Update scripts
This commit is contained in:
James Betker
parent
34fbb78671
commit
f44b064c5e
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@ -112,13 +112,17 @@ if __name__ == '__main__':
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]
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parser = argparse.ArgumentParser()
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parser.add_argument('-text', type=str, help='Text to speak.', default='This is the real secret of life: to be completely engaged in what you are doing in the here and now. To realize that instead of work, it is play.')
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parser.add_argument('-opt_code_gen', type=str, help='Path to options YAML file used to train the code_gen model', default='D:\\dlas\\options\\train_encoder_build_ctc_alignments.yml')
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parser.add_argument('-code_gen_model_name', type=str, help='Name of the code_gen model in opt.', default='generator')
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parser.add_argument('-code_gen_model_path', type=str, help='Path to saved code_gen model weights', default='D:\\dlas\\experiments\\train_encoder_build_ctc_alignments\\models\\31000_generator_ema.pth')
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parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium.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='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\models\\73000_generator_ema.pth')
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parser.add_argument('-sr_opt', type=str, help='Path to options YAML file used to train the SR diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample.yml')
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parser.add_argument('-sr_diffusion_model_name', type=str, help='Name of the SR diffusion model in opt.', default='generator')
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parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample\\models\\7000_generator_ema.pth')
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parser.add_argument('-cond', type=str, help='Type of conditioning voice', default='carlin')
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parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample\\models\\31000_generator_ema.pth')
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parser.add_argument('-cond', type=str, help='Type of conditioning voice', default='simmons')
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parser.add_argument('-diffusion_steps', type=int, help='Number of diffusion steps to perform to create the generate. Lower steps reduces quality, but >40 is generally pretty good.', default=100)
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parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_tts')
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parser.add_argument('-device', type=str, help='Device to run on', default='cuda')
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@ -129,6 +133,21 @@ if __name__ == '__main__':
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base_sample_rate = 5500
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sr_sample_rate = 22050
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print("Loading provided conditional audio..")
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sr_cond = load_audio(conditioning_clips[args.cond], sr_sample_rate).to(args.device)
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if sr_cond.shape[-1] > 88000:
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sr_cond = sr_cond[:,:88000]
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cond_mel = wav_to_mel(sr_cond)
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cond = torchaudio.functional.resample(sr_cond, sr_sample_rate, base_sample_rate)
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torchaudio.save(os.path.join(args.output_path, 'cond_base.wav'), cond.cpu(), base_sample_rate)
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torchaudio.save(os.path.join(args.output_path, 'cond_sr.wav'), sr_cond.cpu(), sr_sample_rate)
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print("Generating codes for text..")
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codegen = load_model_from_config(args.opt_code_gen, args.code_gen_model_name, also_load_savepoint=False,
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load_path=args.code_gen_model_path, device='cuda').eval()
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codes = codegen.generate(cond_mel, [args.text])
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del codegen
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print("Loading Diffusion Models..")
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diffusion = load_model_from_config(args.opt, args.diffusion_model_name, also_load_savepoint=False,
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load_path=args.diffusion_model_path, device='cpu').eval()
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@ -137,23 +156,17 @@ if __name__ == '__main__':
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sr_diffusion = load_model_from_config(args.sr_opt, args.sr_diffusion_model_name, also_load_savepoint=False,
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load_path=args.sr_diffusion_model_path, device='cpu').eval()
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sr_diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps, schedule='linear')
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sr_cond = load_audio(conditioning_clips[args.cond], sr_sample_rate).to(args.device)
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if sr_cond.shape[-1] > 88000:
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sr_cond = sr_cond[:,:88000]
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cond = audio = torchaudio.functional.resample(sr_cond, sr_sample_rate, base_sample_rate)
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torchaudio.save(os.path.join(args.output_path, 'cond_base.wav'), cond.cpu(), base_sample_rate)
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torchaudio.save(os.path.join(args.output_path, 'cond_sr.wav'), sr_cond.cpu(), sr_sample_rate)
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with torch.no_grad():
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for p, code in enumerate(provided_codes):
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for p, code in enumerate([codes]):
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print("Loading data..")
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aligned_codes = torch.tensor(code).to(args.device)
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aligned_codes = code.to(args.device)
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print("Performing initial diffusion..")
