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