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Dont require collation for nv_tacotron

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This commit is contained in:
James Betker 2021-08-12 15:44:55 -06:00
parent 5b07d3b623
commit 4c76257c71
2 changed files with 24 additions and 6 deletions
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3
codes/data/__init__.py
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@ -69,7 +69,8 @@ def create_dataset(dataset_opt, return_collate=False):
default_params = create_hparams() default_params = create_hparams()
default_params.update(dataset_opt) default_params.update(dataset_opt)
dataset_opt = munchify(default_params) dataset_opt = munchify(default_params)
collate = C(dataset_opt.n_frames_per_step) if opt_get(dataset_opt, ['needs_collate'], True):
collate = C(dataset_opt.n_frames_per_step)
elif mode == 'gpt_tts': elif mode == 'gpt_tts':
from data.audio.gpt_tts_dataset import GptTtsDataset as D from data.audio.gpt_tts_dataset import GptTtsDataset as D
from data.audio.gpt_tts_dataset import GptTtsCollater as C from data.audio.gpt_tts_dataset import GptTtsCollater as C

27
codes/data/audio/nv_tacotron_dataset.py
View File

@ -5,6 +5,7 @@ import audio2numpy
import numpy as np import numpy as np
import torch import torch
import torch.utils.data import torch.utils.data
import torch.nn.functional as F
from tqdm import tqdm from tqdm import tqdm
import models.tacotron2.layers as layers import models.tacotron2.layers as layers
@ -40,7 +41,6 @@ class TextMelLoader(torch.utils.data.Dataset):
self.audiopaths_and_text = fetcher_fn(hparams['path']) 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.max_mel_len = opt_get(hparams, ['max_mel_length'], None)
self.sampling_rate = hparams.sampling_rate self.sampling_rate = hparams.sampling_rate
self.load_mel_from_disk = hparams.load_mel_from_disk self.load_mel_from_disk = hparams.load_mel_from_disk
self.return_wavs = opt_get(hparams, ['return_wavs'], False) self.return_wavs = opt_get(hparams, ['return_wavs'], False)
@ -52,6 +52,12 @@ class TextMelLoader(torch.utils.data.Dataset):
hparams.mel_fmax) hparams.mel_fmax)
random.seed(hparams.seed) random.seed(hparams.seed)
random.shuffle(self.audiopaths_and_text) random.shuffle(self.audiopaths_and_text)
self.max_mel_len = opt_get(hparams, ['max_mel_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
def get_mel_text_pair(self, audiopath_and_text): def get_mel_text_pair(self, audiopath_and_text):
# separate filename and text # separate filename and text
@ -88,8 +94,8 @@ class TextMelLoader(torch.utils.data.Dataset):
else: else:
melspec = torch.from_numpy(np.load(filename)) melspec = torch.from_numpy(np.load(filename))
assert melspec.size(0) == self.stft.n_mel_channels, ( assert melspec.size(0) == self.stft.n_mel_channels, (
'Mel dimension mismatch: given {}, expected {}'.format( 'Mel dimension mismatch: given {}, expected {}'.format(melspec.size(0), self.stft.n_mel_channels))
melspec.size(0), self.stft.n_mel_channels))
return melspec return melspec
@ -99,11 +105,18 @@ class TextMelLoader(torch.utils.data.Dataset):
def __getitem__(self, index): def __getitem__(self, index):
t, m, p = self.get_mel_text_pair(self.audiopaths_and_text[index]) t, m, p = self.get_mel_text_pair(self.audiopaths_and_text[index])
if self.max_mel_len is not None and m.shape[-1] > self.max_mel_len: mel_oversize = self.max_mel_len is not None and m.shape[-1] > self.max_mel_len
print(f"Exception {index} mel_len:{m.shape[-1]} fname: {p}") text_oversize = self.max_text_len is not None and t.shape[0] > self.max_text_len
if mel_oversize or text_oversize:
print(f"Exception {index} mel_len:{m.shape[-1]} text_len:{t.shape[0]} fname: {p}")
# 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. # 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.
rv = random.randint(0,len(self)-1) rv = random.randint(0,len(self)-1)
return self[rv] return self[rv]
if not self.needs_collate:
if m.shape[-1] != self.max_mel_len:
m = F.pad(m, (0, self.max_mel_len - m.shape[-1]))
if t.shape[0] != self.max_text_len:
t = F.pad(t, (0, self.max_text_len - t.shape[0]))
return t, m, p return t, m, p
def __len__(self): def __len__(self):
@ -174,6 +187,9 @@ if __name__ == '__main__':
'n_workers': 0, 'n_workers': 0,
'batch_size': 32, 'batch_size': 32,
'fetcher_mode': 'mozilla_cv', 'fetcher_mode': 'mozilla_cv',
'needs_collate': False,
'max_mel_length': 800,
'max_text_length': 200,
#'return_wavs': True, #'return_wavs': True,
#'input_sample_rate': 22050, #'input_sample_rate': 22050,
#'sampling_rate': 8000 #'sampling_rate': 8000
@ -185,6 +201,7 @@ if __name__ == '__main__':
i = 0 i = 0
m = None m = None
for i, b in tqdm(enumerate(dl)): for i, b in tqdm(enumerate(dl)):
continue
pm = b['padded_mel'] pm = b['padded_mel']
pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1])) pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1]))
m = pm if m is None else torch.cat([m, pm], dim=0) m = pm if m is None else torch.cat([m, pm], dim=0)