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add support for the original vocoder to audio_diffusion_fid; also add a new "intelligibility" metric

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James Betker 2022-03-08 15:53:27 -07:00
parent 3e5da71b16
commit c4e4cf91a0
1 changed files with 77 additions and 7 deletions
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84
codes/trainer/eval/audio_diffusion_fid.py
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@ -47,6 +47,10 @@ class AudioDiffusionFid(evaluator.Evaluator):
mode = opt_get(opt_eval, ['diffusion_type'], 'tts')
if mode == 'tts':
self.diffusion_fn = self.perform_diffusion_tts
elif mode == 'original_vocoder':
self.dvae = load_speech_dvae().to(self.env['device'])
self.dvae.eval()
self.diffusion_fn = self.perform_original_diffusion_vocoder
elif mode == 'vocoder':
self.dvae = load_speech_dvae().to(self.env['device'])
self.dvae.eval()
@ -67,6 +71,31 @@ class AudioDiffusionFid(evaluator.Evaluator):
'conditioning_input': real_resampled})
return gen, real_resampled, sample_rate
def perform_original_diffusion_vocoder(self, audio, codes, text, sample_rate=11025):
mel = wav_to_mel(audio)
mel_codes = convert_mel_to_codes(self.dvae, mel)
back_to_mel = self.dvae.decode(mel_codes)[0]
orig_audio = audio
real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
output_size = real_resampled.shape[-1]
aligned_mel_compression_factor = output_size // back_to_mel.shape[-1]
padded_size = ceil_multiple(output_size, 2048)
padding_added = padded_size - output_size
padding_needed_for_codes = padding_added // aligned_mel_compression_factor
if padding_needed_for_codes > 0:
back_to_mel = F.pad(back_to_mel, (0, padding_needed_for_codes))
output_shape = (1, 1, padded_size)
gen = self.diffuser.p_sample_loop(self.model, output_shape,
model_kwargs={'spectrogram': back_to_mel,
'conditioning_input': orig_audio.unsqueeze(0)})
# Pop it back down to 5.5kHz for an accurate comparison with the other diffusers.
real_resampled = torchaudio.functional.resample(real_resampled.squeeze(0), sample_rate, 5500).unsqueeze(0)
gen = torchaudio.functional.resample(gen.squeeze(0), sample_rate, 5500).unsqueeze(0)
return gen, real_resampled, 5500
def perform_diffusion_vocoder(self, audio, codes, text, sample_rate=5500):
mel = wav_to_mel(audio)
mel_codes = convert_mel_to_codes(self.dvae, mel)
@ -104,6 +133,25 @@ class AudioDiffusionFid(evaluator.Evaluator):
mel = wav_to_mel(sample)
return projector.get_speech_projection(mel).squeeze(0) # Getting rid of the batch dimension means it's just [hidden_dim]
def load_w2v(self):
return Wav2Vec2ForCTC.from_pretrained("jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli")
def intelligibility_loss(self, w2v, sample, real_sample, sample_rate, real_text):
"""
Measures the differences between CTC losses using wav2vec2 against the real sample and the generated sample.
"""
text_codes = torch.tensor(text_to_sequence(real_text), device=sample.device)
results = []
for s in [sample, real_sample]:
s = torchaudio.functional.resample(s, sample_rate, 16000)
norm_s = (s - s.mean()) / torch.sqrt(s.var() + 1e-7)
norm_s = norm_s.squeeze(1)
loss = w2v(input_values=norm_s, labels=text_codes).loss
results.append(loss)
gen_loss, real_loss = results
return gen_loss - real_loss
def compute_frechet_distance(self, proj1, proj2):
# I really REALLY FUCKING HATE that this is going to numpy. Why does "pytorch_fid" operate in numpy land. WHY?
proj1 = proj1.cpu().numpy()
@ -123,6 +171,9 @@ class AudioDiffusionFid(evaluator.Evaluator):
if hasattr(self, 'dvae'):
self.dvae = self.dvae.to(self.env['device'])
w2v = self.load_w2v().to(self.env['device'])
w2v.eval()
# Attempt to fix the random state as much as possible. RNG state will be restored before returning.
rng_state = torch.get_rng_state()
torch.manual_seed(5)
@ -131,6 +182,7 @@ class AudioDiffusionFid(evaluator.Evaluator):
with torch.no_grad():
gen_projections = []
real_projections = []
intelligibility_losses = []
for i in tqdm(list(range(0, len(self.data), self.skip))):
path, text, codes = self.data[i + self.env['rank']]
audio = load_audio(path, 22050).to(self.dev)
@ -139,33 +191,51 @@ class AudioDiffusionFid(evaluator.Evaluator):
gen_projections.append(self.project(projector, sample, sample_rate).cpu()) # Store on CPU to avoid wasting GPU memory.
real_projections.append(self.project(projector, ref, sample_rate).cpu())
intelligibility_losses.append(self.intelligibility_loss(w2v, sample, ref, sample_rate, text))
torchaudio.save(os.path.join(save_path, f"{self.env['rank']}_{i}_gen.wav"), sample.squeeze(0).cpu(), sample_rate)
torchaudio.save(os.path.join(save_path, f"{self.env['rank']}_{i}_real.wav"), ref.squeeze(0).cpu(), sample_rate)
gen_projections = torch.stack(gen_projections, dim=0)
real_projections = torch.stack(real_projections, dim=0)
intelligibility_loss = torch.stack(intelligibility_losses, dim=0).mean()
frechet_distance = torch.tensor(self.compute_frechet_distance(gen_projections, real_projections), device=self.env['device'])
if distributed.is_initialized() and distributed.get_world_size() > 1:
distributed.all_reduce(frechet_distance)
frechet_distance = frechet_distance / distributed.get_world_size()
distributed.all_reduce(intelligibility_loss)
intelligibility_loss = intelligibility_loss / distributed.get_world_size()
self.model.train()
if hasattr(self, 'dvae'):
self.dvae = self.dvae.to('cpu')
torch.set_rng_state(rng_state)
return {"frechet_distance": frechet_distance}
return {"frechet_distance": frechet_distance, "intelligibility_loss": intelligibility_loss}
"""
if __name__ == '__main__':
from utils.util import load_model_from_config
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts7_dvae_thin_with_text.yml', 'generator',
also_load_savepoint=False, load_path='X:\\dlas\\experiments\\train_diffusion_tts7_dvae_thin_with_text\\models\\56500_generator_ema.pth').cuda()
opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 100,
'conditioning_free': False, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'vocoder'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 2, 'device': 'cuda', 'opt': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())
"""
if __name__ == '__main__':
from utils.util import load_model_from_config
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts7_dvae_thin_with_text.yml', 'generator',
also_load_savepoint=False, load_path='X:\\dlas\\experiments\\train_diffusion_tts7_dvae_thin_with_text\\models\\47500_generator_ema.pth').cuda()
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_vocoder_clips_from_dvae_archived_r3_b256_conditioning\\config.yml', 'generator',
also_load_savepoint=False, load_path='X:\\dlas\\experiments\\train_diffusion_vocoder_clips_from_dvae_archived_r3_b256_conditioning\\models\\80800_generator_ema.pth').cuda()
opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 100,
'conditioning_free': True, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'vocoder'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 1, 'device': 'cuda', 'opt': {}}
'conditioning_free': False, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'original_vocoder'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 4, 'device': 'cuda', 'opt': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())
print(eval.perform_eval())