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output_shape = (1, 1, ceil_multiple(aligned_codes.shape[-1]*aligned_codes_compression_factor, 2048))
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diffusion = diffusion.cuda()
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output_base = diffuser.p_sample_loop(diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
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model_kwargs={'tokens': aligned_codes.unsqueeze(0),
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model_kwargs={'tokens': aligned_codes,
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'conditioning_input': cond.unsqueeze(0)})
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diffusion = diffusion.cpu()
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torchaudio.save(os.path.join(args.output_path, f'{p}_output_mean_base.wav'), output_base.cpu().squeeze(0), base_sample_rate)
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@ -161,8 +174,8 @@ if __name__ == '__main__':
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print("Performing SR diffusion..")
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output_shape = (1, 1, output_base.shape[-1] * (sr_sample_rate // base_sample_rate))
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sr_diffusion = sr_diffusion.cuda()
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output = diffuser.p_sample_loop(sr_diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
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model_kwargs={'tokens': aligned_codes.unsqueeze(0),
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output = sr_diffuser.p_sample_loop(sr_diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
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model_kwargs={'tokens': torch.zeros_like(aligned_codes),
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'conditioning_input': sr_cond.unsqueeze(0),
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'lr_input': output_base})
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sr_diffusion = sr_diffusion.cpu()
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@ -18,8 +18,23 @@ def ceil_multiple(base, multiple):
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return base + (multiple - res)
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def get_ctc_codes_for(src_clip_path):
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"""
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Uses wav2vec2 to infer CTC codes for the audio clip at the specified path.
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"""
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from transformers import Wav2Vec2ForCTC
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from transformers import Wav2Vec2Processor
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model = Wav2Vec2ForCTC.from_pretrained(f"facebook/wav2vec2-large-960h").to("cuda")
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processor = Wav2Vec2Processor.from_pretrained(f"facebook/wav2vec2-large-960h")
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clip = load_audio(src_clip_path, 16000).squeeze()
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clip_inp = processor(clip.numpy(), return_tensors='pt', sampling_rate=16000).input_values.cuda()
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logits = model(clip_inp).logits
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return torch.argmax(logits, dim=-1), clip
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if __name__ == '__main__':
