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adf for ar-latent tfd

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This commit is contained in:
James Betker 2022-06-15 16:41:08 -06:00
parent 157d5d56c3
commit 34d9d5f202
1 changed files with 35 additions and 3 deletions
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38
codes/trainer/eval/audio_diffusion_fid.py
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@ -91,6 +91,15 @@ class AudioDiffusionFid(evaluator.Evaluator):
elif 'tfd' == mode:
self.diffusion_fn = self.perform_diffusion_tfd
self.local_modules['vocoder'] = load_univnet_vocoder().cpu()
elif 'tfd_ar' == mode:
self.local_modules['dvae'] = load_speech_dvae().cpu()
self.local_modules['autoregressive'] = load_model_from_config("../experiments/train_gpt_tts_unified.yml",
model_name='gpt',
also_load_savepoint=False,
load_path='../experiments/tortoise_ar.pth',
device=torch.device('cpu')).cuda().eval()
self.diffusion_fn = self.perform_diffusion_tfd_ar_prior
self.local_modules['vocoder'] = load_univnet_vocoder().cpu()
def perform_diffusion_tts(self, audio, codes, text, sample_rate=5500):
real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
@ -219,6 +228,29 @@ class AudioDiffusionFid(evaluator.Evaluator):
real_dec = self.local_modules['vocoder'].inference(denormalize_mel(umel))
return gen_wav.float(), real_dec, gen_mel, umel, SAMPLE_RATE
def perform_diffusion_tfd_ar_prior(self, audio, codes, text):
SAMPLE_RATE = 24000
audio_resampled = torchaudio.functional.resample(audio, 22050, SAMPLE_RATE).unsqueeze(0)
vmel = wav_to_mel(audio)
umel = wav_to_univnet_mel(audio_resampled, do_normalization=True)
mel_codes = convert_mel_to_codes(self.local_modules['dvae'], vmel)
text_codes = torch.LongTensor(self.bpe_tokenizer.encode(text)).unsqueeze(0).to(vmel.device)
cond_inputs = pad_or_truncate(vmel, 132300//256).unsqueeze(1)
mlc = self.local_modules['autoregressive'].mel_length_compression
auto_latents = self.local_modules['autoregressive'](cond_inputs, text_codes,
torch.tensor([text_codes.shape[-1]], device=vmel.device),
mel_codes,
torch.tensor([mel_codes.shape[-1]*mlc], device=vmel.device),
text_first=True, raw_mels=None, return_latent=True)
gen_mel = self.diffuser.p_sample_loop(self.model, umel.shape,
model_kwargs={'codes': auto_latents})
gen_wav = self.local_modules['vocoder'].inference(denormalize_mel(gen_mel))
real_dec = self.local_modules['vocoder'].inference(denormalize_mel(umel))
return gen_wav.float(), real_dec, gen_mel, umel, SAMPLE_RATE
def load_projector(self):
"""
Builds the CLIP model used to project speech into a latent. This model has fixed parameters and a fixed loading
@ -335,12 +367,12 @@ if __name__ == '__main__':
if __name__ == '__main__':
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_tts_diffusion_tfd11_quant.yml', 'generator',
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_tts_diffusion_tfd12_ar_inputs.yml', 'generator',
also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\train_tts_diffusion_tfd12_linear_dvae\\models\\12000_generator.pth').cuda()
load_path='X:\\dlas\\experiments\\train_tts_diffusion_tfd12_ar_inputs_pretrain\\models\\4500_generator.pth').cuda()
opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 50,
'conditioning_free': False, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'tfd'}
'diffusion_schedule': 'linear', 'diffusion_type': 'tfd_ar'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 101, 'device': 'cuda', 'opt': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())