From 8d877914700bb6c5c17571a67d02dd4d4b60c275 Mon Sep 17 00:00:00 2001
From: Eren Golge <egolge@mozilla.com>
Date: Wed, 16 Jan 2019 11:58:31 +0100
Subject: [PATCH] Remove TWEB

---
 datasets/TWEB.py | 144 -----------------------------------------------
 1 file changed, 144 deletions(-)
 delete mode 100644 datasets/TWEB.py

diff --git a/datasets/TWEB.py b/datasets/TWEB.py
deleted file mode 100644
index 50cdb448..00000000
--- a/datasets/TWEB.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import os
-import numpy as np
-import collections
-import librosa
-import torch
-from torch.utils.data import Dataset
-
-from TTS.utils.text import text_to_sequence
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.data import (prepare_data, pad_per_step, prepare_tensor,
-                            prepare_stop_target)
-
-
-class TWEBDataset(Dataset):
-    def __init__(self,
-                 csv_file,
-                 root_dir,
-                 outputs_per_step,
-                 sample_rate,
-                 text_cleaner,
-                 num_mels,
-                 min_level_db,
-                 frame_shift_ms,
-                 frame_length_ms,
-                 preemphasis,
-                 ref_level_db,
-                 num_freq,
-                 power,
-                 min_seq_len=0):
-
-        with open(csv_file, "r") as f:
-            self.frames = [line.split('\t') for line in f]
-        self.root_dir = root_dir
-        self.outputs_per_step = outputs_per_step
-        self.sample_rate = sample_rate
-        self.cleaners = text_cleaner
-        self.min_seq_len = min_seq_len
-        self.ap = AudioProcessor(sample_rate, num_mels, min_level_db,
-                                 frame_shift_ms, frame_length_ms, preemphasis,
-                                 ref_level_db, num_freq, power)
-        print(" > Reading TWEB from - {}".format(root_dir))
-        print(" | > Number of instances : {}".format(len(self.frames)))
-        self._sort_frames()
-
-    def load_wav(self, filename):
-        try:
-            audio = librosa.core.load(filename, sr=self.sample_rate)
-            return audio
-        except RuntimeError as e:
-            print(" !! Cannot read file : {}".format(filename))
-
-    def _sort_frames(self):
-        r"""Sort sequences in ascending order"""
-        lengths = np.array([len(ins[1]) for ins in self.frames])
-
-        print(" | > Max length sequence {}".format(np.max(lengths)))
-        print(" | > Min length sequence {}".format(np.min(lengths)))
-        print(" | > Avg length sequence {}".format(np.mean(lengths)))
-
-        idxs = np.argsort(lengths)
-        new_frames = []
-        ignored = []
-        for i, idx in enumerate(idxs):
-            length = lengths[idx]
-            if length < self.min_seq_len:
-                ignored.append(idx)
-            else:
-                new_frames.append(self.frames[idx])
-        print(" | > {} instances are ignored by min_seq_len ({})".format(
-            len(ignored), self.min_seq_len))
-        self.frames = new_frames
-
-    def __len__(self):
-        return len(self.frames)
-
-    def __getitem__(self, idx):
-        wav_name = os.path.join(self.root_dir, self.frames[idx][0]) + '.wav'
-        text = self.frames[idx][1]
-        text = np.asarray(
-            text_to_sequence(text, [self.cleaners]), dtype=np.int32)
-        wav = np.asarray(self.load_wav(wav_name)[0], dtype=np.float32)
-        sample = {'text': text, 'wav': wav, 'item_idx': self.frames[idx][0]}
-        return sample
-
-    def collate_fn(self, batch):
-        r"""
-            Perform preprocessing and create a final data batch:
-            1. PAD sequences with the longest sequence in the batch
-            2. Convert Audio signal to Spectrograms.
-            3. PAD sequences that can be divided by r.
-            4. Convert Numpy to Torch tensors.
-        """
-
-        # Puts each data field into a tensor with outer dimension batch size
-        if isinstance(batch[0], collections.Mapping):
-            keys = list()
-
-            wav = [d['wav'] for d in batch]
-            item_idxs = [d['item_idx'] for d in batch]
-            text = [d['text'] for d in batch]
-
-            text_lenghts = np.array([len(x) for x in text])
-            max_text_len = np.max(text_lenghts)
-
-            linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
-            mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
-            mel_lengths = [m.shape[1] + 1 for m in mel]  # +1 for zero-frame
-
-            # compute 'stop token' targets
-            stop_targets = [
-                np.array([0.] * (mel_len - 1)) for mel_len in mel_lengths
-            ]
-
-            # PAD stop targets
-            stop_targets = prepare_stop_target(stop_targets,
-                                               self.outputs_per_step)
-
-            # PAD sequences with largest length of the batch
-            text = prepare_data(text).astype(np.int32)
-            wav = prepare_data(wav)
-
-            # PAD features with largest length + a zero frame
-            linear = prepare_tensor(linear, self.outputs_per_step)
-            mel = prepare_tensor(mel, self.outputs_per_step)
-            assert mel.shape[2] == linear.shape[2]
-            timesteps = mel.shape[2]
-
-            # B x T x D
-            linear = linear.transpose(0, 2, 1)
-            mel = mel.transpose(0, 2, 1)
-
-            # convert things to pytorch
-            text_lenghts = torch.LongTensor(text_lenghts)
-            text = torch.LongTensor(text)
-            linear = torch.FloatTensor(linear)
-            mel = torch.FloatTensor(mel)
-            mel_lengths = torch.LongTensor(mel_lengths)
-            stop_targets = torch.FloatTensor(stop_targets)
-
-            return text, text_lenghts, linear, mel, mel_lengths, stop_targets, item_idxs[
-                0]
-
-        raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
-                         found {}".format(type(batch[0]))))