import os
import os.path as osp
import torch
import torchaudio
import torchvision
from pytorch_fid import fid_score
from pytorch_fid.fid_score import calculate_frechet_distance
from torch import distributed
from tqdm import tqdm
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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 models.clip.mel_text_clip import MelTextCLIP
from models.tacotron2.text import sequence_to_text, 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
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.
"""
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')
if mode == 'tts':
self.diffusion_fn = self.perform_diffusion_tts
elif mode == 'vocoder':
self.dvae = load_speech_dvae()
self.dvae.eval()
self.diffusion_fn = self.perform_diffusion_vocoder
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_diffusion_vocoder(self, audio, codes, text, sample_rate=5500):
mel = wav_to_mel(audio)
mel_codes = convert_mel_to_codes(self.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 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 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()
if hasattr(self, 'dvae'):
self.dvae = self.dvae.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 = []
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())
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)
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()
self.model.train()
if hasattr(self, 'dvae'):
self.dvae = self.dvae.to('cpu')
torch.set_rng_state(rng_state)
return {"frechet_distance": frechet_distance}
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()
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': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())