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More inference improvements

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This commit is contained in:
James Betker 2021-12-19 09:01:19 -07:00
parent f9c45d70f0
commit b4ddcd7111
2 changed files with 9 additions and 9 deletions
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8
codes/models/gpt_voice/gpt_tts_hf.py
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@ -108,8 +108,8 @@ class GptTtsHf(nn.Module):
loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
return loss_text.mean(), loss_mel.mean(), mel_logits
def inference(self, text_inputs, cond_inputs, do_sample=False, temperature=1.0, num_beams=8):
text_inputs, cond_inputs = torch.load("debug_text_and_cond.pt")
def inference(self, text_inputs, cond_inputs, do_sample=False, temperature=1.0, num_beams=8, repetition_penalty=1):
#text_inputs, cond_inputs = torch.load("debug_text_and_cond.pt")
if not hasattr(self, 'inference_model'):
self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.mel_pos_embedding, self.final_norm, self.mel_head)
@ -134,8 +134,8 @@ class GptTtsHf(nn.Module):
fake_inputs[:,-1] = self.START_MEL_TOKEN
gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.START_MEL_TOKEN, pad_token_id=self.STOP_MEL_TOKEN, eos_token_id=self.STOP_MEL_TOKEN,
max_length=emb.shape[1]+self.max_mel_tokens, temperature=temperature, num_beams=num_beams, use_cache=True)
return gen[:, fake_inputs.shape[1]:]
max_length=emb.shape[1]+self.max_mel_tokens, temperature=temperature, num_beams=num_beams, use_cache=True, repetition_penalty=repetition_penalty)
return gen[:, fake_inputs.shape[1]:-1]
@register_model

10
codes/scripts/audio/gen/use_gpt_tts.py
View File

@ -46,9 +46,9 @@ if __name__ == '__main__':
parser.add_argument('-dvae_model_name', type=str, help='Name of the DVAE model in opt.', default='dvae')
parser.add_argument('-opt_gpt_tts', type=str, help='Path to options YAML file used to train the GPT-TTS model', default='X:\\dlas\\experiments\\train_gpt_tts.yml')
parser.add_argument('-gpt_tts_model_name', type=str, help='Name of the GPT TTS model in opt.', default='gpt')
parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts\\models\\32000_gpt.pth')
parser.add_argument('-text', type=str, help='Text to speak.', default="I'm a language model that has learned to speak.")
parser.add_argument('-cond_path', type=str, help='Folder containing conditioning samples.', default='Z:\\clips\\books1\\3042_18_Holden__000000000')
parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts\\models\\48000_gpt.pth')
parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
parser.add_argument('-cond_path', type=str, help='Folder containing conditioning samples.', default='Y:\\clips\\podcasts-0\\8816_20210511-Pay Taxes Less Frequently_ We\'re Interested')
parser.add_argument('-num_cond', type=int, help='Number of conditioning samples to load.', default=3)
args = parser.parse_args()
@ -63,7 +63,7 @@ if __name__ == '__main__':
conds, cond_wav = load_conditioning_candidates(args.cond_path, args.num_cond)
print("Performing GPT inference..")
codes = gpt.inference(text, conds, num_beams=32)
codes = gpt.inference(text, conds, num_beams=32, repetition_penalty=10.0)
# Delete the GPT TTS model to free up GPU memory
del gpt
@ -72,7 +72,7 @@ if __name__ == '__main__':
dvae = load_model_from_config(args.opt_diffuse, args.dvae_model_name)
print("Loading Diffusion Model..")
diffusion = load_model_from_config(args.opt_diffuse, args.diffusion_model_name, also_load_savepoint=False, load_path=args.diffusion_model_path)
diffuser = load_discrete_vocoder_diffuser()
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=50)
print("Performing vocoding..")
wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, codes, cond_wav, spectrogram_compression_factor=128, plt_spec=False)