[:, :, 0, :]
-    diff = compare_torch_tf(out_torch, out_tf_)
-    assert diff < 1e-5, diff
-
-torch.manual_seed(0)
-dummy_input_torch = torch.rand((1, 80, 100))
-dummy_input_tf = tf.convert_to_tensor(dummy_input_torch.numpy())
-model.inference_padding = 0
-model_tf.inference_padding = 0
-output_torch = model.inference(dummy_input_torch)
-output_tf = model_tf(dummy_input_tf, training=False)
-assert compare_torch_tf(output_torch, output_tf) < 1e-5, compare_torch_tf(
-    output_torch, output_tf)
-
-# save tf model
-save_checkpoint(model_tf, checkpoint['step'], checkpoint['epoch'],
-                args.output_path)
-print(' > Model conversion is successfully completed :).')
-
diff --git a/vocoder/tf/layers/melgan.py b/vocoder/tf/layers/melgan.py
deleted file mode 100644
index 3fad4c2a..00000000
--- a/vocoder/tf/layers/melgan.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import tensorflow as tf
-
-
-class ReflectionPad1d(tf.keras.layers.Layer):
-    def __init__(self, padding):
-        super(ReflectionPad1d, self).__init__()
-        self.padding = padding
-
-    def call(self, x):
-        return tf.pad(x, [[0, 0], [self.padding, self.padding], [0, 0], [0, 0]], "REFLECT")
-
-
-class ResidualStack(tf.keras.layers.Layer):
-    def __init__(self, channels, num_res_blocks, kernel_size, name):
-        super(ResidualStack, self).__init__(name=name)
-
-        assert (kernel_size - 1) % 2 == 0, " [!] kernel_size has to be odd."
-        base_padding = (kernel_size - 1) // 2
-
-        self.blocks = []
-        num_layers = 2
-        for idx in range(num_res_blocks):
-            layer_kernel_size = kernel_size
-            layer_dilation = layer_kernel_size**idx
-            layer_padding = base_padding * layer_dilation
-            block = [
-                tf.keras.layers.LeakyReLU(0.2),
-                ReflectionPad1d(layer_padding),
-                tf.keras.layers.Conv2D(filters=channels,
-                                       kernel_size=(kernel_size, 1),
-                                       dilation_rate=(layer_dilation, 1),
-                                       use_bias=True,
-                                       padding='valid',
-                                       name=f'blocks.{idx}.{num_layers}'),
-                tf.keras.layers.LeakyReLU(0.2),
-                tf.keras.layers.Conv2D(filters=channels,
-                                       kernel_size=(1, 1),
-                                       use_bias=True,
-                                       name=f'blocks.{idx}.{num_layers + 2}')
-            ]
-            self.blocks.append(block)
-        self.shortcuts = [
-            tf.keras.layers.Conv2D(channels,
-                                   kernel_size=1,
-                                   use_bias=True,
-                                   name=f'shortcuts.{i}')
-            for i in range(num_res_blocks)
-        ]
-
-    def call(self, x):
-        # breakpoint()
-        for block, shortcut in zip(self.blocks, self.shortcuts):
-            res = shortcut(x)
-            for layer in block:
-                x = layer(x)
-            x += res
-        return x
\ No newline at end of file
diff --git a/vocoder/tf/layers/pqmf.py b/vocoder/tf/layers/pqmf.py
deleted file mode 100644
index c018971f..00000000
--- a/vocoder/tf/layers/pqmf.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import numpy as np
-import tensorflow as tf
-
-from scipy import signal as sig
-
-
-class PQMF(tf.keras.layers.Layer):
-    def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
-        super(PQMF, self).__init__()
-        # define filter coefficient
-        self.N = N
-        self.taps = taps
-        self.cutoff = cutoff
-        self.beta = beta
-
-        QMF = sig.firwin(taps + 1, cutoff, window=('kaiser', beta))
-        H = np.zeros((N, len(QMF)))
-        G = np.zeros((N, len(QMF)))
-        for k in range(N):
-            constant_factor = (2 * k + 1) * (np.pi /
-                                             (2 * N)) * (np.arange(taps + 1) -
-                                                         ((taps - 1) / 2))
-            phase = (-1)**k * np.pi / 4
-            H[k] = 2 * QMF * np.cos(constant_factor + phase)
-
-            G[k] = 2 * QMF * np.cos(constant_factor - phase)
-
-        # [N, 1, taps + 1] == [filter_width, in_channels, out_channels]
-        self.H = np.transpose(H[:, None, :], (2, 1, 0)).astype('float32')
-        self.G = np.transpose(G[None, :, :], (2, 1, 0)).astype('float32')
-
-        # filter for downsampling & upsampling
-        updown_filter = np.zeros((N, N, N), dtype=np.float32)
-        for k in range(N):
-            updown_filter[0, k, k] = 1.0
-        self.updown_filter = updown_filter.astype(np.float32)
-
-    def analysis(self, x):
-        """
-        x : B x 1 x T
-        """
-        x = tf.transpose(x, perm=[0, 2, 1])
-        x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]], constant_values=0.0)
-        x = tf.nn.conv1d(x, self.H, stride=1, padding='VALID')
-        x = tf.nn.conv1d(x,
-                         self.updown_filter,
-                         stride=self.N,
-                         padding='VALID')
-        x = tf.transpose(x, perm=[0, 2, 1])
-        return x
-
-    def synthesis(self, x):
-        """
-        x : B x D x T
-        """
-        x = tf.transpose(x, perm=[0, 2, 1])
-        x = tf.nn.conv1d_transpose(
-            x,
-            self.updown_filter * self.N,
-            strides=self.N,
-            output_shape=(tf.shape(x)[0], tf.shape(x)[1] * self.N,
-                          self.N))
-        x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]], constant_values=0.0)
-        x = tf.nn.conv1d(x, self.G, stride=1, padding="VALID")
-        x = tf.transpose(x, perm=[0, 2, 1])
-        return x
diff --git a/vocoder/tf/models/melgan_generator.py b/vocoder/tf/models/melgan_generator.py
deleted file mode 100644
index 168fd29e..00000000
--- a/vocoder/tf/models/melgan_generator.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import logging
-import os
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # FATAL
-logging.getLogger('tensorflow').setLevel(logging.FATAL)
-
-import tensorflow as tf
-from TTS.vocoder.tf.layers.melgan import ResidualStack, ReflectionPad1d
-
-
-#pylint: disable=too-many-ancestors
-#pylint: disable=abstract-method
-class MelganGenerator(tf.keras.models.Model):
-    """ Melgan Generator TF implementation dedicated for inference with no
-    weight norm """
-    def __init__(self,
-                 in_channels=80,
-                 out_channels=1,
-                 proj_kernel=7,
-                 base_channels=512,
-                 upsample_factors=(8, 8, 2, 2),
-                 res_kernel=3,
-                 num_res_blocks=3):
-        super(MelganGenerator, self).__init__()
-
-        self.in_channels = in_channels
-
-        # assert model parameters
-        assert (proj_kernel -
-                1) % 2 == 0, " [!] proj_kernel should be an odd number."