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conditioning_clips = {
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provided_voices = {
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# Male
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'simmons': 'Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav',
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'carlin': 'Y:\\clips\\books1\\12_dchha13 Bubonic Nukes\\00097.wav',
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@ -31,96 +46,17 @@ if __name__ == '__main__':
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'adrift': 'Y:\\clips\\books2\\5608_Gear__W_Michael_-_Donovan_1-5_(2018-2021)_(book_4_Gear__W_Michael_-_Donovan_5_-_Adrift_(2021)_Gear__W_Michael_-_Adrift_(Donovan_5)_—_82__000000000\\00019.wav',
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}
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provided_codes = [
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# but facts within easy reach of any one who cares to know them go to say that the greater abstenence of women is in some part
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# due to an imperative conventionality and this conventionality is in a general way strongest were the patriarchal tradition
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# the tradition that the woman is a chattel has retained its hold in greatest vigor
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# 3570/5694/3570_5694_000008_000001.wav
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[0, 0, 24, 0, 16, 0, 6, 0, 4, 0, 0, 0, 0, 0, 20, 0, 7, 0, 0, 19, 19, 0, 0, 6, 0, 0, 12, 12, 0, 4, 4, 0, 18, 18,
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0, 10, 0, 6, 11, 11, 10, 10, 9, 9, 4, 4, 4, 5, 5, 0, 7, 0, 0, 0, 0, 12, 0, 22, 22, 0, 4, 4, 0, 13, 13, 5, 0, 7,
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7, 0, 0, 19, 11, 0, 4, 4, 8, 20, 4, 4, 4, 7, 0, 9, 9, 0, 22, 4, 4, 0, 8, 0, 9, 5, 4, 4, 18, 11, 11, 8, 4, 4, 0,
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0, 0, 19, 19, 7, 0, 0, 13, 5, 5, 0, 12, 12, 4, 4, 6, 6, 8, 8, 4, 4, 0, 26, 9, 9, 8, 0, 18, 0, 0, 4, 4, 6, 6,
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11, 5, 0, 17, 17, 0, 0, 4, 4, 4, 4, 0, 0, 0, 21, 0, 8, 0, 0, 0, 0, 4, 4, 6, 6, 8, 0, 4, 4, 0, 0, 12, 0, 7, 7,
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0, 0, 22, 0, 4, 4, 6, 11, 11, 7, 6, 6, 4, 4, 6, 11, 5, 4, 4, 4, 0, 21, 0, 13, 5, 5, 7, 7, 0, 0, 6, 6, 5, 0, 13,
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0, 4, 4, 0, 7, 0, 0, 0, 24, 0, 0, 12, 12, 0, 0, 6, 0, 5, 0, 0, 9, 9, 0, 5, 0, 9, 0, 0, 19, 5, 5, 4, 4, 8, 20,
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20, 4, 4, 4, 4, 0, 18, 18, 8, 0, 0, 0, 17, 0, 5, 0, 9, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 10, 0, 0, 12, 12, 4, 4, 0,
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10, 0, 9, 0, 4, 4, 0, 0, 12, 0, 0, 8, 0, 17, 5, 5, 4, 4, 0, 0, 0, 23, 23, 0, 7, 0, 13, 0, 0, 0, 6, 0, 4, 0, 0,
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0, 0, 14, 0, 16, 16, 0, 0, 5, 0, 4, 4, 0, 6, 8, 0, 4, 4, 7, 9, 4, 4, 4, 0, 10, 10, 17, 0, 0, 0, 23, 0, 5, 0, 0,
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13, 13, 0, 7, 0, 0, 6, 6, 0, 10, 0, 25, 5, 5, 4, 4, 0, 0, 0, 19, 19, 8, 8, 9, 0, 0, 0, 0, 0, 25, 0, 5, 0, 9, 0,
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0, 0, 6, 6, 10, 8, 8, 0, 9, 0, 0, 0, 7, 0, 0, 15, 0, 10, 0, 0, 0, 0, 6, 6, 0, 0, 22, 0, 0, 0, 4, 4, 4, 4, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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7, 0, 9, 14, 0, 4, 0, 0, 6, 11, 10, 0, 0, 0, 12, 0, 4, 4, 0, 19, 19, 8, 9, 9, 0, 0, 25, 0, 5, 0, 9, 0, 0, 6, 6,
