DL-Art-School/codes/scripts/audio/use_vocoder.py

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import pathlib
import numpy
import torch
from scipy.io import wavfile
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from tqdm import tqdm
from models.waveglow.waveglow import WaveGlow
class Vocoder:
def __init__(self):
self.model = WaveGlow(n_mel_channels=80, n_flows=12, n_group=8, n_early_size=2, n_early_every=4, WN_config={'n_layers': 8, 'n_channels': 256, 'kernel_size': 3})
sd = torch.load('../experiments/waveglow_256channels_universal_v5.pth')
self.model.load_state_dict(sd)
self.model = self.model.to('cuda')
self.model.eval()
def transform_mel_to_audio(self, mel):
if len(mel.shape) == 2: # Assume it's missing the batch dimension and fix that.
mel = mel.unsqueeze(0)
with torch.no_grad():
return self.model.infer(mel)
if __name__ == '__main__':
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path = 'data/audio'
files = list(pathlib.Path(path).glob('*.npy'))
for inp in tqdm(files):
inp = str(inp)
mel = torch.tensor(numpy.load(inp)).to('cuda')
vocoder = Vocoder()
wav = vocoder.transform_mel_to_audio(mel)
wavfile.write(f'{inp}.wav', 22050, wav[0].cpu().numpy())