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Auto-normalize wav files by data type

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This commit is contained in:
James Betker 2021-08-15 09:09:51 -06:00
parent 98057b6516
commit a523c4f932
5 changed files with 28 additions and 20 deletions
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4
codes/data/audio/nv_tacotron_dataset.py
View File

@ -49,7 +49,6 @@ class TextMelLoader(torch.utils.data.Dataset):
raise NotImplementedError()
self.audiopaths_and_text.extend(fetcher_fn(p))
self.text_cleaners = hparams.text_cleaners
self.max_wav_value = hparams.max_wav_value
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)
@ -83,7 +82,6 @@ class TextMelLoader(torch.utils.data.Dataset):
else:
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)
@ -108,8 +106,6 @@ class TextMelLoader(torch.utils.data.Dataset):
else:
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
else:
return melspec

30
codes/data/audio/wavfile_dataset.py
View File

@ -15,23 +15,22 @@ from utils.util import opt_get
class WavfileDataset(torch.utils.data.Dataset):
def __init__(self, opt):
cache_path = opt_get(opt, ['cache_path'], os.path.join(self.path, 'cache.pth')) # Will fail when multiple paths specified, must be specified in this case.
self.path = os.path.dirname(opt['path'])
if not isinstance(self.path, list):
self.path = [self.path]
path = opt['path']
cache_path = opt['cache_path'] # Will fail when multiple paths specified, must be specified in this case.
if not isinstance(path, list):
path = [path]
if os.path.exists(cache_path):
self.audiopaths = torch.load(cache_path)
else:
print("Building cache..")
self.audiopaths = []
for p in self.path:
for p in path:
self.audiopaths.extend(find_files_of_type('img', p, qualifier=is_wav_file)[0])
torch.save(self.audiopaths, cache_path)
# Parse options
self.sampling_rate = opt_get(opt, ['sampling_rate'], 24000)
self.augment = opt_get(opt, ['do_augmentation'], False)
self.max_wav_value = 32768.0
self.window = 2 * self.sampling_rate
if self.augment:
@ -39,13 +38,20 @@ class WavfileDataset(torch.utils.data.Dataset):
def get_audio_for_index(self, index):
audiopath = self.audiopaths[index]
filename = os.path.join(self.path, audiopath)
audio, sampling_rate = load_wav_to_torch(filename)
audio, sampling_rate = load_wav_to_torch(audiopath)
if sampling_rate != self.sampling_rate:
raise ValueError(f"Input sampling rate does not match specified rate {self.sampling_rate}")
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
return audio_norm, audiopath
if sampling_rate < self.sampling_rate:
print(f'{audiopath} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {self.sampling_rate}. This is not a good idea.')
audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.sampling_rate/sampling_rate, mode='nearest', recompute_scale_factor=False).squeeze()
# Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
# '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
if torch.any(audio > 2) or not torch.any(audio < 0):
print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
audio.clip_(-1, 1)
audio = audio.unsqueeze(0)
return audio, audiopath
def __getitem__(self, index):
clip1, clip2 = None, None

8
codes/models/tacotron2/taco_utils.py
View File

@ -14,7 +14,13 @@ def get_mask_from_lengths(lengths, max_len=None):
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
if data.dtype == np.int16:
norm_fix = 32768
elif data.dtype == np.float16 or data.dtype == np.float32:
norm_fix = 1.
else:
raise NotImplemented(f"Provided data dtype not supported: {data.dtype}")
return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate)
def load_filepaths_and_text(filename, split="|"):

2
codes/scripts/audio/random_mp3_splitter.py
View File

@ -23,7 +23,7 @@ if __name__ == '__main__':
separator = Separator('spleeter:2stems')
files = find_audio_files(src_dir, include_nonwav=True)
for e, file in enumerate(tqdm(files)):
if e < 1:
if e < 1092:
continue
file_basis = osp.relpath(file, src_dir)\
.replace('/', '_')\

4
codes/train.py
View File

@ -234,7 +234,7 @@ class Trainer:
if self.rank <= 0:
for k, v in reduced_metrics.items():
val = torch.stack(v).mean().item()
self.tb_logger.add_scalar(k, val, self.current_step)
self.tb_logger.add_scalar(f'val_{k}', val, self.current_step)
print(f">>Eval {k}: {val}")
if opt['wandb']:
import wandb
@ -282,7 +282,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_asr_mozcv.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_lrdvae_audio_clips.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()