mirror of https://github.com/coqui-ai/TTS.git
Remove TWEB
This commit is contained in:
Eren Golge
parent
b78fc96115
commit
8d87791470
144
datasets/TWEB.py
144
datasets/TWEB.py
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@ -1,144 +0,0 @@
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import os
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import numpy as np
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import collections
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import librosa
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import torch
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from torch.utils.data import Dataset
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from TTS.utils.text import text_to_sequence
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.data import (prepare_data, pad_per_step, prepare_tensor,
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prepare_stop_target)
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class TWEBDataset(Dataset):
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def __init__(self,
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csv_file,
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root_dir,
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outputs_per_step,
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sample_rate,
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text_cleaner,
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num_mels,
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min_level_db,
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frame_shift_ms,
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frame_length_ms,
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preemphasis,
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ref_level_db,
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num_freq,
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power,
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min_seq_len=0):
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with open(csv_file, "r") as f:
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self.frames = [line.split('\t') for line in f]
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self.root_dir = root_dir
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self.outputs_per_step = outputs_per_step
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self.sample_rate = sample_rate
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self.cleaners = text_cleaner
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self.min_seq_len = min_seq_len
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self.ap = AudioProcessor(sample_rate, num_mels, min_level_db,
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frame_shift_ms, frame_length_ms, preemphasis,
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ref_level_db, num_freq, power)
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print(" > Reading TWEB from - {}".format(root_dir))
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print(" | > Number of instances : {}".format(len(self.frames)))
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self._sort_frames()
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def load_wav(self, filename):
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try:
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audio = librosa.core.load(filename, sr=self.sample_rate)
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return audio
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except RuntimeError as e:
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print(" !! Cannot read file : {}".format(filename))
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def _sort_frames(self):
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r"""Sort sequences in ascending order"""
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lengths = np.array([len(ins[1]) for ins in self.frames])
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print(" | > Max length sequence {}".format(np.max(lengths)))
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print(" | > Min length sequence {}".format(np.min(lengths)))
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print(" | > Avg length sequence {}".format(np.mean(lengths)))
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idxs = np.argsort(lengths)
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new_frames = []
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ignored = []
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for i, idx in enumerate(idxs):
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length = lengths[idx]
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if length < self.min_seq_len:
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ignored.append(idx)
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else:
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new_frames.append(self.frames[idx])
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print(" | > {} instances are ignored by min_seq_len ({})".format(
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len(ignored), self.min_seq_len))
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self.frames = new_frames
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def __len__(self):
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return len(self.frames)
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def __getitem__(self, idx):
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wav_name = os.path.join(self.root_dir, self.frames[idx][0]) + '.wav'
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text = self.frames[idx][1]
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text = np.asarray(
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text_to_sequence(text, [self.cleaners]), dtype=np.int32)
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wav = np.asarray(self.load_wav(wav_name)[0], dtype=np.float32)
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sample = {'text': text, 'wav': wav, 'item_idx': self.frames[idx][0]}
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return sample
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def collate_fn(self, batch):
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r"""
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Perform preprocessing and create a final data batch:
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1. PAD sequences with the longest sequence in the batch
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2. Convert Audio signal to Spectrograms.
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3. PAD sequences that can be divided by r.
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4. Convert Numpy to Torch tensors.
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"""
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# Puts each data field into a tensor with outer dimension batch size
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if isinstance(batch[0], collections.Mapping):
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keys = list()
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wav = [d['wav'] for d in batch]
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item_idxs = [d['item_idx'] for d in batch]
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text = [d['text'] for d in batch]
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text_lenghts = np.array([len(x) for x in text])
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max_text_len = np.max(text_lenghts)
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linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
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mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
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mel_lengths = [m.shape[1] + 1 for m in mel] # +1 for zero-frame
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# compute 'stop token' targets
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stop_targets = [
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np.array([0.] * (mel_len - 1)) for mel_len in mel_lengths
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]
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# PAD stop targets
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stop_targets = prepare_stop_target(stop_targets,
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self.outputs_per_step)
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# PAD sequences with largest length of the batch
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text = prepare_data(text).astype(np.int32)
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wav = prepare_data(wav)
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# PAD features with largest length + a zero frame
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linear = prepare_tensor(linear, self.outputs_per_step)
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mel = prepare_tensor(mel, self.outputs_per_step)
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assert mel.shape[2] == linear.shape[2]
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timesteps = mel.shape[2]
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# B x T x D
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linear = linear.transpose(0, 2, 1)
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mel = mel.transpose(0, 2, 1)
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# convert things to pytorch
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text_lenghts = torch.LongTensor(text_lenghts)
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text = torch.LongTensor(text)
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linear = torch.FloatTensor(linear)
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mel = torch.FloatTensor(mel)
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mel_lengths = torch.LongTensor(mel_lengths)
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stop_targets = torch.FloatTensor(stop_targets)
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return text, text_lenghts, linear, mel, mel_lengths, stop_targets, item_idxs[
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0]
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raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
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found {}".format(type(batch[0]))))
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