DL-Art-School/codes/trainer/eval/audio_diffusion_fid.py

295 lines
17 KiB
Python

import os
import os.path as osp
import torch
import torchaudio
from pytorch_fid.fid_score import calculate_frechet_distance
from torch import distributed
from tqdm import tqdm
from transformers import Wav2Vec2ForCTC
import torch.nn.functional as F
import numpy as np
import trainer.eval.evaluator as evaluator
from data.audio.paired_voice_audio_dataset import load_tsv_aligned_codes
from data.audio.unsupervised_audio_dataset import load_audio
from data.audio.voice_tokenizer import VoiceBpeTokenizer
from models.clip.mel_text_clip import MelTextCLIP
from models.audio.tts.tacotron2 import text_to_sequence
from scripts.audio.gen.speech_synthesis_utils import load_discrete_vocoder_diffuser, wav_to_mel, load_speech_dvae, \
convert_mel_to_codes, load_univnet_vocoder, wav_to_univnet_mel
from trainer.injectors.audio_injectors import denormalize_tacotron_mel
from utils.util import ceil_multiple, opt_get
class AudioDiffusionFid(evaluator.Evaluator):
"""
Evaluator produces generate from a diffusion model, then uses a CLIP model to judge the similarity between text & speech.
This evaluator is kind of a mess. It has been repeatedly modified to work with several different model types, which
means it is bloated beyond belief. I would not recommend attempting to understand what is going on here.
"""
def __init__(self, model, opt_eval, env):
super().__init__(model, opt_eval, env, uses_all_ddp=True)
self.real_path = opt_eval['eval_tsv']
self.data = load_tsv_aligned_codes(self.real_path)
if distributed.is_initialized() and distributed.get_world_size() > 1:
self.skip = distributed.get_world_size() # One batch element per GPU.
else:
self.skip = 1
diffusion_steps = opt_get(opt_eval, ['diffusion_steps'], 50)
diffusion_schedule = opt_get(env['opt'], ['steps', 'generator', 'injectors', 'diffusion', 'beta_schedule', 'schedule_name'], None)
if diffusion_schedule is None:
print("Unable to infer diffusion schedule from master options. Getting it from eval (or guessing).")
diffusion_schedule = opt_get(opt_eval, ['diffusion_schedule'], 'cosine')
conditioning_free_diffusion_enabled = opt_get(opt_eval, ['conditioning_free'], False)
conditioning_free_k = opt_get(opt_eval, ['conditioning_free_k'], 1)
self.diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_steps, schedule=diffusion_schedule,
enable_conditioning_free_guidance=conditioning_free_diffusion_enabled,
conditioning_free_k=conditioning_free_k)
self.dev = self.env['device']
mode = opt_get(opt_eval, ['diffusion_type'], 'tts')
self.local_modules = {}
if mode == 'tts':
self.diffusion_fn = self.perform_diffusion_tts
elif mode == 'original_vocoder':
self.local_modules['dvae'] = load_speech_dvae().cpu()
self.diffusion_fn = self.perform_original_diffusion_vocoder
elif mode == 'vocoder':
self.local_modules['dvae'] = load_speech_dvae().cpu()
self.diffusion_fn = self.perform_diffusion_vocoder
elif 'tts9_mel' in mode:
mel_means, self.mel_max, self.mel_min, mel_stds, mel_vars = torch.load('../experiments/univnet_mel_norms.pth')
self.bpe_tokenizer = VoiceBpeTokenizer('../experiments/bpe_lowercase_asr_256.json')
self.local_modules['dvae'] = load_speech_dvae().cpu()
self.local_modules['vocoder'] = load_univnet_vocoder().cpu()
self.diffusion_fn = self.perform_diffusion_tts9_mel_from_codes
if mode == 'tts9_mel_autoin':
self.local_modules['autoregressive'] = load_model_from_config("../experiments/train_gpt_tts_unified.yml",
model_name='gpt',
also_load_savepoint=False,
load_path='../experiments/unified_large_diverse_basis.pth',
device=torch.device('cpu')).cuda().eval()
self.tts9_codegen = self.tts9_get_autoregressive_codes
else:
self.tts9_codegen = self.tts9_get_dvae_codes
def perform_diffusion_tts(self, audio, codes, text, sample_rate=5500):
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)
padding_added = padded_size - output_size
padding_needed_for_codes = padding_added // aligned_codes_compression_factor
if padding_needed_for_codes > 0:
codes = F.pad(codes, (0, padding_needed_for_codes))
output_shape = (1, 1, padded_size)
gen = self.diffuser.p_sample_loop(self.model, output_shape,
model_kwargs={'tokens': codes.unsqueeze(0),
'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.local_modules['dvae'], mel)
back_to_mel = self.local_modules['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.local_modules['dvae'], mel)