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10, 8, 8, 9, 9, 0, 0, 7, 0, 0, 15, 0, 10, 0, 0, 0, 0, 6, 0, 22, 22, 0, 4, 4, 0, 0, 10, 0, 0, 0, 0, 12, 12, 0,
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0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 10, 0, 9, 4, 4, 4, 7, 4, 4, 4, 0, 21, 0, 5, 0, 9, 0, 5, 5, 13, 13, 7, 0, 15,
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15, 0, 0, 4, 4, 0, 18, 18, 0, 7, 0, 0, 22, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 12, 12, 0, 0, 0, 6, 6, 13, 13, 8, 0, 0, 9, 9, 0, 21, 0, 0, 5, 5, 0, 0, 0, 12, 12, 0, 0, 6,
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0, 0, 0, 4, 4, 0, 0, 0, 18, 0, 5, 0, 13, 0, 5, 4, 4, 6, 11, 5, 0, 4, 4, 23, 23, 7, 7, 0, 0, 0, 6, 0, 13, 13,
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10, 10, 0, 0, 0, 0, 7, 13, 13, 0, 19, 11, 11, 0, 0, 7, 15, 15, 0, 0, 4, 4, 0, 6, 13, 13, 7, 7, 0, 0, 0, 14, 10,
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10, 0, 0, 0, 0, 0, 6, 10, 10, 8, 8, 9, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 11, 5, 0, 4, 4, 0, 6, 13, 13, 7, 7, 0, 0, 0, 14, 10, 10, 0, 0, 0, 6, 10, 10,
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8, 9, 9, 0, 0, 4, 4, 0, 6, 11, 7, 0, 6, 4, 4, 6, 11, 5, 4, 4, 4, 18, 18, 8, 0, 0, 17, 7, 0, 9, 0, 4, 10, 0, 0,
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12, 12, 4, 4, 4, 7, 4, 4, 0, 0, 0, 19, 11, 0, 7, 0, 6, 0, 0, 0, 6, 0, 5, 0, 15, 15, 0, 0, 0, 4, 4, 4, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 0, 7, 0, 0, 0, 12, 0, 0, 4, 4, 0, 13, 5, 5, 0, 0, 0, 0, 6, 6, 0, 0,
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7, 10, 10, 0, 9, 0, 5, 0, 14, 4, 4, 4, 0, 10, 0, 0, 0, 6, 0, 0, 0, 0, 0, 12, 0, 4, 4, 0, 0, 0, 11, 0, 0, 8, 0,
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0, 0, 15, 0, 0, 14, 0, 4, 4, 4, 0, 10, 0, 9, 4, 4, 4, 4, 4, 0, 21, 0, 13, 5, 5, 7, 7, 0, 0, 6, 0, 5, 0, 0, 12,
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0, 6, 0, 4, 0, 0, 25, 10, 0, 0, 0, 21, 0, 8, 0, 0, 13, 13, 0, 0, 4, 4, 4, 4, 0, 0, 0],
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# the competitor with whom the entertainer wishes to institute a comparison is by this method made to serve as a means to the end
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# 3570/5694/3570_5694_000011_000005.wav
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[0, 0, 6, 11, 5, 0, 4, 0, 19, 19, 8, 17, 0, 0, 0, 0, 23, 0, 5, 5, 0, 0, 6, 6, 10, 10, 0, 0, 6, 6, 0, 8, 0, 13,
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13, 0, 4, 4, 18, 18, 10, 0, 6, 11, 11, 4, 4, 4, 0, 0, 18, 18, 11, 0, 8, 0, 0, 0, 0, 17, 0, 0, 4, 0, 6, 11, 5,
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0, 4, 4, 0, 5, 9, 9, 0, 6, 5, 5, 13, 13, 0, 0, 6, 6, 0, 7, 0, 10, 0, 9, 0, 0, 5, 0, 13, 4, 4, 0, 18, 10, 10, 0,
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0, 12, 11, 11, 0, 5, 0, 0, 0, 12, 0, 0, 4, 4, 0, 0, 6, 6, 8, 0, 0, 4, 4, 4, 0, 10, 9, 9, 0, 0, 0, 0, 12, 0, 0,
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6, 0, 10, 0, 0, 0, 6, 0, 16, 16, 0, 6, 5, 0, 4, 4, 7, 4, 4, 19, 19, 8, 0, 17, 0, 0, 0, 0, 0, 23, 0, 0, 7, 0, 0,
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0, 13, 0, 10, 0, 0, 0, 0, 0, 12, 0, 0, 8, 0, 9, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 24, 0, 22, 0, 4, 4,
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0, 6, 11, 10, 0, 0, 0, 12, 0, 0, 4, 4, 0, 0, 17, 5, 5, 0, 0, 0, 6, 11, 11, 8, 0, 0, 14, 14, 0, 0, 4, 4, 4, 4,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 17, 7, 0, 0, 0, 0, 14, 5, 0, 4, 4, 6, 8, 4,
