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