dan
/
fastspeech_squeezewave


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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)