55 lines
2.8 KiB
Python
55 lines
2.8 KiB
Python
import argparse
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import torch
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import torchaudio
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from data.audio.unsupervised_audio_dataset import load_audio
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from scripts.audio.gen.speech_synthesis_utils import do_spectrogram_diffusion, \
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load_discrete_vocoder_diffuser, wav_to_mel, convert_mel_to_codes, wav_to_univnet_mel, load_univnet_vocoder
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from trainer.injectors.audio_injectors import denormalize_mel
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from utils.audio import plot_spectrogram
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from 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 do_spectrogram_diffusion(vocoder, dvae, diffuser, codes, cond, spectrogram_compression_factor=256, plt_spec=plot_spec)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-codes_file', type=str, help='Which discretes to decode. Should be a path to a pytorch pickle that simply contains the codes.')
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parser.add_argument('-cond_file', type=str, help='Path to the input audio file.')
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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_tts_mel_flat0\\last_train.yml')
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parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator')
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parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='X:\\dlas\\experiments\\train_diffusion_tts_mel_flat0\\models\\114000_generator_ema.pth')
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args = parser.parse_args()
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print("Loading data..")
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codes = torch.load(args.codes_file)
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conds = load_audio(args.cond_file, 24000)
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conds = conds[:,:102400]
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cond_mel = wav_to_univnet_mel(conds.to('cuda'), do_normalization=False)
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output_shape = (1,100,codes.shape[-1]*4)
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print("Loading Diffusion Model..")
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diffusion = load_model_from_config(args.opt, args.diffusion_model_name, also_load_savepoint=False, load_path=args.diffusion_model_path, strict_load=False).cuda().eval()
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=50, schedule='linear', enable_conditioning_free_guidance=True, conditioning_free_k=1)
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vocoder = load_univnet_vocoder().cuda()
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with torch.no_grad():
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print("Performing inference..")
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for i in range(codes.shape[0]):
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gen_mel = diffuser.p_sample_loop(diffusion, output_shape, model_kwargs={'aligned_conditioning': codes[i].unsqueeze(0), 'conditioning_input': cond_mel})
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gen_mel = denormalize_mel(gen_mel)
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genWav = vocoder.inference(gen_mel)
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torchaudio.save(f'vocoded_{i}.wav', genWav.cpu().squeeze(0), 24000) |