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4, 0, 0, 0, 12, 12, 0, 5, 5, 0, 13, 13, 0, 25, 5, 4, 4, 7, 0, 12, 4, 4, 4, 7, 4, 4, 0, 17, 5, 0, 0, 7, 0, 0, 9,
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0, 0, 0, 0, 12, 0, 4, 4, 0, 6, 0, 8, 0, 4, 4, 6, 11, 5, 4, 4, 4, 0, 0, 5, 0, 9, 9, 0, 0, 0, 0, 14, 0, 0, 4, 4,
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4, 4, 4, 0, 0],
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# the livery becomes obnoxious to nearly all who are required to wear it
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# 3570/5694/3570_5694_000014_000021.wav
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[0, 0, 6, 11, 5, 0, 0, 4, 4, 0, 15, 10, 10, 0, 0, 25, 5, 0, 13, 13, 0, 22, 0, 0, 4, 0, 24, 24, 5, 0, 0, 0, 19,
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19, 0, 8, 0, 17, 5, 5, 0, 12, 0, 4, 4, 4, 0, 8, 0, 0, 24, 0, 0, 0, 9, 9, 0, 8, 0, 0, 0, 0, 0, 28, 0, 0, 0, 10,
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0, 8, 16, 0, 12, 12, 12, 0, 4, 0, 6, 6, 8, 0, 4, 4, 0, 9, 5, 0, 7, 7, 13, 0, 0, 15, 22, 22, 4, 4, 0, 0, 0, 0,
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0, 0, 0, 7, 0, 15, 0, 0, 15, 0, 4, 4, 4, 18, 11, 11, 8, 0, 4, 4, 0, 7, 0, 13, 5, 4, 4, 13, 13, 5, 0, 0, 0, 30,
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30, 16, 0, 0, 10, 0, 0, 0, 13, 5, 0, 14, 4, 4, 6, 6, 8, 0, 4, 4, 18, 18, 5, 5, 7, 7, 13, 13, 0, 4, 4, 0, 10, 0,
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0, 0, 0, 6, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0],
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# in the nature of things luxuries and the comforts of life belong to the leisure class
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# 3570/5694/3570_5694_000006_000007.wav
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[0, 0, 0, 0, 0, 10, 9, 0, 4, 4, 6, 11, 5, 4, 4, 4, 9, 9, 7, 7, 0, 0, 0, 0, 0, 0, 6, 0, 16, 16, 13, 13, 5, 0, 4, 4, 8, 0, 20, 4, 4, 4, 0, 6, 0, 11, 10, 0, 9, 0, 21, 0, 0, 0, 12, 12, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 15, 0, 16, 16, 0, 0, 28, 0, 0, 0, 16, 16, 0, 13, 13, 0, 10, 0, 5, 0, 0, 0, 12, 0, 0, 4, 4, 4, 0, 0, 7, 0, 9, 0, 14, 4, 4, 6, 11, 5, 4, 4, 0, 0, 19, 0, 8, 17, 17, 0, 0, 0, 0, 0, 20, 0, 8, 0, 13, 0, 6, 0, 12, 4, 4, 8, 0, 20, 4, 4, 4, 0, 0, 15, 0, 10, 10, 0, 0, 0, 20, 5, 0, 4, 4, 0, 0, 24, 5, 0, 0, 0, 15, 8, 0, 9, 0, 21, 0, 0, 0, 4, 4, 6, 8, 4, 4, 4, 6, 11, 5, 4, 4, 15, 15, 5, 10, 0, 0, 12, 0, 16, 13, 5, 5, 4, 4, 0, 19, 0, 15, 15, 0, 0, 7, 0, 0, 12, 12, 0, 0, 0, 12, 12, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],
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# from arcaic times down through all the length of the patriarchal regime it has been the office of the women to
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# prepare and administer these luxuries and it has been the perquisite of the men of gentle birth and breeding
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# to consume them
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# 3570/5694/3570_5694_000007_000003.wav
|
||||
