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add tfd audio diffusion

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This commit is contained in:
James Betker 2022-06-12 13:59:22 -06:00
parent 11e70dde14
commit a3da7f186e
1 changed files with 21 additions and 5 deletions
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26
codes/trainer/eval/audio_diffusion_fid.py
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@ -88,7 +88,9 @@ class AudioDiffusionFid(evaluator.Evaluator):
self.tts9_codegen = self.tts9_get_autoregressive_codes self.tts9_codegen = self.tts9_get_autoregressive_codes
else: else:
self.tts9_codegen = self.tts9_get_dvae_codes self.tts9_codegen = self.tts9_get_dvae_codes
elif 'tfd' == mode:
self.diffusion_fn = self.perform_diffusion_tfd
self.local_modules['vocoder'] = load_univnet_vocoder().cpu()
def perform_diffusion_tts(self, audio, codes, text, sample_rate=5500): def perform_diffusion_tts(self, audio, codes, text, sample_rate=5500):
real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0) real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
@ -203,6 +205,22 @@ class AudioDiffusionFid(evaluator.Evaluator):
real_dec = self.local_modules['vocoder'].inference(denormalize_mel(univnet_mel)) real_dec = self.local_modules['vocoder'].inference(denormalize_mel(univnet_mel))
return gen_wav.float(), real_dec, gen_mel, univnet_mel, SAMPLE_RATE return gen_wav.float(), real_dec, gen_mel, univnet_mel, SAMPLE_RATE
def perform_diffusion_tfd(self, audio, codes, text):
SAMPLE_RATE = 24000
audio_resampled = torchaudio.functional.resample(audio, 22050, SAMPLE_RATE).unsqueeze(0)
mel = wav_to_univnet_mel(audio_resampled, do_normalization=True)
gen_mel = self.diffuser.p_sample_loop(self.model, mel.shape,
model_kwargs={'truth_mel': mel,
'conditioning_input': None,
'disable_diversity': True})
# denormalize mel
gen_mel = denormalize_mel(gen_mel)
gen_wav = self.local_modules['vocoder'].inference(gen_mel)
real_dec = self.local_modules['vocoder'].inference(mel)
return gen_wav.float(), real_dec, SAMPLE_RATE
def load_projector(self): def load_projector(self):
""" """
Builds the CLIP model used to project speech into a latent. This model has fixed parameters and a fixed loading Builds the CLIP model used to project speech into a latent. This model has fixed parameters and a fixed loading
@ -319,14 +337,12 @@ if __name__ == '__main__':
if __name__ == '__main__': if __name__ == '__main__':
# 34k; no conditioning_free: {'frechet_distance': tensor(1.4559, device='cuda:0', dtype=torch.float64), 'intelligibility_loss': tensor(151.9112, device='cuda:0')}
# 34k; conditioning_free: {'frechet_distance': tensor(1.4059, device='cuda:0', dtype=torch.float64), 'intelligibility_loss': tensor(118.3377, device='cuda:0')}
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_speech_diffusion_from_ctc_und10\\train.yml', 'generator', diffusion = load_model_from_config('X:\\dlas\\experiments\\train_speech_diffusion_from_ctc_und10\\train.yml', 'generator',
also_load_savepoint=False, also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\train_speech_diffusion_from_ctc_und10\\models\\43000_generator_ema.pth').cuda() load_path='X:\\dlas\\experiments\\train_speech_diffusion_from_ctc_und10\\models\\43000_generator_ema.pth').cuda()
opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-oco-realtext.tsv', 'diffusion_steps': 100, opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-oco-realtext.tsv', 'diffusion_steps': 100,
'conditioning_free': False, 'conditioning_free_k': 1, 'conditioning_free': False, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'ctc_to_mel'} 'diffusion_schedule': 'linear', 'diffusion_type': 'tfd'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 223, 'device': 'cuda', 'opt': {}} env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 100, 'device': 'cuda', 'opt': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env) eval = AudioDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval()) print(eval.perform_eval())