256 lines
13 KiB
Python
256 lines
13 KiB
Python
import os
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import os.path as osp
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from glob import glob
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from random import shuffle
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import numpy as np
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import torch
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import torchaudio
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import torchvision
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from pytorch_fid.fid_score import calculate_frechet_distance
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from torch import distributed
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from tqdm import tqdm
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import trainer.eval.evaluator as evaluator
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from data.audio.unsupervised_audio_dataset import load_audio
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from models.audio.mel2vec import ContrastiveTrainingWrapper
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from models.audio.music.unet_diffusion_waveform_gen import DiffusionWaveformGen
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from models.clip.contrastive_audio import ContrastiveAudio
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from models.diffusion.gaussian_diffusion import get_named_beta_schedule
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from models.diffusion.respace import space_timesteps, SpacedDiffusion
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from trainer.injectors.audio_injectors import denormalize_mel, TorchMelSpectrogramInjector, pixel_shuffle_1d, \
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normalize_mel
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from utils.music_utils import get_music_codegen, get_mel2wav_model
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from utils.util import opt_get, load_model_from_config
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class MusicDiffusionFid(evaluator.Evaluator):
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"""
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Evaluator produces generate from a music diffusion model.
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"""
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def __init__(self, model, opt_eval, env):
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super().__init__(model, opt_eval, env, uses_all_ddp=True)
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self.real_path = opt_eval['path']
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self.data = self.load_data(self.real_path)
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if distributed.is_initialized() and distributed.get_world_size() > 1:
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self.skip = distributed.get_world_size() # One batch element per GPU.
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else:
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self.skip = 1
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diffusion_steps = opt_get(opt_eval, ['diffusion_steps'], 50)
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diffusion_schedule = opt_get(env['opt'], ['steps', 'generator', 'injectors', 'diffusion', 'beta_schedule', 'schedule_name'], None)
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if diffusion_schedule is None:
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print("Unable to infer diffusion schedule from master options. Getting it from eval (or guessing).")
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diffusion_schedule = opt_get(opt_eval, ['diffusion_schedule'], 'linear')
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conditioning_free_diffusion_enabled = opt_get(opt_eval, ['conditioning_free'], False)
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conditioning_free_k = opt_get(opt_eval, ['conditioning_free_k'], 1)
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self.diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [diffusion_steps]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule(diffusion_schedule, 4000),
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conditioning_free=conditioning_free_diffusion_enabled, conditioning_free_k=conditioning_free_k)
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self.dev = self.env['device']
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mode = opt_get(opt_eval, ['diffusion_type'], 'tts')
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self.spec_decoder = get_mel2wav_model()
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self.projector = ContrastiveAudio(model_dim=512, transformer_heads=8, dropout=0, encoder_depth=8, mel_channels=256)
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self.projector.load_state_dict(torch.load('../experiments/music_eval_projector.pth', map_location=torch.device('cpu')))
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self.local_modules = {'spec_decoder': self.spec_decoder, 'projector': self.projector}
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if mode == 'spec_decode':
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self.diffusion_fn = self.perform_diffusion_spec_decode
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elif 'gap_fill_' in mode:
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self.diffusion_fn = self.perform_diffusion_gap_fill
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if '_freq' in mode:
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self.gap_gen_fn = self.gen_freq_gap
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else:
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self.gap_gen_fn = self.gen_time_gap
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elif 'rerender' in mode:
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self.diffusion_fn = self.perform_rerender
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elif 'from_codes' == mode:
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self.diffusion_fn = self.perform_diffusion_from_codes
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self.local_modules['codegen'] = get_music_codegen()
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self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 22000, 'normalize': True, 'in': 'in', 'out': 'out'}, {})
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def load_data(self, path):
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return list(glob(f'{path}/*.wav'))
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def perform_diffusion_spec_decode(self, audio, sample_rate=22050):
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if sample_rate != sample_rate:
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real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
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else:
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real_resampled = audio
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audio = audio.unsqueeze(0)
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output_shape = (1, 16, audio.shape[-1] // 16)
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mel = self.spec_fn({'in': audio})['out']
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gen = self.diffuser.p_sample_loop(self.model, output_shape, noise=torch.zeros(*output_shape, device=audio.device),
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model_kwargs={'aligned_conditioning': mel})
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gen = pixel_shuffle_1d(gen, 16)
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return gen, real_resampled, normalize_mel(self.spec_fn({'in': gen})['out']), normalize_mel(mel), sample_rate
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def gen_freq_gap(self, mel, band_range=(60,100)):
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gap_start, gap_end = band_range
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mask = torch.ones_like(mel)
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mask[:, gap_start:gap_end] = 0
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return mel * mask, mask
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def gen_time_gap(self, mel):
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mask = torch.ones_like(mel)
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mask[:, :, 86*4:86*6] = 0
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return mel * mask, mask
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def perform_diffusion_gap_fill(self, audio, sample_rate=22050):
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if sample_rate != sample_rate:
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real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
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else:
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real_resampled = audio
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audio = audio.unsqueeze(0)
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# Fetch the MEL and mask out the requested bands.
