inference.py

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import glob
import os
import numpy as np 
import torch
from denoiser import Denoiser
from scipy.io.wavfile import write


MAX_WAV_VALUE = 32768

def main(waveglow_path, sigma, output_dir, sampling_rate, is_fp16, denoiser_strength):
    waveglow = torch.load(waveglow_path)['model']
    waveglow = waveglow.remove_weightnorm(waveglow)
    waveglow.cuda().eval()
    if is_fp16:
        from apex import amp
        waveglow, _ = amp.initialize(waveglow, [], opt_level="O3")

    if denoiser_strength > 0:
        denoiser = Denoiser(waveglow).cuda()

    for i, file_path in enumerate(glob.glob('*.npy')):
        file_name = os.path.splitext(os.path.basename(file_path))[0]
        mel = torch.from_numpy(np.load(file_path))
        mel = torch.unsqueeze(mel, 0).cuda()
        mel = mel.half() if is_fp16 else mel
        with torch.no_grad():
            audio = waveglow.infer(mel, sigma=sigma)
            if denoiser_strength > 0:
                audio = denoiser(audio, denoiser_strength)
            audio = audio * MAX_WAV_VALUE
        audio = audio.squeeze().cpu().numpy()
        audio_path = os.path.join(output_dir, f'waveglow_{file_name}.wav')
        write(audio_path, sampling_rate, audio.astype('int16'))


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument('-w', '--waveglow_path',
                        help='Path to waveglow decoder checkpoint with model')
    parser.add_argument('-o', "--output_dir", default='.')
    parser.add_argument("-s", "--sigma", default=1.0, type=float)
    parser.add_argument("--sampling_rate", default=22050, type=int)
    parser.add_argument("--is_fp16", action="store_true")
    parser.add_argument("-d", "--denoiser_strength", default=0.0, type=float,
                        help='Removes model bias. Start with 0.1 and adjust')

    args = parser.parse_args()

    main(args.waveglow_path, args.sigma, args.output_dir,
         args.sampling_rate, args.is_fp16, args.denoiser_strength)