-
-        # setup additional model parameters
-        base_padding = (proj_kernel - 1) // 2
-        act_slope = 0.2
-        self.inference_padding = 2
-
-        # initial layer
-        self.initial_layer = [
-            ReflectionPad1d(base_padding),
-            tf.keras.layers.Conv2D(filters=base_channels,
-                                   kernel_size=(proj_kernel, 1),
-                                   strides=1,
-                                   padding='valid',
-                                   use_bias=True,
-                                   name="1")
-        ]
-        num_layers = 3  # count number of layers for layer naming
-
-        # upsampling layers and residual stacks
-        self.upsample_layers = []
-        for idx, upsample_factor in enumerate(upsample_factors):
-            layer_out_channels = base_channels // (2**(idx + 1))
-            layer_filter_size = upsample_factor * 2
-            layer_stride = upsample_factor
-            # layer_output_padding = upsample_factor % 2
-            self.upsample_layers += [
-                tf.keras.layers.LeakyReLU(act_slope),
-                tf.keras.layers.Conv2DTranspose(
-                    filters=layer_out_channels,
-                    kernel_size=(layer_filter_size, 1),
-                    strides=(layer_stride, 1),
-                    padding='same',
-                    # output_padding=layer_output_padding,
-                    use_bias=True,
-                    name=f'{num_layers}'),
-                ResidualStack(channels=layer_out_channels,
-                              num_res_blocks=num_res_blocks,
-                              kernel_size=res_kernel,
-                              name=f'layers.{num_layers + 1}')
-            ]
-            num_layers += num_res_blocks - 1
-
-        self.upsample_layers += [tf.keras.layers.LeakyReLU(act_slope)]
-
-        # final layer
-        self.final_layers = [
-            ReflectionPad1d(base_padding),
-            tf.keras.layers.Conv2D(filters=out_channels,
-                                   kernel_size=(proj_kernel, 1),
-                                   use_bias=True,
-                                   name=f'layers.{num_layers + 1}'),
-            tf.keras.layers.Activation("tanh")
-        ]
-
-        # self.model_layers = tf.keras.models.Sequential(self.initial_layer + self.upsample_layers + self.final_layers, name="layers")
-        self.model_layers = self.initial_layer + self.upsample_layers + self.final_layers
-
-    @tf.function(experimental_relax_shapes=True)
-    def call(self, c, training=False):
-        """
-        c : B x C x T
-        """
-        if training:
-            raise NotImplementedError()
-        return self.inference(c)
-
-    def inference(self, c):
-        c = tf.transpose(c, perm=[0, 2, 1])
-        c = tf.expand_dims(c, 2)
-        # FIXME: TF had no replicate padding as in Torch
-        # c = tf.pad(c, [[0, 0], [self.inference_padding, self.inference_padding], [0, 0], [0, 0]], "REFLECT")
-        o = c
-        for layer in self.model_layers:
-            o = layer(o)
-        # o = self.model_layers(c)
-        o = tf.transpose(o, perm=[0, 3, 2, 1])
-        return o[:, :, 0, :]
-
-    def build_inference(self):
-        x = tf.random.uniform((1, self.in_channels, 4), dtype=tf.float32)
-        self(x, training=False)
-
-    @tf.function(
-        experimental_relax_shapes=True,
-        input_signature=[
-            tf.TensorSpec([1, None, None], dtype=tf.float32),
-        ],)
-    def inference_tflite(self, c):
-        c = tf.transpose(c, perm=[0, 2, 1])
-        c = tf.expand_dims(c, 2)
-        # FIXME: TF had no replicate padding as in Torch
-        # c = tf.pad(c, [[0, 0], [self.inference_padding, self.inference_padding], [0, 0], [0, 0]], "REFLECT")
-        o = c
-        for layer in self.model_layers:
-            o = layer(o)
-        # o = self.model_layers(c)
-        o = tf.transpose(o, perm=[0, 3, 2, 1])
-        return o[:, :, 0, :]
\ No newline at end of file
diff --git a/vocoder/tf/models/multiband_melgan_generator.py b/vocoder/tf/models/multiband_melgan_generator.py
deleted file mode 100644
index bdd333ed..00000000
--- a/vocoder/tf/models/multiband_melgan_generator.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import tensorflow as tf
-
-from TTS.vocoder.tf.models.melgan_generator import MelganGenerator
-from TTS.vocoder.tf.layers.pqmf import PQMF
-
-#pylint: disable=too-many-ancestors
-#pylint: disable=abstract-method
-class MultibandMelganGenerator(MelganGenerator):
-    def __init__(self,
-                 in_channels=80,
-                 out_channels=4,
-                 proj_kernel=7,
-                 base_channels=384,
-                 upsample_factors=(2, 8, 2, 2),
-                 res_kernel=3,
-                 num_res_blocks=3):
-        super(MultibandMelganGenerator,
-              self).__init__(in_channels=in_channels,
-                             out_channels=out_channels,
-                             proj_kernel=proj_kernel,
-                             base_channels=base_channels,
-                             upsample_factors=upsample_factors,
-                             res_kernel=res_kernel,
-                             num_res_blocks=num_res_blocks)
-        self.pqmf_layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
-
-    def pqmf_analysis(self, x):
-        return self.pqmf_layer.analysis(x)
-
-    def pqmf_synthesis(self, x):
-        return self.pqmf_layer.synthesis(x)
-
-    def inference(self, c):
-        c = tf.transpose(c, perm=[0, 2, 1])
-        c = tf.expand_dims(c, 2)
-        # FIXME: TF had no replicate padding as in Torch
-        # c = tf.pad(c, [[0, 0], [self.inference_padding, self.inference_padding], [0, 0], [0, 0]], "REFLECT")
-        o = c
-        for layer in self.model_layers:
-            o = layer(o)
-        o = tf.transpose(o, perm=[0, 3, 2, 1])
-        o = self.pqmf_layer.synthesis(o[:, :, 0, :])
-        return o
-
-    @tf.function(
-        experimental_relax_shapes=True,
-        input_signature=[
-            tf.TensorSpec([1, 80, None], dtype=tf.float32),
-        ],)
-    def inference_tflite(self, c):
-        c = tf.transpose(c, perm=[0, 2, 1])
-        c = tf.expand_dims(c, 2)
-        # FIXME: TF had no replicate padding as in Torch
-        # c = tf.pad(c, [[0, 0], [self.inference_padding, self.inference_padding], [0, 0], [0, 0]], "REFLECT")
-        o = c
-        for layer in self.model_layers:
-            o = layer(o)
-        o = tf.transpose(o, perm=[0, 3, 2, 1])
-        o = self.pqmf_layer.synthesis(o[:, :, 0, :])
-        return o
diff --git a/vocoder/tf/utils/__init__.py b/vocoder/tf/utils/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/vocoder/tf/utils/convert_torch_to_tf_utils.py b/vocoder/tf/utils/convert_torch_to_tf_utils.py
deleted file mode 100644
index 25139cc3..00000000
--- a/vocoder/tf/utils/convert_torch_to_tf_utils.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import numpy as np
-import tensorflow as tf
-
-
-def compare_torch_tf(torch_tensor, tf_tensor):
-    """ Compute the average absolute difference b/w torch and tf tensors """
-    return abs(torch_tensor.detach().numpy() - tf_tensor.numpy()).mean()
-
-
-def convert_tf_name(tf_name):
-    """ Convert certain patterns in TF layer names to Torch patterns """
-    tf_name_tmp = tf_name
-    tf_name_tmp = tf_name_tmp.replace(':0', '')
-    tf_name_tmp = tf_name_tmp.replace('/forward_lstm/lstm_cell_1/recurrent_kernel', '/weight_hh_l0')
-    tf_name_tmp = tf_name_tmp.replace('/forward_lstm/lstm_cell_2/kernel', '/weight_ih_l1')
-    tf_name_tmp = tf_name_tmp.replace('/recurrent_kernel', '/weight_hh')
-    tf_name_tmp = tf_name_tmp.replace('/kernel', '/weight')
-    tf_name_tmp = tf_name_tmp.replace('/gamma', '/weight')
-    tf_name_tmp = tf_name_tmp.replace('/beta', '/bias')
-    tf_name_tmp = tf_name_tmp.replace('/', '.')
