fastspeech_squeezewave/SqueezeWave/inference.py

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import os
from scipy.io.wavfile import write
import torch
from mel2samp import files_to_list, MAX_WAV_VALUE
from denoiser import Denoiser
import time

def main(mel_files, squeezewave_path, sigma, output_dir, sampling_rate, is_fp16,
         denoiser_strength):
    mel_files = files_to_list(mel_files)

    #device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    device = torch.device('cpu')   
    squeezewave = torch.load(squeezewave_path,map_location=device) ['model']
    squeezewave = squeezewave.remove_weightnorm(squeezewave)
    squeezewave.eval()
    if is_fp16:
        from apex import amp
        squeezewave, _ = amp.initialize(squeezewave,[],opt_level="O3")

    if denoiser_strength > 0:
        denoiser = Denoiser(squeezewave)
    start = time.time()
    for i, file_path in enumerate(mel_files):
        file_name = os.path.splitext(os.path.basename(file_path))[0]
        mel = torch.load(file_path,map_location=device)
        mel = torch.autograd.Variable(mel)
        mel = mel.half() 
        with torch.no_grad():
            audio = squeezewave.infer(mel, sigma=sigma).float()
            if denoiser_strength > 0:
                audio = denoiser(audio, denoiser_strength)
            audio = audio * MAX_WAV_VALUE
        audio = audio.squeeze()
        audio = audio.cpu().numpy()
        audio = audio.astype('int16')
        audio_path = os.path.join(
            output_dir, "{}_synthesis.wav".format(file_name))
        write(audio_path, sampling_rate, audio)
        print(audio_path)
    end = time.time()
    print("Squeezewave vocoder time")
    print(end-start)


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument('-f', "--filelist_path", required=True)
    parser.add_argument('-w', '--squeezewave_path',
                        help='Path to squeezewave decoder checkpoint with model')
    parser.add_argument('-o', "--output_dir", required=True)
    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.filelist_path, args.squeezewave_path, args.sigma, args.output_dir,
         args.sampling_rate, args.is_fp16, args.denoiser_strength)