[0, 0, 0, 0, 0, 0, 20, 13, 8, 0, 17, 0, 4, 4, 0, 7, 0, 13, 0, 0, 0, 0, 0, 19, 0, 0, 0, 7, 0, 0, 0, 0, 10, 0, 19, 0, 0, 0, 4, 4, 0, 0, 0, 0, 6, 0, 0, 0, 10, 0, 0, 17, 5, 0, 0, 0, 12, 0, 4, 0, 0, 0, 0, 14, 0, 0, 8, 0, 18, 0, 0, 0, 9, 0, 0, 0, 0, 4, 4, 0, 0, 0, 6, 11, 13, 8, 0, 16, 21, 21, 11, 0, 4, 4, 7, 0, 15, 0, 15, 15, 4, 4, 6, 11, 5, 5, 4, 4, 0, 15, 0, 5, 0, 0, 9, 9, 0, 21, 0, 0, 6, 11, 0, 4, 4, 8, 8, 20, 4, 4, 4, 6, 11, 5, 4, 4, 0, 0, 0, 23, 0, 7, 7, 0, 0, 0, 0, 0, 6, 6, 13, 13, 13, 10, 0, 0, 0, 0, 0, 7, 13, 13, 0, 19, 11, 11, 11, 0, 0, 7, 15, 15, 0, 4, 4, 4, 13, 13, 5, 0, 0, 0, 0, 21, 21, 0, 0, 10, 0, 0, 0, 0, 17, 5, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 6, 4, 4, 0, 0, 11, 7, 7, 0, 0, 12, 0, 4, 4, 0, 24, 5, 0, 0, 5, 5, 9, 0, 4, 6, 6, 11, 5, 4, 4, 0, 0, 8, 0, 20, 0, 0, 0, 20, 0, 10, 0, 0, 0, 19, 5, 0, 4, 4, 8, 0, 20, 4, 4, 6, 11, 5, 4, 4, 4, 18, 8, 0, 0, 0, 17, 5, 0, 9, 9, 0, 0, 4, 4, 0, 6, 6, 8, 0, 0, 4, 4, 0, 23, 23, 13, 5, 5, 0, 0, 0, 0, 23, 23, 0, 7, 0, 0, 0, 13, 5, 0, 0, 0, 4, 4, 0, 7, 0, 9, 14, 0, 4, 4, 0, 0, 7, 0, 14, 0, 0, 0, 17, 17, 10, 0, 9, 0, 10, 10, 0, 0, 12, 12, 0, 0, 0, 6, 0, 5, 13, 13, 0, 0, 0, 0, 4, 4, 4, 6, 11, 11, 5, 0, 0, 0, 12, 5, 5, 4, 4, 15, 15, 0, 16, 0, 0, 0, 28, 0, 0, 0, 16, 0, 0, 13, 13, 10, 0, 5, 5, 0, 0, 12, 12, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 9, 0, 14, 4, 4, 10, 0, 6, 4, 4, 0, 11, 11, 7, 0, 0, 0, 12, 0, 4, 4, 0, 0, 0, 0, 24, 5, 0, 0, 5, 5, 9, 9, 4, 4, 4, 6, 11, 5, 4, 4, 0, 0, 0, 23, 0, 5, 0, 13, 0, 0, 0, 0, 0, 30, 30, 16, 10, 10, 0, 0, 0, 12, 0, 10, 0, 0, 6, 5, 0, 4, 4, 8, 20, 0, 4, 4, 6, 11, 5, 4, 4, 0, 17, 5, 0, 0, 0, 9, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 20, 4, 4, 4, 0, 0, 21, 0, 5, 5, 0, 9, 9, 0, 0, 0, 6, 0, 15, 0, 5, 0, 4, 0, 0, 0, 24, 0, 10, 0, 13, 0, 0, 0, 0, 6, 11, 0, 0, 4, 0, 0, 7, 0, 9, 14, 14, 4, 4, 4, 0, 0, 24, 13, 5, 0, 0, 0, 5, 0, 0, 14, 10, 0, 9, 21, 21, 0, 4, 4, 0, 6, 8, 0, 4, 4, 0, 19, 8, 0, 9, 0, 0, 0, 0, 0, 0, 0, 12, 0, 16, 0, 17, 5, 0, 0, 4, 4, 6, 11, 5, 0, 17, 0, 4, 4, 4, 4, 0, 0],
|
||||
# yes it is perfection she declared
|
||||
# 1284/1180/1284_1180_000036_000000.wav
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 0, 4, 4, 4, 4, 0, 0, 10, 0, 6, 0, 4, 4, 0, 0, 10, 0, 0, 0, 0, 0, 12, 0, 4, 4, 0, 0, 0, 23, 0, 5, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0, 5, 0, 0, 0, 19, 0, 0, 6, 6, 0, 10, 0, 8, 0, 9, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 12, 11, 11, 5, 0, 4, 4, 0, 14, 0, 5, 0, 0, 0, 0, 19, 15, 15, 0, 0, 7, 0, 0, 0, 13, 0, 5, 0, 14, 4, 4, 4, 4, 0, 0, 0],
|
||||
# then it must be somewhere in the blue forest
|
||||
# 1284/1180/1284_1180_000016_000002.wav
|
||||
[0, 0, 0, 6, 11, 5, 0, 9, 0, 4, 4, 10, 6, 4, 4, 0, 17, 17, 16, 0, 0, 12, 0, 6, 4, 4, 0, 24, 5, 5, 0, 0, 4, 4, 0, 0, 12, 12, 0, 8, 0, 0, 17, 5, 5, 0, 0, 18, 18, 11, 5, 0, 13, 13, 5, 0, 4, 4, 10, 9, 4, 4, 6, 11, 5, 4, 4, 0, 24, 15, 15, 16, 16, 0, 5, 5, 0, 0, 4, 4, 0, 0, 0, 20, 8, 8, 8, 0, 0, 0, 13, 13, 0, 5, 5, 0, 0, 0, 0, 0, 12, 12, 0, 0, 6, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],
|
||||
# happy youth that is ready to pack its valus and start for cathay on an hour's notice
|
||||
# 4970/29093/4970_29093_000044_000002.wav
|
||||
[0, 0, 0, 0, 11, 0, 7, 23, 0, 0, 0, 0, 23, 0, 22, 22, 0, 0, 0, 4, 4, 0, 0, 22, 8, 8, 16, 16, 0, 0, 0, 6, 6, 11, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 11, 7, 6, 0, 4, 4, 10, 0, 0, 12, 0, 4, 0, 13, 13, 5, 0, 7, 0, 0, 14, 22, 0, 0, 0, 4, 0, 6, 0, 8, 4, 4, 0, 0, 0, 0, 0, 0, 23, 0, 7, 0, 0, 19, 0, 0, 26, 4, 4, 4, 10, 0, 6, 0, 12, 4, 4, 0, 0, 0, 25, 0, 7, 0, 0, 0, 15, 0, 0, 16, 0, 0, 0, 0, 12, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 9, 0, 14, 4, 4, 0, 12, 12, 0, 6, 0, 7, 0, 13, 0, 0, 0, 6, 0, 0, 4, 4, 0, 0, 0, 0, 20, 8, 0, 13, 0, 4, 4, 4, 0, 0, 19, 0, 7, 7, 0, 0, 0, 0, 0, 6, 11, 0, 0, 7, 0, 0, 0, 22, 0, 0, 0, 0, 0, 4, 4, 0, 0, 8, 0, 9, 0, 4, 4, 7, 9, 4, 4, 4, 0, 0, 0, 11, 8, 8, 16, 0, 0, 13, 13, 0, 0, 0, 27, 0, 12, 0, 4, 4, 0, 9, 8, 8, 0, 0, 0, 0, 6, 10, 0, 0, 0, 0, 0, 19, 5, 5, 0, 0, 4, 4, 4, 4, 4, 0],
|
||||
# well then i must make some suggestions to you
|
||||
# 1580/141084/1580_141084_000057_000000.wav
|
||||
