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Decouple MEL from nv_tacotron_dataset

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This commit is contained in:
James Betker 2021-10-31 15:01:38 -06:00
parent b8b268b5f6
commit f7d0901ce6
2 changed files with 45 additions and 139 deletions
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4
codes/data/__init__.py
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@ -61,14 +61,14 @@ def create_dataset(dataset_opt, return_collate=False):
elif mode == 'zipfile':
from data.zip_file_dataset import ZipFileDataset as D
elif mode == 'nv_tacotron':
from data.audio.nv_tacotron_dataset import TextMelLoader as D
from data.audio.nv_tacotron_dataset import TextWavLoader as D
from data.audio.nv_tacotron_dataset import TextMelCollate as C
from models.tacotron2.hparams import create_hparams
default_params = create_hparams()
default_params.update(dataset_opt)
dataset_opt = munchify(default_params)
if opt_get(dataset_opt, ['needs_collate'], True):
collate = C(dataset_opt.n_frames_per_step)
collate = C()
elif mode == 'gpt_tts':
from data.audio.gpt_tts_dataset import GptTtsDataset as D
from data.audio.gpt_tts_dataset import GptTtsCollater as C

180
codes/data/audio/nv_tacotron_dataset.py
View File

@ -6,9 +6,11 @@ import numpy as np
import torch
import torch.utils.data
import torch.nn.functional as F
import torchaudio
from tqdm import tqdm
import models.tacotron2.layers as layers
from data.audio.unsupervised_audio_dataset import load_audio
from models.tacotron2.taco_utils import load_wav_to_torch, load_filepaths_and_text
from models.tacotron2.text import text_to_sequence
@ -37,12 +39,7 @@ def load_voxpopuli(filename):
return filepaths_and_text
class TextMelLoader(torch.utils.data.Dataset):
"""
1) loads audio,text pairs
2) normalizes text and converts them to sequences of one-hot vectors
3) computes mel-spectrograms from audio files.
"""
class TextWavLoader(torch.utils.data.Dataset):
def __init__(self, hparams):
self.path = hparams['path']
if not isinstance(self.path, list):
@ -65,117 +62,67 @@ class TextMelLoader(torch.utils.data.Dataset):
raise NotImplementedError()
self.audiopaths_and_text.extend(fetcher_fn(p))
self.text_cleaners = hparams.text_cleaners
self.sampling_rate = hparams.sampling_rate
self.load_mel_from_disk = opt_get(hparams, ['load_mel_from_disk'], False)
self.return_wavs = opt_get(hparams, ['return_wavs'], False)
self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate)
assert not (self.load_mel_from_disk and self.return_wavs)
self.stft = layers.TacotronSTFT(
hparams.filter_length, hparams.hop_length, hparams.win_length,
hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
hparams.mel_fmax)
self.sample_rate = hparams.sample_rate
random.seed(hparams.seed)
random.shuffle(self.audiopaths_and_text)
self.max_mel_len = opt_get(hparams, ['max_mel_length'], None)
self.max_wav_len = opt_get(hparams, ['max_wav_length'], None)
self.max_text_len = opt_get(hparams, ['max_text_length'], None)
# If needs_collate=False, all outputs will be aligned and padded at maximum length.
self.needs_collate = opt_get(hparams, ['needs_collate'], True)
if not self.needs_collate:
assert self.max_mel_len is not None and self.max_text_len is not None
assert self.max_wav_len is not None and self.max_text_len is not None
def get_mel_text_pair(self, audiopath_and_text):
def get_wav_text_pair(self, audiopath_and_text):
# separate filename and text
audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
text_seq = self.get_text(text)
mel = self.get_mel(audiopath)
return (text_seq, mel, text, audiopath_and_text[0])
def get_mel(self, filename):
if self.load_mel_from_disk and os.path.exists(f'{filename}_mel.npy'):
melspec = torch.from_numpy(np.load(f'{filename}_mel.npy'))
assert melspec.size(0) == self.stft.n_mel_channels, (
'Mel dimension mismatch: given {}, expected {}'.format(melspec.size(0), self.stft.n_mel_channels))
else:
if filename.endswith('.wav'):
audio, sampling_rate = load_wav_to_torch(filename)
elif filename.endswith('.mp3'):