-    return tf_name_tmp
-
-
-def transfer_weights_torch_to_tf(tf_vars, var_map_dict, state_dict):
-    """ Transfer weigths from torch state_dict to TF variables """
-    print(" > Passing weights from Torch to TF ...")
-    for tf_var in tf_vars:
-        torch_var_name = var_map_dict[tf_var.name]
-        print(f' | > {tf_var.name} <-- {torch_var_name}')
-        # if tuple, it is a bias variable
-        if 'kernel' in tf_var.name:
-            torch_weight = state_dict[torch_var_name]
-            numpy_weight = torch_weight.permute([2, 1, 0]).numpy()[:, None, :, :]
-        if 'bias' in tf_var.name:
-            torch_weight = state_dict[torch_var_name]
-            numpy_weight = torch_weight
-        assert np.all(tf_var.shape == numpy_weight.shape), f" [!] weight shapes does not match: {tf_var.name} vs {torch_var_name} --> {tf_var.shape} vs {numpy_weight.shape}"
-        tf.keras.backend.set_value(tf_var, numpy_weight)
-    return tf_vars
-
-
-def load_tf_vars(model_tf, tf_vars):
-    for tf_var in tf_vars:
-        model_tf.get_layer(tf_var.name).set_weights(tf_var)
-    return model_tf
diff --git a/vocoder/tf/utils/generic_utils.py b/vocoder/tf/utils/generic_utils.py
deleted file mode 100644
index 580a3738..00000000
--- a/vocoder/tf/utils/generic_utils.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import re
-import importlib
-
-
-def to_camel(text):
-    text = text.capitalize()
-    return re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), text)
-
-
-def setup_generator(c):
-    print(" > Generator Model: {}".format(c.generator_model))
-    MyModel = importlib.import_module('TTS.vocoder.tf.models.' +
-                                      c.generator_model.lower())
-    MyModel = getattr(MyModel, to_camel(c.generator_model))
-    if c.generator_model in 'melgan_generator':
-        model = MyModel(
-            in_channels=c.audio['num_mels'],
-            out_channels=1,
-            proj_kernel=7,
-            base_channels=512,
-            upsample_factors=c.generator_model_params['upsample_factors'],
-            res_kernel=3,
-            num_res_blocks=c.generator_model_params['num_res_blocks'])
-    if c.generator_model in 'melgan_fb_generator':
-        pass
-    if c.generator_model in 'multiband_melgan_generator':
-        model = MyModel(
-            in_channels=c.audio['num_mels'],
-            out_channels=4,
-            proj_kernel=7,
-            base_channels=384,
-            upsample_factors=c.generator_model_params['upsample_factors'],
-            res_kernel=3,
-            num_res_blocks=c.generator_model_params['num_res_blocks'])
-    return model
\ No newline at end of file
diff --git a/vocoder/tf/utils/io.py b/vocoder/tf/utils/io.py
deleted file mode 100644
index d95d972c..00000000
--- a/vocoder/tf/utils/io.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import datetime
-import pickle
-import tensorflow as tf
-
-
-def save_checkpoint(model, current_step, epoch, output_path, **kwargs):
-    """ Save TF Vocoder model """
-    state = {
-        'model': model.weights,
-        'step': current_step,
-        'epoch': epoch,
-        'date': datetime.date.today().strftime("%B %d, %Y"),
-    }
-    state.update(kwargs)
-    pickle.dump(state, open(output_path, 'wb'))
-
-
-def load_checkpoint(model, checkpoint_path):
-    """ Load TF Vocoder model """
-    checkpoint = pickle.load(open(checkpoint_path, 'rb'))
-    chkp_var_dict = {var.name: var.numpy() for var in checkpoint['model']}
-    tf_vars = model.weights
-    for tf_var in tf_vars:
-        layer_name = tf_var.name
-        chkp_var_value = chkp_var_dict[layer_name]
-        tf.keras.backend.set_value(tf_var, chkp_var_value)
-    return model
\ No newline at end of file
diff --git a/vocoder/tf/utils/tflite.py b/vocoder/tf/utils/tflite.py
deleted file mode 100644
index d0637596..00000000
--- a/vocoder/tf/utils/tflite.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import tensorflow as tf
-
-
-def convert_melgan_to_tflite(model,
-                             output_path=None,
-                             experimental_converter=True):
-    """Convert Tensorflow MelGAN model to TFLite. Save a binary file if output_path is
-    provided, else return TFLite model."""
-
-    concrete_function = model.inference_tflite.get_concrete_function()
-    converter = tf.lite.TFLiteConverter.from_concrete_functions(
-        [concrete_function])
-    converter.experimental_new_converter = experimental_converter
-    converter.optimizations = []
-    converter.target_spec.supported_ops = [
-        tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
-    ]
-    tflite_model = converter.convert()
-    print(f'Tflite Model size is {len(tflite_model) / (1024.0 * 1024.0)} MBs.')
