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dependencies = ['torch']
import torch
from model.generator import Generator
model_params = {
'nvidia_tacotron2_LJ11_epoch6400': {
'mel_channel': 80,
'model_url': 'https://github.com/seungwonpark/melgan/releases/download/v0.3-alpha/nvidia_tacotron2_LJ11_epoch6400.pt',
},
}
def melgan(model_name='nvidia_tacotron2_LJ11_epoch6400', pretrained=True, progress=True):
params = model_params[model_name]
model = Generator(params['mel_channel'])
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(params['model_url'],
progress=progress)
model.load_state_dict(state_dict['model_g'])
model.eval(inference=True)
return model
if __name__ == '__main__':
vocoder = torch.hub.load('seungwonpark/melgan', 'melgan')
mel = torch.randn(1, 80, 234) # use your own mel-spectrogram here
print('Input mel-spectrogram shape: {}'.format(mel.shape))
if torch.cuda.is_available():
print('Moving data & model to GPU')
vocoder = vocoder.cuda()
mel = mel.cuda()
with torch.no_grad():
audio = vocoder.inference(mel)
print('Output audio shape: {}'.format(audio.shape))
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