# https://github.com/neonbjb/pyfastmp3decoder - Definitely worth it.
from pyfastmp3decoder.mp3decoder import load_mp3
audio, sampling_rate = load_mp3(filename, self.input_sample_rate)
audio = torch.FloatTensor(audio)
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:
print(f'{filename} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {self.input_sample_rate}. This is not a good idea.')
audio_norm = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.input_sample_rate/sampling_rate, mode='nearest', recompute_scale_factor=False).squeeze()
else:
audio_norm = audio
if audio_norm.std() > 1:
print(f"Something is very wrong with the given audio. std_dev={audio_norm.std()}. file={filename}")
return None
audio_norm.clip_(-1, 1)
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
if self.input_sample_rate != self.sampling_rate:
ratio = self.sampling_rate / self.input_sample_rate
audio_norm = torch.nn.functional.interpolate(audio_norm.unsqueeze(0), scale_factor=ratio, mode='area').squeeze(0)
if self.return_wavs:
melspec = audio_norm
else:
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
return melspec
wav = load_audio(audiopath, self.sample_rate)
return (text_seq, wav, text, audiopath_and_text[0])
def get_text(self, text):
text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
return text_norm
def __getitem__(self, index):
tseq, mel, text, path = self.get_mel_text_pair(self.audiopaths_and_text[index])
if mel is None or \
(self.max_mel_len is not None and mel.shape[-1] > self.max_mel_len) or \
tseq, wav, text, path = self.get_wav_text_pair(self.audiopaths_and_text[index])
if wav is None or \
(self.max_wav_len is not None and wav.shape[-1] > self.max_wav_len) or \
(self.max_text_len is not None and tseq.shape[0] > self.max_text_len):
#if mel is not None:
# print(f"Exception {index} mel_len:{mel.shape[-1]} text_len:{tseq.shape[0]} fname: {path}")
# Basically, this audio file is nonexistent or too long to be supported by the dataset.
# It's hard to handle this situation properly. Best bet is to return the a random valid token and skew the dataset somewhat as a result.
#if wav is not None:
# print(f"Exception {index} wav_len:{wav.shape[-1]} text_len:{tseq.shape[0]} fname: {path}")
rv = random.randint(0,len(self)-1)
return self[rv]
orig_output = mel.shape[-1]
orig_output = wav.shape[-1]
orig_text_len = tseq.shape[0]
if not self.needs_collate:
if mel.shape[-1] != self.max_mel_len:
mel = F.pad(mel, (0, self.max_mel_len - mel.shape[-1]))
if wav.shape[-1] != self.max_wav_len:
wav = F.pad(wav, (0, self.max_wav_len - wav.shape[-1]))
if tseq.shape[0] != self.max_text_len:
tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0]))
return {
'real_text': text,
'padded_text': tseq,
'input_lengths': torch.tensor(orig_text_len, dtype=torch.long),
'padded_mel': mel,
'wav': wav,
'output_lengths': torch.tensor(orig_output, dtype=torch.long),
'filenames': path
}
return tseq, mel, path, text
return tseq, wav, path, text
def __len__(self):
return len(self.audiopaths_and_text)
class TextMelCollate():
""" Zero-pads model inputs and targets based on number of frames per setep
""" Zero-pads model inputs and targets based on number of frames per step
"""
def __init__(self, n_frames_per_step):
self.n_frames_per_step = n_frames_per_step
def __call__(self, batch):
"""Collate's training batch from normalized text and mel-spectrogram
