mirror of https://github.com/coqui-ai/TTS.git
93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
import pandas as pd
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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_data, pad_per_step
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class LJSpeechDataset(Dataset):
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def __init__(self, csv_file, root_dir, outputs_per_step, sample_rate,
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text_cleaner, num_mels, min_level_db, frame_shift_ms,
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frame_length_ms, preemphasis, ref_level_db, num_freq, power):
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self.frames = pd.read_csv(csv_file, sep='|', header=None)
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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.ap = AudioProcessor(sample_rate, num_mels, min_level_db, frame_shift_ms,
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frame_length_ms, preemphasis, ref_level_db, num_freq, power
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)
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print(" > Reading LJSpeech from - {}".format(root_dir))
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print(" | > Number of instances : {}".format(len(self.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 __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,
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self.frames.ix[idx, 0]) + '.wav'
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text = self.frames.ix[idx, 1]
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text = np.asarray(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.ix[idx, 0]}
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return sample
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def get_dummy_data(self):
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return torch.autograd.Variable(torch.ones(16, 143)).type(torch.LongTensor)
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def collate_fn(self, batch):
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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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# 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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linear = np.array([self.ap.spectrogram(w).astype('float32') for w in wav])
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mel = np.array([self.ap.melspectrogram(w).astype('float32') for w in wav])
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assert mel.shape[2] == linear.shape[2]
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timesteps = mel.shape[2]
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# PAD with zeros that can be divided by outputs per step
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if timesteps % self.outputs_per_step != 0:
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linear = pad_per_step(linear, self.outputs_per_step)
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mel = pad_per_step(mel, self.outputs_per_step)
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# reshape jombo
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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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return text, text_lenghts, linear, mel, item_idxs[0]
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raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
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found {}"
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.format(type(batch[0]))))
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