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This commit is contained in:
James Betker 2022-02-22 23:12:58 -07:00
parent 03752c1cd6
commit 58f6c9805b
2 changed files with 39 additions and 8 deletions
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6
codes/scripts/audio/gen/speech_synthesis_utils.py
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@ -9,6 +9,12 @@ from models.diffusion.gaussian_diffusion import get_named_beta_schedule
from models.diffusion.respace import SpacedDiffusion, space_timesteps from models.diffusion.respace import SpacedDiffusion, space_timesteps
from trainer.injectors.audio_injectors import TorchMelSpectrogramInjector from trainer.injectors.audio_injectors import TorchMelSpectrogramInjector
from utils.audio import plot_spectrogram from utils.audio import plot_spectrogram
from utils.util import load_model_from_config
def load_speech_dvae():
return load_model_from_config("X:\\dlas\\experiments\\train_diffusion_vocoder_22k_level.yml",
"dvae").cuda()
def wav_to_mel(wav, mel_norms_file='../experiments/clips_mel_norms.pth'): def wav_to_mel(wav, mel_norms_file='../experiments/clips_mel_norms.pth'):

41
codes/trainer/eval/audio_diffusion_fid.py
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@ -15,14 +15,15 @@ import trainer.eval.evaluator as evaluator
from data.audio.paired_voice_audio_dataset import load_tsv_aligned_codes from data.audio.paired_voice_audio_dataset import load_tsv_aligned_codes
from data.audio.unsupervised_audio_dataset import load_audio from data.audio.unsupervised_audio_dataset import load_audio
from models.clip.mel_text_clip import MelTextCLIP from models.clip.mel_text_clip import MelTextCLIP
from scripts.audio.gen.speech_synthesis_utils import load_discrete_vocoder_diffuser, wav_to_mel from models.tacotron2.text import sequence_to_text
from scripts.audio.gen.speech_synthesis_utils import load_discrete_vocoder_diffuser, wav_to_mel, load_speech_dvae, \
convert_mel_to_codes
from utils.util import ceil_multiple, opt_get from utils.util import ceil_multiple, opt_get
class AudioDiffusionFid(evaluator.Evaluator): class AudioDiffusionFid(evaluator.Evaluator):
""" """
Evaluator produces generate from a diffusion model, then uses a pretrained wav2vec model to compute a frechet Evaluator produces generate from a diffusion model, then uses a CLIP model to judge the similarity between text & speech.
distance between real and fake samples.
""" """
def __init__(self, model, opt_eval, env): def __init__(self, model, opt_eval, env):
super().__init__(model, opt_eval, env, uses_all_ddp=True) super().__init__(model, opt_eval, env, uses_all_ddp=True)
@ -39,8 +40,14 @@ class AudioDiffusionFid(evaluator.Evaluator):
diffusion_schedule = opt_get(opt_eval, ['diffusion_schedule'], 'cosine') diffusion_schedule = opt_get(opt_eval, ['diffusion_schedule'], 'cosine')
self.diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_steps, schedule=diffusion_schedule) self.diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_steps, schedule=diffusion_schedule)
self.dev = self.env['device'] self.dev = self.env['device']
mode = opt_get(opt_eval, ['diffusion_type'], 'tts')
if mode == 'tts':
self.diffusion_fn = self.perform_diffusion_tts
elif mode == 'vocoder':
self.dvae = load_speech_dvae()
self.diffusion_fn = self.perform_diffusion_vocoder
def perform_diffusion(self, audio, codes, 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)
aligned_codes_compression_factor = sample_rate * 221 // 11025 aligned_codes_compression_factor = sample_rate * 221 // 11025
output_size = codes.shape[-1]*aligned_codes_compression_factor output_size = codes.shape[-1]*aligned_codes_compression_factor
@ -55,6 +62,23 @@ class AudioDiffusionFid(evaluator.Evaluator):
'conditioning_input': real_resampled}) 'conditioning_input': real_resampled})
return gen, real_resampled, sample_rate return gen, real_resampled, sample_rate
def perform_dvae_diffusion(self, audio, codes, text, sample_rate=5500):
mel = wav_to_mel(audio)
mel_codes = convert_mel_to_codes(self.dvae, mel)
text_codes = sequence_to_text(text)
real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
aligned_codes_compression_factor = sample_rate * 221 // 11025
output_size = codes.shape[-1]*aligned_codes_compression_factor
padded_size = ceil_multiple(output_size, 2048)
output_shape = (1, 1, padded_size)
gen = self.diffuser.p_sample_loop(self.model, output_shape,
model_kwargs={'tokens': mel_codes.unsqueeze(0),
'conditioning_input': real_resampled,
'unaligned_input': text_codes})
return gen, real_resampled, 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
@ -101,7 +125,7 @@ class AudioDiffusionFid(evaluator.Evaluator):
path, text, codes = self.data[i + self.env['rank']] path, text, codes = self.data[i + self.env['rank']]
audio = load_audio(path, 22050).to(self.dev) audio = load_audio(path, 22050).to(self.dev)
codes = codes.to(self.dev) codes = codes.to(self.dev)
sample, ref, sample_rate = self.perform_diffusion(audio, codes) sample, ref, sample_rate = self.perform_diffusion_fn(audio, codes, text)
gen_projections.append(self.project(projector, sample).cpu(), sample_rate) # Store on CPU to avoid wasting GPU memory. gen_projections.append(self.project(projector, sample).cpu(), sample_rate) # Store on CPU to avoid wasting GPU memory.
real_projections.append(self.project(projector, ref).cpu(), sample_rate) real_projections.append(self.project(projector, ref).cpu(), sample_rate)
@ -124,9 +148,10 @@ class AudioDiffusionFid(evaluator.Evaluator):
if __name__ == '__main__': if __name__ == '__main__':
from utils.util import load_model_from_config from utils.util import load_model_from_config
diffusion = load_model_from_config('X:\\dlas\\experiments\\sweep_diffusion_tts6\\baseline\\train_diffusion_tts6.yml', 'generator', 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\\sweep_diffusion_tts6\\baseline\\models\\102000_generator_ema.pth').cuda() also_load_savepoint=False, load_path='X:\\dlas\\experiments\\train_diffusion_tts7_dvae_thin_with_text\\models\\12500_generator_ema.pth').cuda()
opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 50, 'diffusion_schedule': 'linear'} opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 50,
'diffusion_schedule': 'linear', 'diffusion_type': 'vocoder'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 500, 'device': 'cuda', 'opt': {}} env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 500, 'device': 'cuda', 'opt': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env) eval = AudioDiffusionFid(diffusion, opt_eval, env)
eval.perform_eval() eval.perform_eval()