-    if output_path is not None:
-        # same model binary if outputpath is provided
-        with open(output_path, 'wb') as f:
-            f.write(tflite_model)
-        return None
-    return tflite_model
-
-
-def load_tflite_model(tflite_path):
-    tflite_model = tf.lite.Interpreter(model_path=tflite_path)
-    tflite_model.allocate_tensors()
-    return tflite_model
\ No newline at end of file
diff --git a/vocoder/train.py b/vocoder/train.py
deleted file mode 100644
index dc081a5e..00000000
--- a/vocoder/train.py
+++ /dev/null
@@ -1,657 +0,0 @@
-import argparse
-import glob
-import os
-import sys
-import time
-import traceback
-
-import torch
-from torch.utils.data import DataLoader
-
-from inspect import signature
-
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.generic_utils import (KeepAverage, count_parameters,
-                                     create_experiment_folder, get_git_branch,
-                                     remove_experiment_folder, set_init_dict)
-from TTS.utils.io import copy_config_file, load_config
-from TTS.utils.radam import RAdam
-from TTS.utils.tensorboard_logger import TensorboardLogger
-from TTS.utils.training import setup_torch_training_env
-from TTS.vocoder.datasets.gan_dataset import GANDataset
-from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
-# from distribute import (DistributedSampler, apply_gradient_allreduce,
-#                         init_distributed, reduce_tensor)
-from TTS.vocoder.layers.losses import DiscriminatorLoss, GeneratorLoss
-from TTS.vocoder.utils.io import save_checkpoint, save_best_model
-from TTS.vocoder.utils.console_logger import ConsoleLogger
-from TTS.vocoder.utils.generic_utils import (check_config, plot_results,
-                                             setup_discriminator,
-                                             setup_generator)
-
-
-use_cuda, num_gpus = setup_torch_training_env(True, True)
-
-
-def setup_loader(ap, is_val=False, verbose=False):
-    if is_val and not c.run_eval:
-        loader = None
-    else:
-        dataset = GANDataset(ap=ap,
-                             items=eval_data if is_val else train_data,
-                             seq_len=c.seq_len,
-                             hop_len=ap.hop_length,
-                             pad_short=c.pad_short,
-                             conv_pad=c.conv_pad,
-                             is_training=not is_val,
-                             return_segments=not is_val,
-                             use_noise_augment=c.use_noise_augment,
-                             use_cache=c.use_cache,
-                             verbose=verbose)
-        dataset.shuffle_mapping()
-        # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
-        loader = DataLoader(dataset,
-                            batch_size=1 if is_val else c.batch_size,
-                            shuffle=True,
-                            drop_last=False,
-                            sampler=None,
-                            num_workers=c.num_val_loader_workers
-                            if is_val else c.num_loader_workers,
-                            pin_memory=False)
-    return loader
-
-
-def format_data(data):
-    if isinstance(data[0], list):
-        # setup input data
-        c_G, x_G = data[0]
-        c_D, x_D = data[1]
-
-        # dispatch data to GPU
-        if use_cuda:
-            c_G = c_G.cuda(non_blocking=True)
-            x_G = x_G.cuda(non_blocking=True)
-            c_D = c_D.cuda(non_blocking=True)
-            x_D = x_D.cuda(non_blocking=True)
-
-        return c_G, x_G, c_D, x_D
-
-    # return a whole audio segment
-    co, x = data
-    if use_cuda:
-        co = co.cuda(non_blocking=True)
-        x = x.cuda(non_blocking=True)
-    return co, x, None, None
-
-
-def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
-          scheduler_G, scheduler_D, ap, global_step, epoch):
-    data_loader = setup_loader(ap, is_val=False, verbose=(epoch == 0))
-    model_G.train()
-    model_D.train()
-    epoch_time = 0
-    keep_avg = KeepAverage()
-    if use_cuda:
-        batch_n_iter = int(
-            len(data_loader.dataset) / (c.batch_size * num_gpus))
-    else:
-        batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
-    end_time = time.time()
-    c_logger.print_train_start()
-    for num_iter, data in enumerate(data_loader):
-        start_time = time.time()
-
-        # format data
-        c_G, y_G, c_D, y_D = format_data(data)
-        loader_time = time.time() - end_time
-
-        global_step += 1
-
-        ##############################
-        # GENERATOR
-        ##############################
-
-        # generator pass
-        y_hat = model_G(c_G)
-        y_hat_sub = None
-        y_G_sub = None
-        y_hat_vis = y_hat  # for visualization
-
-        # PQMF formatting
-        if y_hat.shape[1] > 1:
-            y_hat_sub = y_hat
-            y_hat = model_G.pqmf_synthesis(y_hat)
-            y_hat_vis = y_hat
-            y_G_sub = model_G.pqmf_analysis(y_G)
-
-        scores_fake, feats_fake, feats_real = None, None, None
-        if global_step > c.steps_to_start_discriminator:
-
-            # run D with or without cond. features
-            if len(signature(model_D.forward).parameters) == 2:
-                D_out_fake = model_D(y_hat, c_G)
-            else:
-                D_out_fake = model_D(y_hat)
-            D_out_real = None
-
-            if c.use_feat_match_loss:
-                with torch.no_grad():
-                    D_out_real = model_D(y_G)
-
-            # format D outputs
-            if isinstance(D_out_fake, tuple):
-                scores_fake, feats_fake = D_out_fake
-                if D_out_real is None:
-                    feats_real = None
-                else:
-                    _, feats_real = D_out_real
-            else:
-                scores_fake = D_out_fake
-
-        # compute losses
-        loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
-                                  feats_real, y_hat_sub, y_G_sub)
-        loss_G = loss_G_dict['G_loss']
-
-        # optimizer generator
-        optimizer_G.zero_grad()
-        loss_G.backward()
-        if c.gen_clip_grad > 0:
-            torch.nn.utils.clip_grad_norm_(model_G.parameters(),
-                                           c.gen_clip_grad)
-        optimizer_G.step()
-        if scheduler_G is not None:
-            scheduler_G.step()
-
-        loss_dict = dict()
-        for key, value in loss_G_dict.items():
-            if isinstance(value, int):
-                loss_dict[key] = value
-            else:
-                loss_dict[key] = value.item()
-
-        ##############################
-        # DISCRIMINATOR
-        ##############################
-        if global_step >= c.steps_to_start_discriminator:
-            # discriminator pass
-            with torch.no_grad():
-                y_hat = model_G(c_D)
-
-            # PQMF formatting
-            if y_hat.shape[1] > 1:
-                y_hat = model_G.pqmf_synthesis(y_hat)
-
-            # run D with or without cond. features
-            if len(signature(model_D.forward).parameters) == 2:
-                D_out_fake = model_D(y_hat.detach(), c_D)
-                D_out_real = model_D(y_D, c_D)
-            else:
-                D_out_fake = model_D(y_hat.detach())
-                D_out_real = model_D(y_D)
-
-            # format D outputs
-            if isinstance(D_out_fake, tuple):
-                scores_fake, feats_fake = D_out_fake
-                if D_out_real is None:
-                    scores_real, feats_real = None, None
-                else:
-                    scores_real, feats_real = D_out_real
-            else:
-                scores_fake = D_out_fake
-                scores_real = D_out_real
-
-            # compute losses
-            loss_D_dict = criterion_D(scores_fake, scores_real)
-            loss_D = loss_D_dict['D_loss']
-
-            # optimizer discriminator
-            optimizer_D.zero_grad()
-            loss_D.backward()
-            if c.disc_clip_grad > 0:
