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nv_tacotron_dataset - Allow training on mozilla cv

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This commit is contained in:
James Betker 2021-08-11 13:34:31 -06:00
parent d0c74278bf
commit 2d3f0cc33c
1 changed files with 32 additions and 7 deletions
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39
codes/data/audio/nv_tacotron_dataset.py
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@ -1,5 +1,7 @@
import os import os
import random import random
import audio2numpy
import numpy as np import numpy as np
import torch import torch
import torch.utils.data import torch.utils.data
@ -12,6 +14,13 @@ from models.tacotron2.text import text_to_sequence
from utils.util import opt_get from utils.util import opt_get
def load_mozilla_cv(filename):
with open(filename, encoding='utf-8') as f:
components = [line.strip().split('\t') for line in f][1:] # First line is the header
filepaths_and_text = [[f'clips/{component[1]}', component[2]] for component in components]
return filepaths_and_text
class TextMelLoader(torch.utils.data.Dataset): class TextMelLoader(torch.utils.data.Dataset):
""" """
1) loads audio,text pairs 1) loads audio,text pairs
@ -20,7 +29,15 @@ class TextMelLoader(torch.utils.data.Dataset):
""" """
def __init__(self, hparams): def __init__(self, hparams):
self.path = os.path.dirname(hparams['path']) self.path = os.path.dirname(hparams['path'])
self.audiopaths_and_text = load_filepaths_and_text(hparams['path']) fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
fetcher_fn = None
if fetcher_mode == 'lj':
fetcher_fn = load_filepaths_and_text
elif fetcher_mode == 'mozilla_cv':
fetcher_fn = load_mozilla_cv
else:
raise NotImplementedError()
self.audiopaths_and_text = fetcher_fn(hparams['path'])
self.text_cleaners = hparams.text_cleaners self.text_cleaners = hparams.text_cleaners
self.max_wav_value = hparams.max_wav_value self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate self.sampling_rate = hparams.sampling_rate
@ -45,11 +62,18 @@ class TextMelLoader(torch.utils.data.Dataset):
def get_mel(self, filename): def get_mel(self, filename):
if not self.load_mel_from_disk: if not self.load_mel_from_disk:
audio, sampling_rate = load_wav_to_torch(filename) if filename.endswith('.wav'):
audio, sampling_rate = load_wav_to_torch(filename)
audio = audio / self.max_wav_value
else:
audio, sampling_rate = audio2numpy.audio_from_file(filename)
audio = torch.tensor(audio)
if sampling_rate != self.input_sample_rate: if sampling_rate != self.input_sample_rate:
raise ValueError(f"Input sampling rate does not match specified rate {self.input_sample_rate}") assert sampling_rate > self.input_sample_rate # Upsampling is not a great idea.
audio_norm = audio / self.max_wav_value audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.input_sample_rate/sampling_rate, mode='area')
audio_norm = audio_norm.unsqueeze(0) audio = (audio.squeeze().clip(-1,1)+1)/2
audio_norm = audio.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False) audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
if self.input_sample_rate != self.sampling_rate: if self.input_sample_rate != self.sampling_rate:
ratio = self.sampling_rate / self.input_sample_rate ratio = self.sampling_rate / self.input_sample_rate
@ -137,10 +161,11 @@ class TextMelCollate():
if __name__ == '__main__': if __name__ == '__main__':
params = { params = {
'mode': 'nv_tacotron', 'mode': 'nv_tacotron',
'path': 'E:\\audio\\LJSpeech-1.1\\ljs_audio_text_train_filelist.txt', 'path': 'E:\\audio\\MozillaCommonVoice\\en\\test.tsv',
'phase': 'train', 'phase': 'train',
'n_workers': 1, 'n_workers': 0,
'batch_size': 32, 'batch_size': 32,
'fetcher_mode': 'mozilla_cv',
#'return_wavs': True, #'return_wavs': True,
#'input_sample_rate': 22050, #'input_sample_rate': 22050,
#'sampling_rate': 8000 #'sampling_rate': 8000