[0, 0, 0, 0, 0, 0, 0, 18, 0, 5, 0, 15, 0, 0, 15, 15, 4, 4, 0, 0, 6, 11, 5, 0, 0, 0, 9, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 10, 0, 4, 4, 0, 17, 0, 16, 0, 0, 12, 0, 6, 0, 4, 4, 0, 17, 17, 7, 0, 26, 5, 5, 4, 4, 0, 12, 12, 8, 8, 17, 17, 5, 0, 4, 4, 4, 12, 12, 16, 0, 21, 0, 0, 0, 0, 21, 21, 0, 5, 0, 0, 0, 12, 0, 0, 0, 6, 6, 0, 10, 0, 8, 8, 9, 0, 0, 0, 0, 0, 0, 12, 0, 0, 4, 4, 0, 0, 6, 0, 8, 0, 4, 4, 4, 0, 0, 22, 22, 0, 8, 16, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],
|
||||
# some others too big cotton county
|
||||
# 1995/1826/1995_1826_000010_000002.wav
|
||||
[0, 0, 0, 0, 12, 0, 8, 0, 17, 5, 4, 4, 0, 8, 0, 0, 6, 11, 5, 0, 13, 13, 0, 0, 12, 0, 4, 4, 0, 0, 6, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 0, 0, 10, 0, 0, 0, 0, 21, 0, 0, 4, 4, 4, 0, 0, 0, 19, 0, 8, 0, 6, 6, 0, 0, 0, 6, 8, 0, 9, 9, 0, 0, 4, 0, 0, 0, 0, 19, 8, 8, 16, 0, 9, 9, 0, 0, 6, 6, 0, 0, 22, 0, 0, 0, 0, 4, 4, 0, 0, 0],
|
||||
]
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-src_clip', type=str, help='Path to the audio file to translate', default='D:\\tortoise-tts\\voices\\dotrice\\1.wav')
|
||||
parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium.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='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\models\\73000_generator_ema.pth')
|
||||
parser.add_argument('-sr_opt', type=str, help='Path to options YAML file used to train the SR diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample.yml')
|
||||
parser.add_argument('-sr_diffusion_model_name', type=str, help='Name of the SR diffusion model in opt.', default='generator')
|
||||
parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample\\models\\7000_generator_ema.pth')
|
||||
parser.add_argument('-cond', type=str, help='Type of conditioning voice', default='carlin')
|
||||
parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample\\models\\26500_generator_ema.pth')
|
||||
parser.add_argument('-voice', type=str, help='Type of conditioning voice', default='puppy')
|
||||
parser.add_argument('-diffusion_steps', type=int, help='Number of diffusion steps to perform to create the generate. Lower steps reduces quality, but >40 is generally pretty good.', default=100)
|
||||
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_tts')
|
||||
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_voice_translation')
|
||||
parser.add_argument('-device', type=str, help='Device to run on', default='cuda')
|
||||
args = parser.parse_args()
|
||||
os.makedirs(args.output_path, exist_ok=True)
|
||||
|
|
@ -137,33 +73,31 @@ if __name__ == '__main__':
|
|||
sr_diffusion = load_model_from_config(args.sr_opt, args.sr_diffusion_model_name, also_load_savepoint=False,
|
||||
load_path=args.sr_diffusion_model_path, device='cpu').eval()
|
||||
sr_diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps, schedule='linear')
|
||||
sr_cond = load_audio(conditioning_clips[args.cond], sr_sample_rate).to(args.device)
|
||||
if sr_cond.shape[-1] > 88000:
|
||||
sr_cond = sr_cond[:,:88000]
|
||||
cond = audio = torchaudio.functional.resample(sr_cond, sr_sample_rate, base_sample_rate)
|
||||
sr_cond = load_audio(provided_voices[args.voice], sr_sample_rate).to(args.device)
|
||||
cond = torchaudio.functional.resample(sr_cond, sr_sample_rate, base_sample_rate)
|
||||
torchaudio.save(os.path.join(args.output_path, 'cond_base.wav'), cond.cpu(), base_sample_rate)
|
||||
torchaudio.save(os.path.join(args.output_path, 'cond_sr.wav'), sr_cond.cpu(), sr_sample_rate)
|
||||
|
||||
with torch.no_grad():
|
||||
for p, code in enumerate(provided_codes):
|
||||
print("Loading data..")