-                torch.nn.utils.clip_grad_norm_(model_D.parameters(),
-                                               c.disc_clip_grad)
-            optimizer_D.step()
-            if scheduler_D is not None:
-                scheduler_D.step()
-
-            for key, value in loss_D_dict.items():
-                if isinstance(value, (int, float)):
-                    loss_dict[key] = value
-                else:
-                    loss_dict[key] = value.item()
-
-        step_time = time.time() - start_time
-        epoch_time += step_time
-
-        # get current learning rates
-        current_lr_G = list(optimizer_G.param_groups)[0]['lr']
-        current_lr_D = list(optimizer_D.param_groups)[0]['lr']
-
-        # update avg stats
-        update_train_values = dict()
-        for key, value in loss_dict.items():
-            update_train_values['avg_' + key] = value
-        update_train_values['avg_loader_time'] = loader_time
-        update_train_values['avg_step_time'] = step_time
-        keep_avg.update_values(update_train_values)
-
-        # print training stats
-        if global_step % c.print_step == 0:
-            c_logger.print_train_step(batch_n_iter, num_iter, global_step,
-                                      step_time, loader_time, current_lr_G,
-                                      current_lr_D, loss_dict,
-                                      keep_avg.avg_values)
-
-        # plot step stats
-        if global_step % 10 == 0:
-            iter_stats = {
-                "lr_G": current_lr_G,
-                "lr_D": current_lr_D,
-                "step_time": step_time
-            }
-            iter_stats.update(loss_dict)
-            tb_logger.tb_train_iter_stats(global_step, iter_stats)
-
-        # save checkpoint
-        if global_step % c.save_step == 0:
-            if c.checkpoint:
-                # save model
-                save_checkpoint(model_G,
-                                optimizer_G,
-                                scheduler_G,
-                                model_D,
-                                optimizer_D,
-                                scheduler_D,
-                                global_step,
-                                epoch,
-                                OUT_PATH,
-                                model_losses=loss_dict)
-
-            # compute spectrograms
-            figures = plot_results(y_hat_vis, y_G, ap, global_step,
-                                   'train')
-            tb_logger.tb_train_figures(global_step, figures)
-
-            # Sample audio
-            sample_voice = y_hat_vis[0].squeeze(0).detach().cpu().numpy()
-            tb_logger.tb_train_audios(global_step,
-                                      {'train/audio': sample_voice},
-                                      c.audio["sample_rate"])
-        end_time = time.time()
-
-    # print epoch stats
-    c_logger.print_train_epoch_end(global_step, epoch, epoch_time, keep_avg)
-
-    # Plot Training Epoch Stats
-    epoch_stats = {"epoch_time": epoch_time}
-    epoch_stats.update(keep_avg.avg_values)
-    tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
-    # TODO: plot model stats
-    # if c.tb_model_param_stats:
-    # tb_logger.tb_model_weights(model, global_step)
-    return keep_avg.avg_values, global_step
-
-
-@torch.no_grad()
-def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch):
-    data_loader = setup_loader(ap, is_val=True, verbose=(epoch == 0))
-    model_G.eval()
-    model_D.eval()
-    epoch_time = 0
-    keep_avg = KeepAverage()
-    end_time = time.time()
-    c_logger.print_eval_start()
-    for num_iter, data in enumerate(data_loader):
-        start_time = time.time()
-
-        # format data
-        c_G, y_G, _, _ = format_data(data)
-        loader_time = time.time() - end_time
-
-        global_step += 1
-
-        ##############################
-        # GENERATOR
-        ##############################
-
-        # generator pass
-        y_hat = model_G(c_G)
-        y_hat_sub = None
-        y_G_sub = None
-
-        # PQMF formatting
-        if y_hat.shape[1] > 1:
-            y_hat_sub = y_hat
-            y_hat = model_G.pqmf_synthesis(y_hat)
-            y_G_sub = model_G.pqmf_analysis(y_G)
-
-
-        scores_fake, feats_fake, feats_real = None, None, None
-        if global_step > c.steps_to_start_discriminator:
-
-            if len(signature(model_D.forward).parameters) == 2:
-                D_out_fake = model_D(y_hat, c_G)
-            else:
-                D_out_fake = model_D(y_hat)
-            D_out_real = None
-
-            if c.use_feat_match_loss:
-                with torch.no_grad():
-                    D_out_real = model_D(y_G)
-
-            # format D outputs
-            if isinstance(D_out_fake, tuple):
-                scores_fake, feats_fake = D_out_fake
-                if D_out_real is None:
-                    feats_real = None
-                else:
-                    _, feats_real = D_out_real
-            else:
-                scores_fake = D_out_fake
-                feats_fake, feats_real = None, None
-
-        # compute losses
-        loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
-                                  feats_real, y_hat_sub, y_G_sub)
-
-        loss_dict = dict()
-        for key, value in loss_G_dict.items():
-            if isinstance(value, (int, float)):
-                loss_dict[key] = value
-            else:
-                loss_dict[key] = value.item()
-
-        ##############################
-        # DISCRIMINATOR
-        ##############################
-
-        if global_step >= c.steps_to_start_discriminator:
-            # discriminator pass
-            with torch.no_grad():
-                y_hat = model_G(c_G)
-
-            # PQMF formatting
-            if y_hat.shape[1] > 1:
-                y_hat = model_G.pqmf_synthesis(y_hat)
-
-            # run D with or without cond. features
-            if len(signature(model_D.forward).parameters) == 2:
-                D_out_fake = model_D(y_hat.detach(), c_G)
-                D_out_real = model_D(y_G, c_G)
-            else:
-                D_out_fake = model_D(y_hat.detach())
-                D_out_real = model_D(y_G)
-
-            # format D outputs
-            if isinstance(D_out_fake, tuple):
-                scores_fake, feats_fake = D_out_fake
-                if D_out_real is None:
-                    scores_real, feats_real = None, None
-                else:
-                    scores_real, feats_real = D_out_real
-            else:
-                scores_fake = D_out_fake
-                scores_real = D_out_real
-
-            # compute losses
-            loss_D_dict = criterion_D(scores_fake, scores_real)
-
-            for key, value in loss_D_dict.items():
-                if isinstance(value, (int, float)):
-                    loss_dict[key] = value
-                else:
-                    loss_dict[key] = value.item()
-
-
-        step_time = time.time() - start_time
-        epoch_time += step_time
-
-        # update avg stats
-        update_eval_values = dict()
-        for key, value in loss_dict.items():
-            update_eval_values['avg_' + key] = value
-        update_eval_values['avg_loader_time'] = loader_time
-        update_eval_values['avg_step_time'] = step_time
-        keep_avg.update_values(update_eval_values)
-
-        # print eval stats
-        if c.print_eval:
-            c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
-
-    # compute spectrograms
-    figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
-    tb_logger.tb_eval_figures(global_step, figures)
-
-    # Sample audio
-    sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
-    tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
-                             c.audio["sample_rate"])
-
-    # synthesize a full voice
-    data_loader.return_segments = False
-
-    tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
-
-    return keep_avg.avg_values
-
-
-# FIXME: move args definition/parsing inside of main?
