music diffusion fid
This commit is contained in:
James Betker
parent
084b1c1527
commit
b1c2c48720
150
codes/trainer/eval/music_diffusion_fid.py
Normal file
150
codes/trainer/eval/music_diffusion_fid.py
Normal file
|
|
@ -0,0 +1,150 @@
|
|||
import os
|
||||
import os.path as osp
|
||||
from glob import glob
|
||||
|
||||
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 models.diffusion.gaussian_diffusion import get_named_beta_schedule
|
||||
from models.diffusion.respace import space_timesteps, SpacedDiffusion
|
||||
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, TorchMelSpectrogramInjector
|
||||
from utils.util import ceil_multiple, opt_get, load_model_from_config, pad_or_truncate
|
||||
|
||||
|
||||
class MusicDiffusionFid(evaluator.Evaluator):
|
||||
"""
|
||||
Evaluator produces generate from a music diffusion model.
|
||||
"""
|
||||
def __init__(self, model, opt_eval, env):
|
||||
super().__init__(model, opt_eval, env, uses_all_ddp=True)
|
||||
self.real_path = opt_eval['path']
|
||||
self.data = self.load_data(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'], 'linear')
|
||||
conditioning_free_diffusion_enabled = opt_get(opt_eval, ['conditioning_free'], False)
|
||||
conditioning_free_k = opt_get(opt_eval, ['conditioning_free_k'], 1)
|
||||
self.diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [diffusion_steps]), model_mean_type='epsilon',
|
||||
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule(diffusion_schedule, 4000),
|
||||
conditioning_free=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 == 'standard':
|
||||
self.diffusion_fn = self.perform_diffusion_standard
|
||||
self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 120, 'mel_fmax': 11000, 'in': 'in', 'out': 'out'}, {})
|
||||
|
||||
def load_data(self, path):
|
||||
return list(glob(f'{path}/*.wav'))
|
||||
|
||||
def perform_diffusion_standard(self, audio, sample_rate=22050):
|
||||
if sample_rate != sample_rate:
|
||||
real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
|
||||
else:
|
||||
real_resampled = audio
|
||||
mel = self.spec_fn({'in': real_resampled})['out']
|
||||
output_shape = (1, 1, mel.shape[-1] * 256)
|
||||
gen = self.diffuser.p_sample_loop(self.model, output_shape, model_kwargs={'aligned_conditioning': mel})
|
||||
return gen, real_resampled, sample_rate
|
||||
|
||||
def load_projector(self):
|
||||
# TODO: implement for music.
|
||||
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):
|
||||
# TODO: implement for music.
|
||||
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()
|
||||
|
||||
# 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()
|
||||
|
||||
frechet_distance = 0
|
||||
with torch.no_grad():
|
||||
gen_projections = []
|
||||
real_projections = []
|
||||
for i in tqdm(list(range(0, len(self.data), self.skip))):
|
||||
path = self.data[i + self.env['rank']]
|
||||
audio = load_audio(path, 22050).to(self.dev)
|
||||
sample, ref, sample_rate = self.diffusion_fn(audio)
|
||||
|
||||
#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.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()
|
||||
# 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}
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_waveform_gen.yml', 'generator',
|
||||
also_load_savepoint=False,
|
||||
load_path='X:\\dlas\\experiments\\train_music_waveform_gen\\models\\36000_generator_ema.pth').cuda()
|
||||
opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 50,
|
||||
'conditioning_free': False, 'conditioning_free_k': 1,
|
||||
'diffusion_schedule': 'linear', 'diffusion_type': 'standard'}
|
||||
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 1, 'device': 'cuda', 'opt': {}}
|
||||
eval = MusicDiffusionFid(diffusion, opt_eval, env)
|
||||
print(eval.perform_eval())
|
||||
Loading…
Reference in New Issue
Block a user