forked from mrq/DL-Art-School
65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
import argparse
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import torch
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import torchaudio
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from dlas.data.audio.unsupervised_audio_dataset import load_audio
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from dlas.scripts.audio.gen.speech_synthesis_utils import (
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convert_mel_to_codes, do_spectrogram_diffusion,
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load_discrete_vocoder_diffuser, wav_to_mel)
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from dlas.utils.audio import plot_spectrogram
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from dlas.utils.util import load_model_from_config
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def roundtrip_vocoding(dvae, vocoder, diffuser, clip, cond=None, plot_spec=False):
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clip = clip.unsqueeze(0)
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if cond is None:
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cond = clip
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else:
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cond = cond.unsqueeze(0)
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mel = wav_to_mel(clip)
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if plot_spec:
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plot_spectrogram(mel[0].cpu())
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codes = convert_mel_to_codes(dvae, mel)
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return
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model',
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default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae.yml')
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parser.add_argument('-diffusion_model_name', type=str,
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help='Name of the diffusion model in opt.', default='generator')
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parser.add_argument('-diffusion_model_path', type=str, help='Name of the diffusion model in opt.',
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default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\6100_generator_ema.pth')
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parser.add_argument('-dvae_model_name', type=str,
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help='Name of the DVAE model in opt.', default='dvae')
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parser.add_argument('-input_file', type=str,
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help='Path to the input torch save file.', default='speech_forward_mels.pth')
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parser.add_argument('-cond', type=str, help='Path to the conditioning input audio file.',
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default='Z:\\clips\\books1\\3042_18_Holden__000000000\\00037.wav')
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args = parser.parse_args()
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print("Loading DVAE..")
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dvae = load_model_from_config(args.opt, args.dvae_model_name)
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print("Loading Diffusion Model..")
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diffusion = load_model_from_config(
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args.opt, args.diffusion_model_name, also_load_savepoint=False, load_path=args.diffusion_model_path)
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print("Loading data..")
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cond = load_audio(args.cond, 22050)
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if cond.shape[-1] > 44100+10000:
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cond = cond[:, 10000:54100]
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cond = cond.unsqueeze(0).cuda()
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=20)
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inp = torch.load(args.input_file)
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codes = inp
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print("Performing inference..")
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for i, cb in enumerate(codes):
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roundtripped = do_spectrogram_diffusion(diffusion, dvae, diffuser, cb.unsqueeze(
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0).cuda(), cond, spectrogram_compression_factor=128, plt_spec=False)
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torchaudio.save(
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f'vocoded_output_sp_{i}.wav', roundtripped.squeeze(0).cpu(), 11025)
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