-def main(args):  # pylint: disable=redefined-outer-name
-    # pylint: disable=global-variable-undefined
-    global train_data, eval_data
-    print(f" > Loading wavs from: {c.data_path}")
-    if c.feature_path is not None:
-        print(f" > Loading features from: {c.feature_path}")
-        eval_data, train_data = load_wav_feat_data(c.data_path, c.feature_path, c.eval_split_size)
-    else:
-        eval_data, train_data = load_wav_data(c.data_path, c.eval_split_size)
-
-    # setup audio processor
-    ap = AudioProcessor(**c.audio)
-
-    # DISTRUBUTED
-    # if num_gpus > 1:
-    # init_distributed(args.rank, num_gpus, args.group_id,
-    #  c.distributed["backend"], c.distributed["url"])
-
-    # setup models
-    model_gen = setup_generator(c)
-    model_disc = setup_discriminator(c)
-
-    # setup optimizers
-    optimizer_gen = RAdam(model_gen.parameters(), lr=c.lr_gen, weight_decay=0)
-    optimizer_disc = RAdam(model_disc.parameters(),
-                           lr=c.lr_disc,
-                           weight_decay=0)
-
-    # schedulers
-    scheduler_gen = None
-    scheduler_disc = None
-    if 'lr_scheduler_gen' in c:
-        scheduler_gen = getattr(torch.optim.lr_scheduler, c.lr_scheduler_gen)
-        scheduler_gen = scheduler_gen(optimizer_gen, **c.lr_scheduler_gen_params)
-    if 'lr_scheduler_disc' in c:
-        scheduler_disc = getattr(torch.optim.lr_scheduler, c.lr_scheduler_disc)
-        scheduler_disc = scheduler_disc(optimizer_disc, **c.lr_scheduler_disc_params)
-
-    # setup criterion
-    criterion_gen = GeneratorLoss(c)
-    criterion_disc = DiscriminatorLoss(c)
-
-    if args.restore_path:
-        checkpoint = torch.load(args.restore_path, map_location='cpu')
-        try:
-            print(" > Restoring Generator Model...")
-            model_gen.load_state_dict(checkpoint['model'])
-            print(" > Restoring Generator Optimizer...")
-            optimizer_gen.load_state_dict(checkpoint['optimizer'])
-            print(" > Restoring Discriminator Model...")
-            model_disc.load_state_dict(checkpoint['model_disc'])
-            print(" > Restoring Discriminator Optimizer...")
-            optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
-            if 'scheduler' in checkpoint:
-                print(" > Restoring Generator LR Scheduler...")
-                scheduler_gen.load_state_dict(checkpoint['scheduler'])
-                # NOTE: Not sure if necessary
-                scheduler_gen.optimizer = optimizer_gen
-            if 'scheduler_disc' in checkpoint:
-                print(" > Restoring Discriminator LR Scheduler...")
-                scheduler_disc.load_state_dict(checkpoint['scheduler_disc'])
-                scheduler_disc.optimizer = optimizer_disc
-        except RuntimeError:
-            # retore only matching layers.
-            print(" > Partial model initialization...")
-            model_dict = model_gen.state_dict()
-            model_dict = set_init_dict(model_dict, checkpoint['model'], c)
-            model_gen.load_state_dict(model_dict)
-
-            model_dict = model_disc.state_dict()
-            model_dict = set_init_dict(model_dict, checkpoint['model_disc'], c)
-            model_disc.load_state_dict(model_dict)
-            del model_dict
-
-        # reset lr if not countinuining training.
-        for group in optimizer_gen.param_groups:
-            group['lr'] = c.lr_gen
-
-        for group in optimizer_disc.param_groups:
-            group['lr'] = c.lr_disc
-
-        print(" > Model restored from step %d" % checkpoint['step'],
-              flush=True)
-        args.restore_step = checkpoint['step']
-    else:
-        args.restore_step = 0
-
-    if use_cuda:
-        model_gen.cuda()
-        criterion_gen.cuda()
-        model_disc.cuda()
-        criterion_disc.cuda()
-
-    # DISTRUBUTED
-    # if num_gpus > 1:
-    #     model = apply_gradient_allreduce(model)
-
-    num_params = count_parameters(model_gen)
-    print(" > Generator has {} parameters".format(num_params), flush=True)
-    num_params = count_parameters(model_disc)
-    print(" > Discriminator has {} parameters".format(num_params), flush=True)
-
-    if 'best_loss' not in locals():
-        best_loss = float('inf')
-
-    global_step = args.restore_step
-    for epoch in range(0, c.epochs):
-        c_logger.print_epoch_start(epoch, c.epochs)
-        _, global_step = train(model_gen, criterion_gen, optimizer_gen,
-                               model_disc, criterion_disc, optimizer_disc,
-                               scheduler_gen, scheduler_disc, ap, global_step,
-                               epoch)
-        eval_avg_loss_dict = evaluate(model_gen, criterion_gen, model_disc, criterion_disc, ap,
-                                      global_step, epoch)
-        c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
-        target_loss = eval_avg_loss_dict[c.target_loss]
-        best_loss = save_best_model(target_loss,
-                                    best_loss,
-                                    model_gen,
-                                    optimizer_gen,
-                                    scheduler_gen,
-                                    model_disc,
-                                    optimizer_disc,
-                                    scheduler_disc,
-                                    global_step,
-                                    epoch,
-                                    OUT_PATH,
-                                    model_losses=eval_avg_loss_dict)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--continue_path',
-        type=str,
-        help=
-        'Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
-        default='',
-        required='--config_path' not in sys.argv)
-    parser.add_argument(
-        '--restore_path',
-        type=str,
-        help='Model file to be restored. Use to finetune a model.',
-        default='')
-    parser.add_argument('--config_path',
-                        type=str,
-                        help='Path to config file for training.',
-                        required='--continue_path' not in sys.argv)
-    parser.add_argument('--debug',
-                        type=bool,
-                        default=False,
-                        help='Do not verify commit integrity to run training.')
-
-    # DISTRUBUTED
-    parser.add_argument(
-        '--rank',
-        type=int,
-        default=0,
-        help='DISTRIBUTED: process rank for distributed training.')
-    parser.add_argument('--group_id',
-                        type=str,
-                        default="",
-                        help='DISTRIBUTED: process group id.')