"""Collate's training batch from normalized text and wav
PARAMS
------
batch: [text_normalized, mel_normalized, filename]
batch: [text_normalized, wav, filename, text]
"""
# Right zero-pad all one-hot text sequences to max input length
input_lengths, ids_sorted_decreasing = torch.sort(
@ -193,81 +140,42 @@ class TextMelCollate():
filenames.append(batch[ids_sorted_decreasing[i]][2])
real_text.append(batch[ids_sorted_decreasing[i]][3])
# Right zero-pad mel-spec
num_mels = batch[0][1].size(0)
# Right zero-pad wav
num_wavs = batch[0][1].size(0)
max_target_len = max([x[1].size(1) for x in batch])
if max_target_len % self.n_frames_per_step != 0:
max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
assert max_target_len % self.n_frames_per_step == 0
# include mel padded and gate padded
mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
mel_padded.zero_()
gate_padded = torch.FloatTensor(len(batch), max_target_len)
gate_padded.zero_()
wav_padded = torch.FloatTensor(len(batch), num_wavs, max_target_len)
wav_padded.zero_()
output_lengths = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
mel = batch[ids_sorted_decreasing[i]][1]
mel_padded[i, :, :mel.size(1)] = mel
gate_padded[i, mel.size(1)-1:] = 1
output_lengths[i] = mel.size(1)
wav = batch[ids_sorted_decreasing[i]][1]
wav_padded[i, :, :wav.size(1)] = wav
output_lengths[i] = wav.size(1)
return {
'padded_text': text_padded,
'input_lengths': input_lengths,
'padded_mel': mel_padded,
'padded_gate': gate_padded,
'wav': wav_padded,
'output_lengths': output_lengths,
'filenames': filenames,
'real_text': real_text,
}
def save_mel_buffer_to_file(mel, path):
np.save(path, mel.cpu().numpy())
def dump_mels_to_disk():
params = {
'mode': 'nv_tacotron',
'path': ['Z:\\mozcv\\en\\train.tsv'],
'fetcher_mode': ['mozilla_cv'],
'phase': 'train',
'n_workers': 8,
'batch_size': 1,
'needs_collate': True,
'max_mel_length': 10000,
'max_text_length': 1000,
#'return_wavs': True,
#'input_sample_rate': 22050,
#'sampling_rate': 8000
}
from data import create_dataset, create_dataloader
ds, c = create_dataset(params, return_collate=True)
dl = create_dataloader(ds, params, collate_fn=c)
for b in tqdm(dl):
mels = b['padded_mel']
fnames = b['filenames']
for j, fname in enumerate(fnames):
save_mel_buffer_to_file(mels[j], f'{fname}_mel.npy')
if __name__ == '__main__':
dump_mels_to_disk()
'''
batch_sz = 32
params = {
'mode': 'nv_tacotron',
'path': 'E:\\audio\\MozillaCommonVoice\\en\\train.tsv',
'path': 'E:\\audio\\MozillaCommonVoice\\en\\test.tsv',
'phase': 'train',
'n_workers': 12,
'batch_size': 32,
'n_workers': 0,
'batch_size': batch_sz,
'fetcher_mode': 'mozilla_cv',
'needs_collate': False,
'max_mel_length': 800,
'max_text_length': 200,
#'return_wavs': True,
#'input_sample_rate': 22050,
#'sampling_rate': 8000
'needs_collate': True,
#'max_wav_length': 256000,
#'max_text_length': 200,
'sample_rate': 22050,
}
from data import create_dataset, create_dataloader
@ -277,9 +185,7 @@ if __name__ == '__main__':
m = None
for k in range(1000):
for i, b in tqdm(enumerate(dl)):
continue
pm = b['padded_mel']
pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1]))
m = pm if m is None else torch.cat([m, pm], dim=0)
print(m.mean(), m.std())
'''
w = b['wav']
for ib in range(batch_sz):
print(f'{i} {ib} {b["real_text"][ib]}')
torchaudio.save(f'{i}_clip_{ib}.wav', b['wav'][ib], ds.sample_rate)