text_codes = text_to_sequence(text)
real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
output_size = real_resampled.shape[-1]
aligned_codes_compression_factor = output_size // mel_codes.shape[-1]
padded_size = ceil_multiple(output_size, 2048)
padding_added = padded_size - output_size
padding_needed_for_codes = padding_added // aligned_codes_compression_factor
if padding_needed_for_codes > 0:
mel_codes = F.pad(mel_codes, (0, padding_needed_for_codes))
output_shape = (1, 1, padded_size)
gen = self.diffuser.p_sample_loop(self.model, output_shape,
model_kwargs={'tokens': mel_codes,
'conditioning_input': audio.unsqueeze(0),
'unaligned_input': torch.tensor(text_codes, device=audio.device).unsqueeze(0)})
return gen, real_resampled, sample_rate
def tts9_get_autoregressive_codes(self, mel, text):
mel_codes = convert_mel_to_codes(self.local_modules['dvae'], mel)
text_codes = torch.LongTensor(self.bpe_tokenizer.encode(text)).unsqueeze(0).to(mel.device)
cond_inputs = mel.unsqueeze(1)
auto_latents = self.local_modules['autoregressive'].forward(cond_inputs, text_codes,
torch.tensor([text_codes.shape[-1]], device=mel.device),
mel_codes,
torch.tensor([mel_codes.shape[-1]], device=mel.device),
text_first=True, raw_mels=None, return_latent=True,
clip_inputs=False)
return auto_latents
def tts9_get_dvae_codes(self, mel, text):
return convert_mel_to_codes(self.local_modules['dvae'], mel)
def perform_diffusion_tts9_mel_from_codes(self, audio, codes, text):
SAMPLE_RATE = 24000
mel = wav_to_mel(audio)
mel_codes = self.tts9_codegen(mel, text)
real_resampled = torchaudio.functional.resample(audio, 22050, SAMPLE_RATE).unsqueeze(0)
univnet_mel = wav_to_univnet_mel(real_resampled, do_normalization=False) # to be used for a conditioning input, but also guides output shape.
output_shape = univnet_mel.shape
gen_mel = self.diffuser.p_sample_loop(self.model, output_shape,
model_kwargs={'aligned_conditioning': mel_codes,
'conditioning_input': univnet_mel})
# denormalize mel
gen_mel = denormalize_tacotron_mel(gen_mel)
gen_wav = self.local_modules['vocoder'].inference(gen_mel)
real_dec = self.local_modules['vocoder'].inference(univnet_mel)
return gen_wav.float(), real_dec, 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
path for the time being.
"""
model = MelTextCLIP(dim_text=512, dim_latent=512, dim_speech=512, num_text_tokens=148, text_enc_depth=8,
text_seq_len=400, text_heads=8, speech_enc_depth=10, speech_heads=8, speech_seq_len=1000,
text_mask_percentage=.15, voice_mask_percentage=.15)
weights = torch.load('../experiments/clip_text_to_voice_for_speech_fid.pth')
model.load_state_dict(weights)
return model
def project(self, projector, sample, sample_rate):
sample = torchaudio.functional.resample(sample, sample_rate, 22050)
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()
proj2 = proj2.cpu().numpy()
mu1 = np.mean(proj1, axis=0)
mu2 = np.mean(proj2, axis=0)
sigma1 = np.cov(proj1, rowvar=False)
sigma2 = np.cov(proj2, rowvar=False)
return torch.tensor(calculate_frechet_distance(mu1, sigma1, mu2, sigma2))
def perform_eval(self):
save_path = osp.join(self.env['base_path'], "../", "audio_eval", str(self.env["step"]))
os.makedirs(save_path, exist_ok=True)
projector = self.load_projector().to(self.env['device'])
projector.eval()
w2v = self.load_w2v().to(self.env['device'])
w2v.eval()
for k, mod in self.local_modules.items():
self.local_modules[k] = mod.to(self.env['device'])
# 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)
self.model.eval()
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)
codes = codes.to(self.dev)
sample, ref, sample_rate = self.diffusion_fn(audio, codes, text)
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()
torch.set_rng_state(rng_state)
# Put modules used for evaluation back into CPU memory.
for k, mod in self.local_modules.items():
self.local_modules[k] = mod.cpu()
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
# 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_diffusion_tts_mel_flat_autoregressive_inputs.yml', 'generator',
also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\tts_flat_autoregressive_inputs_r2_initial\\models\\500_generator.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': 'tts9_mel_autoin'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 561, 'device': 'cuda', 'opt': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())