-    args = parser.parse_args()
-
-    if args.continue_path != '':
-        args.output_path = args.continue_path
-        args.config_path = os.path.join(args.continue_path, 'config.json')
-        list_of_files = glob.glob(
-            args.continue_path +
-            "/*.pth.tar")  # * means all if need specific format then *.csv
-        latest_model_file = max(list_of_files, key=os.path.getctime)
-        args.restore_path = latest_model_file
-        print(f" > Training continues for {args.restore_path}")
-
-    # setup output paths and read configs
-    c = load_config(args.config_path)
-    check_config(c)
-    _ = os.path.dirname(os.path.realpath(__file__))
-
-    OUT_PATH = args.continue_path
-    if args.continue_path == '':
-        OUT_PATH = create_experiment_folder(c.output_path, c.run_name,
-                                            args.debug)
-
-    AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios')
-
-    c_logger = ConsoleLogger()
-
-    if args.rank == 0:
-        os.makedirs(AUDIO_PATH, exist_ok=True)
-        new_fields = {}
-        if args.restore_path:
-            new_fields["restore_path"] = args.restore_path
-        new_fields["github_branch"] = get_git_branch()
-        copy_config_file(args.config_path,
-                         os.path.join(OUT_PATH, 'config.json'), new_fields)
-        os.chmod(AUDIO_PATH, 0o775)
-        os.chmod(OUT_PATH, 0o775)
-
-        LOG_DIR = OUT_PATH
-        tb_logger = TensorboardLogger(LOG_DIR, model_name='VOCODER')
-
-        # write model desc to tensorboard
-        tb_logger.tb_add_text('model-description', c['run_description'], 0)
-
-    try:
-        main(args)
-    except KeyboardInterrupt:
-        remove_experiment_folder(OUT_PATH)
-        try:
-            sys.exit(0)
-        except SystemExit:
-            os._exit(0)  # pylint: disable=protected-access
-    except Exception:  # pylint: disable=broad-except
-        remove_experiment_folder(OUT_PATH)
-        traceback.print_exc()
-        sys.exit(1)
diff --git a/vocoder/utils/__init__.py b/vocoder/utils/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
deleted file mode 100644
index 6af0b823..00000000
--- a/vocoder/utils/console_logger.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import datetime
-from TTS.utils.io import AttrDict
-
-
-tcolors = AttrDict({
-    'OKBLUE': '\033[94m',
-    'HEADER': '\033[95m',
-    'OKGREEN': '\033[92m',
-    'WARNING': '\033[93m',
-    'FAIL': '\033[91m',
-    'ENDC': '\033[0m',
-    'BOLD': '\033[1m',
-    'UNDERLINE': '\033[4m'
-})
-
-
-class ConsoleLogger():
-    # TODO: merge this with TTS ConsoleLogger
-    def __init__(self):
-        # use these to compare values between iterations
-        self.old_train_loss_dict = None
-        self.old_epoch_loss_dict = None
-        self.old_eval_loss_dict = None
-
-    # pylint: disable=no-self-use
-    def get_time(self):
-        now = datetime.datetime.now()
-        return now.strftime("%Y-%m-%d %H:%M:%S")
-
-    def print_epoch_start(self, epoch, max_epoch):
-        print("\n{}{} > EPOCH: {}/{}{}".format(tcolors.UNDERLINE, tcolors.BOLD,
-                                               epoch, max_epoch, tcolors.ENDC),
-              flush=True)
-
-    def print_train_start(self):
-        print(f"\n{tcolors.BOLD} > TRAINING ({self.get_time()}) {tcolors.ENDC}")
-
-    def print_train_step(self, batch_steps, step, global_step,
-                         step_time, loader_time, lrG, lrD,
-                         loss_dict, avg_loss_dict):
-        indent = "     | > "
-        print()
-        log_text = "{}   --> STEP: {}/{} -- GLOBAL_STEP: {}{}\n".format(
-            tcolors.BOLD, step, batch_steps, global_step, tcolors.ENDC)
-        for key, value in loss_dict.items():
-            # print the avg value if given
-            if f'avg_{key}' in avg_loss_dict.keys():
-                log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
-            else:
-                log_text += "{}{}: {:.5f} \n".format(indent, key, value)
-        log_text += f"{indent}step_time: {step_time:.2f}\n{indent}loader_time: {loader_time:.2f}\n{indent}lrG: {lrG}\n{indent}lrD: {lrD}"
-        print(log_text, flush=True)
-
-    # pylint: disable=unused-argument
-    def print_train_epoch_end(self, global_step, epoch, epoch_time,
-                              print_dict):
-        indent = "     | > "
-        log_text = f"\n{tcolors.BOLD}   --> TRAIN PERFORMACE -- EPOCH TIME: {epoch_time:.2f} sec -- GLOBAL_STEP: {global_step}{tcolors.ENDC}\n"
-        for key, value in print_dict.items():
-            log_text += "{}{}: {:.5f}\n".format(indent, key, value)
-        print(log_text, flush=True)
-
-    def print_eval_start(self):
-        print(f"{tcolors.BOLD} > EVALUATION {tcolors.ENDC}\n")
-
-    def print_eval_step(self, step, loss_dict, avg_loss_dict):
-        indent = "     | > "
-        print()
-        log_text = f"{tcolors.BOLD}   --> STEP: {step}{tcolors.ENDC}\n"
-        for key, value in loss_dict.items():
-            # print the avg value if given
-            if f'avg_{key}' in avg_loss_dict.keys():
-                log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
-            else:
-                log_text += "{}{}: {:.5f} \n".format(indent, key, value)
-        print(log_text, flush=True)
-
-    def print_epoch_end(self, epoch, avg_loss_dict):
-        indent = "     | > "
-        log_text = "  {}--> EVAL PERFORMANCE{}\n".format(
-            tcolors.BOLD, tcolors.ENDC)
-        for key, value in avg_loss_dict.items():
-            # print the avg value if given
-            color = ''
-            sign = '+'
-            diff = 0
-            if self.old_eval_loss_dict is not None and key in self.old_eval_loss_dict:
-                diff = value - self.old_eval_loss_dict[key]
-                if diff < 0:
-                    color = tcolors.OKGREEN
-                    sign = ''
-                elif diff > 0:
-                    color = tcolors.FAIL
-                    sign = '+'
-            log_text += "{}{}:{} {:.5f} {}({}{:.5f})\n".format(indent, key, color, value, tcolors.ENDC, sign, diff)
-        self.old_eval_loss_dict = avg_loss_dict
-        print(log_text, flush=True)
diff --git a/vocoder/utils/generic_utils.py b/vocoder/utils/generic_utils.py
deleted file mode 100644
index 031d299d..00000000
--- a/vocoder/utils/generic_utils.py
+++ /dev/null
@@ -1,149 +0,0 @@
-import re
-import importlib
-import numpy as np
-from matplotlib import pyplot as plt
-
-from TTS.utils.visual import plot_spectrogram
-
-
-def plot_results(y_hat, y, ap, global_step, name_prefix):
-    """ Plot vocoder model results """
-
-    # select an instance from batch
-    y_hat = y_hat[0].squeeze(0).detach().cpu().numpy()
-    y = y[0].squeeze(0).detach().cpu().numpy()
-
-    spec_fake = ap.melspectrogram(y_hat).T
-    spec_real = ap.melspectrogram(y).T
-    spec_diff = np.abs(spec_fake - spec_real)
-
-    # plot figure and save it
-    fig_wave = plt.figure()
-    plt.subplot(2, 1, 1)
-    plt.plot(y)
-    plt.title("groundtruth speech")
-    plt.subplot(2, 1, 2)
-    plt.plot(y_hat)
-    plt.title(f"generated speech @ {global_step} steps")
-    plt.tight_layout()
-    plt.close()
-
-    figures = {
-        name_prefix + "spectrogram/fake": plot_spectrogram(spec_fake),
-        name_prefix + "spectrogram/real": plot_spectrogram(spec_real),
