29 lines
1.0 KiB
Python
29 lines
1.0 KiB
Python
import torch
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from scipy.io import wavfile
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from models.waveglow.waveglow import WaveGlow
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from utils.audio import plot_spectrogram
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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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vocoder = Vocoder()
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m = torch.load('test_mels.pth')
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for i, b in enumerate(m):
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plot_spectrogram(b.cpu())
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wav = vocoder.transform_mel_to_audio(b)
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wavfile.write(f'{i}.wav', 22050, wav[0].cpu().numpy()) |