|
||||
aligned_codes = torch.tensor(code).to(args.device)
|
||||
print("Extracting CTC codes from source clip..")
|
||||
aligned_codes, src_clip = get_ctc_codes_for(args.src_clip)
|
||||
torchaudio.save(os.path.join(args.output_path, f'source_clip.wav'), src_clip.unsqueeze(0).cpu(), 16000)
|
||||
|
||||
print("Performing initial diffusion..")
|
||||
output_shape = (1, 1, ceil_multiple(aligned_codes.shape[-1]*aligned_codes_compression_factor, 2048))
|
||||
diffusion = diffusion.cuda()
|
||||
output_base = diffuser.p_sample_loop(diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
|
||||
model_kwargs={'tokens': aligned_codes.unsqueeze(0),
|
||||
'conditioning_input': cond.unsqueeze(0)})
|
||||
diffusion = diffusion.cpu()
|
||||
torchaudio.save(os.path.join(args.output_path, f'{p}_output_mean_base.wav'), output_base.cpu().squeeze(0), base_sample_rate)
|
||||
print("Performing initial diffusion..")
|
||||
output_shape = (1, 1, ceil_multiple(aligned_codes.shape[-1]*aligned_codes_compression_factor, 2048))
|
||||
diffusion = diffusion.cuda()
|
||||
output_base = diffuser.p_sample_loop(diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
|
||||
model_kwargs={'tokens': aligned_codes,
|
||||
'conditioning_input': cond.unsqueeze(0)})
|
||||
diffusion = diffusion.cpu()
|
||||
torchaudio.save(os.path.join(args.output_path, f'output_mean_base.wav'), output_base.cpu().squeeze(0), base_sample_rate)
|
||||
|
||||
print("Performing SR diffusion..")
|
||||
output_shape = (1, 1, output_base.shape[-1] * (sr_sample_rate // base_sample_rate))
|
||||
sr_diffusion = sr_diffusion.cuda()
|
||||
output = diffuser.p_sample_loop(sr_diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
|
||||
model_kwargs={'tokens': aligned_codes.unsqueeze(0),
|
||||
'conditioning_input': sr_cond.unsqueeze(0),
|
||||
'lr_input': output_base})
|
||||
sr_diffusion = sr_diffusion.cpu()
|
||||
torchaudio.save(os.path.join(args.output_path, f'{p}_output_mean_sr.wav'), output.cpu().squeeze(0), sr_sample_rate)
|
||||
print("Performing SR diffusion..")
|
||||
output_shape = (1, 1, output_base.shape[-1] * (sr_sample_rate // base_sample_rate))
|
||||
sr_diffusion = sr_diffusion.cuda()
|
||||
output = sr_diffuser.p_sample_loop(sr_diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
|
||||
model_kwargs={'tokens': aligned_codes,
|
||||
'conditioning_input': sr_cond.unsqueeze(0),
|
||||
'lr_input': output_base})
|
||||
sr_diffusion = sr_diffusion.cpu()
|
||||
torchaudio.save(os.path.join(args.output_path, f'output_mean_sr.wav'), output.cpu().squeeze(0), sr_sample_rate)
|
||||
|
|
|
|||
|
|
@ -299,7 +299,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_encoder_build_ctc_alignments.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_encoder_build_ctc_alignments_medium/train_encoder_build_ctc_alignments.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user