-        name_prefix + "spectrogram/diff": plot_spectrogram(spec_diff),
-        name_prefix + "speech_comparison": fig_wave,
-    }
-    return figures
-
-
-def to_camel(text):
-    text = text.capitalize()
-    return re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), text)
-
-
-def setup_generator(c):
-    print(" > Generator Model: {}".format(c.generator_model))
-    MyModel = importlib.import_module('TTS.vocoder.models.' +
-                                      c.generator_model.lower())
-    MyModel = getattr(MyModel, to_camel(c.generator_model))
-    if c.generator_model in 'melgan_generator':
-        model = MyModel(
-            in_channels=c.audio['num_mels'],
-            out_channels=1,
-            proj_kernel=7,
-            base_channels=512,
-            upsample_factors=c.generator_model_params['upsample_factors'],
-            res_kernel=3,
-            num_res_blocks=c.generator_model_params['num_res_blocks'])
-    if c.generator_model in 'melgan_fb_generator':
-        pass
-    if c.generator_model in 'multiband_melgan_generator':
-        model = MyModel(
-            in_channels=c.audio['num_mels'],
-            out_channels=4,
-            proj_kernel=7,
-            base_channels=384,
-            upsample_factors=c.generator_model_params['upsample_factors'],
-            res_kernel=3,
-            num_res_blocks=c.generator_model_params['num_res_blocks'])
-    if c.generator_model in 'parallel_wavegan_generator':
-        model = MyModel(
-           in_channels=1,
-            out_channels=1,
-            kernel_size=3,
-            num_res_blocks=c.generator_model_params['num_res_blocks'],
-            stacks=c.generator_model_params['stacks'],
-            res_channels=64,
-            gate_channels=128,
-            skip_channels=64,
-            aux_channels=c.audio['num_mels'],
-            aux_context_window=c['conv_pad'],
-            dropout=0.0,
-            bias=True,
-            use_weight_norm=True,
-            upsample_conditional_features=True,
-            upsample_factors=c.generator_model_params['upsample_factors'])
-    return model
-
-
-def setup_discriminator(c):
-    print(" > Discriminator Model: {}".format(c.discriminator_model))
-    if 'parallel_wavegan' in c.discriminator_model:
-        MyModel = importlib.import_module('TTS.vocoder.models.parallel_wavegan_discriminator')
-    else:
-        MyModel = importlib.import_module('TTS.vocoder.models.' +
-                                        c.discriminator_model.lower())
-    MyModel = getattr(MyModel, to_camel(c.discriminator_model.lower()))
-    if c.discriminator_model in 'random_window_discriminator':
-        model = MyModel(
-            cond_channels=c.audio['num_mels'],
-            hop_length=c.audio['hop_length'],
-            uncond_disc_donwsample_factors=c.
-            discriminator_model_params['uncond_disc_donwsample_factors'],
-            cond_disc_downsample_factors=c.
-            discriminator_model_params['cond_disc_downsample_factors'],
-            cond_disc_out_channels=c.
-            discriminator_model_params['cond_disc_out_channels'],
-            window_sizes=c.discriminator_model_params['window_sizes'])
-    if c.discriminator_model in 'melgan_multiscale_discriminator':
-        model = MyModel(
-            in_channels=1,
-            out_channels=1,
-            kernel_sizes=(5, 3),
-            base_channels=c.discriminator_model_params['base_channels'],
-            max_channels=c.discriminator_model_params['max_channels'],
-            downsample_factors=c.
-            discriminator_model_params['downsample_factors'])
-    if c.discriminator_model == 'residual_parallel_wavegan_discriminator':
-        model = MyModel(
-            in_channels=1,
-            out_channels=1,
-            kernel_size=3,
-            num_layers=c.discriminator_model_params['num_layers'],
-            stacks=c.discriminator_model_params['stacks'],
-            res_channels=64,
-            gate_channels=128,
-            skip_channels=64,
-            dropout=0.0,
-            bias=True,
-            nonlinear_activation="LeakyReLU",
-            nonlinear_activation_params={"negative_slope": 0.2},
-        )
-    if c.discriminator_model == 'parallel_wavegan_discriminator':
-        model = MyModel(
-            in_channels=1,
-            out_channels=1,
-            kernel_size=3,
-            num_layers=c.discriminator_model_params['num_layers'],
-            conv_channels=64,
-            dilation_factor=1,
-            nonlinear_activation="LeakyReLU",
-            nonlinear_activation_params={"negative_slope": 0.2},
-            bias=True
-        )
-    return model
-
-
-def check_config(c):
-    pass
\ No newline at end of file
diff --git a/vocoder/utils/io.py b/vocoder/utils/io.py
deleted file mode 100644
index 9d350238..00000000
--- a/vocoder/utils/io.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import os
-import torch
-import datetime
-
-
-def save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
-               scheduler_disc, current_step, epoch, output_path, **kwargs):
-    model_state = model.state_dict()
-    model_disc_state = model_disc.state_dict()\
-         if model_disc is not None else None
-    optimizer_state = optimizer.state_dict()\
-         if optimizer is not None else None
-    optimizer_disc_state = optimizer_disc.state_dict()\
-        if optimizer_disc is not None else None
-    scheduler_state = scheduler.state_dict()\
-        if scheduler is not None else None
-    scheduler_disc_state = scheduler_disc.state_dict()\
-        if scheduler_disc is not None else None
-    state = {
-        'model': model_state,
-        'optimizer': optimizer_state,
-        'scheduler': scheduler_state,
-        'model_disc': model_disc_state,
-        'optimizer_disc': optimizer_disc_state,
-        'scheduler_disc': scheduler_disc_state,
-        'step': current_step,
-        'epoch': epoch,
-        'date': datetime.date.today().strftime("%B %d, %Y"),
-    }
-    state.update(kwargs)
-    torch.save(state, output_path)
-
-
-def save_checkpoint(model, optimizer, scheduler, model_disc, optimizer_disc,
-                    scheduler_disc, current_step, epoch, output_folder,
-                    **kwargs):
-    file_name = 'checkpoint_{}.pth.tar'.format(current_step)
-    checkpoint_path = os.path.join(output_folder, file_name)
-    print(" > CHECKPOINT : {}".format(checkpoint_path))
-    save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
-               scheduler_disc, current_step, epoch, checkpoint_path, **kwargs)
-
-
-def save_best_model(target_loss, best_loss, model, optimizer, scheduler,
-                    model_disc, optimizer_disc, scheduler_disc, current_step,
-                    epoch, output_folder, **kwargs):
-    if target_loss < best_loss:
-        file_name = 'best_model.pth.tar'
-        checkpoint_path = os.path.join(output_folder, file_name)
-        print(" > BEST MODEL : {}".format(checkpoint_path))
-        save_model(model,
-                   optimizer,
-                   scheduler,
-                   model_disc,
-                   optimizer_disc,
-                   scheduler_disc,
-                   current_step,
-                   epoch,
-                   checkpoint_path,
-                   model_loss=target_loss,
-                   **kwargs)
-        best_loss = target_loss
-    return best_loss
\ No newline at end of file