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@ -59,14 +59,12 @@ _ = model.eval() |
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print("This Tacotron model has been trained for ",torch.load(tacotron2_pretrained_model, map_location=torch.device('cpu'))['iteration']," Iterations.") |
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print("This Tacotron model has been trained for ",torch.load(tacotron2_pretrained_model, map_location=torch.device('cpu'))['iteration']," Iterations.") |
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# Load WaveGlow model into GPU |
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# Load WaveGlow model into GPU |
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waveglow_pretrained_model = 'squeezewave.pt' |
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# squeezewave = torch.load(waveglow_pretrained_model, map_location=torch.device('cpu'))['model'] |
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waveglow_pretrained_model = 'squeezewave_dict.pt' |
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with open(join(project_name2, 'SqueezeWave/configs/config_a128_c256.json')) as f: |
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with open(join(project_name2, 'SqueezeWave/configs/config_a128_c256.json')) as f: |
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data = f.read() |
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data = f.read() |
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config = json.loads(data) |
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config = json.loads(data) |
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waveglow = SqueezeWave(**config['squeezewave_config']) |
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waveglow = SqueezeWave(**config['squeezewave_config']) |
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waveglow.load_state_dict(torch.load('squeezewave_dict.pt'), strict=False) |
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# waveglow.load_state_dict(squeezewave.state_dict(), strict=False) |
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waveglow.load_state_dict(torch.load(waveglow_pretrained_model), strict=False) |
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waveglow = waveglow.remove_weightnorm(waveglow) |
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waveglow = waveglow.remove_weightnorm(waveglow) |
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waveglow.eval() |
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waveglow.eval() |
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for k in waveglow.convinv: |
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for k in waveglow.convinv: |
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