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mel = self.spec_fn({'in': audio})['out']
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mel = normalize_mel(mel)
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mel, mask = self.gap_gen_fn(mel)
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# Repair the MEL with the given model.
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spec = self.diffuser.p_sample_loop_with_guidance(self.model, mel, mask, model_kwargs={'truth': mel})
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spec = denormalize_mel(spec)
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# Re-convert the resulting MEL back into audio using the spectrogram decoder.
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output_shape = (1, 16, audio.shape[-1] // 16)
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self.spec_decoder = self.spec_decoder.to(audio.device)
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# Cool fact: we can re-use the diffuser for the spectrogram diffuser since it has the same parametrization.
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gen = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, noise=torch.zeros(*output_shape, device=audio.device),
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model_kwargs={'aligned_conditioning': spec})
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gen = pixel_shuffle_1d(gen, 16)
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return gen, real_resampled, normalize_mel(spec), mel, sample_rate
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def perform_rerender(self, audio, sample_rate=22050):
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if sample_rate != sample_rate:
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real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
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else:
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real_resampled = audio
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audio = audio.unsqueeze(0)
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# Fetch the MEL and mask out the requested bands.
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mel = self.spec_fn({'in': audio})['out']
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mel = normalize_mel(mel)
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segments = [(0,10),(10,25),(25,45),(45,60),(60,80),(80,100),(100,130),(130,170),(170,210),(210,256)]
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shuffle(segments)
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spec = mel
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for i, segment in enumerate(segments):
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mel, mask = self.gen_freq_gap(mel, band_range=segment)
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# Repair the MEL with the given model.
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spec = self.diffuser.p_sample_loop_with_guidance(self.model, spec, mask, model_kwargs={'truth': spec})
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torchvision.utils.save_image((spec.unsqueeze(1) + 1) / 2, f"{i}_rerender.png")
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spec = denormalize_mel(spec)
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# Re-convert the resulting MEL back into audio using the spectrogram decoder.
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output_shape = (1, 16, audio.shape[-1] // 16)
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self.spec_decoder = self.spec_decoder.to(audio.device)
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# Cool fact: we can re-use the diffuser for the spectrogram diffuser since it has the same parametrization.
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gen = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, noise=torch.zeros(*output_shape, device=audio.device),
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model_kwargs={'aligned_conditioning': spec})
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gen = pixel_shuffle_1d(gen, 16)
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return gen, real_resampled, normalize_mel(spec), mel, sample_rate
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def perform_diffusion_from_codes(self, audio, sample_rate=22050):
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if sample_rate != sample_rate:
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real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
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else:
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real_resampled = audio
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audio = audio.unsqueeze(0)
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# Fetch the MEL and mask out the requested bands.
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mel = self.spec_fn({'in': audio})['out']
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codegen = self.local_modules['codegen'].to(mel.device)
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codes = codegen.get_codes(mel)
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mel_norm = normalize_mel(mel)
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precomputed_codes, precomputed_cond = self.model.timestep_independent(codes=codes, conditioning_input=mel_norm[:,:,:112],
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expected_seq_len=mel_norm.shape[-1], return_code_pred=False)
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gen_mel = self.diffuser.p_sample_loop(self.model, mel_norm.shape, noise=torch.zeros_like(mel_norm),
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model_kwargs={'precomputed_code_embeddings': precomputed_codes, 'precomputed_cond_embeddings': precomputed_cond})
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gen_mel_denorm = denormalize_mel(gen_mel)
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output_shape = (1,16,audio.shape[-1]//16)
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self.spec_decoder = self.spec_decoder.to(audio.device)
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gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, model_kwargs={'aligned_conditioning': gen_mel_denorm})
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gen_wav = pixel_shuffle_1d(gen_wav, 16)
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return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate
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def project(self, sample, sample_rate):
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sample = torchaudio.functional.resample(sample, sample_rate, 22050)
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mel = self.spec_fn({'in': sample})['out']
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projection = self.projector.project(mel)
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return projection.squeeze(0) # Getting rid of the batch dimension means it's just [hidden_dim]
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def compute_frechet_distance(self, proj1, proj2):
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# I really REALLY FUCKING HATE that this is going to numpy. Why does "pytorch_fid" operate in numpy land. WHY?
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proj1 = proj1.cpu().numpy()
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proj2 = proj2.cpu().numpy()
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mu1 = np.mean(proj1, axis=0)
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mu2 = np.mean(proj2, axis=0)
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sigma1 = np.cov(proj1, rowvar=False)
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sigma2 = np.cov(proj2, rowvar=False)
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return torch.tensor(calculate_frechet_distance(mu1, sigma1, mu2, sigma2))
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def perform_eval(self):
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save_path = osp.join(self.env['base_path'], "../", "audio_eval", str(self.env["step"]))
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os.makedirs(save_path, exist_ok=True)
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self.projector = self.projector.to(self.dev)
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self.projector.eval()
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# Attempt to fix the random state as much as possible. RNG state will be restored before returning.
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rng_state = torch.get_rng_state()
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torch.manual_seed(5)
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self.model.eval()
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with torch.no_grad():
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gen_projections = []
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real_projections = []
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for i in tqdm(list(range(0, len(self.data), self.skip))):
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path = self.data[i + self.env['rank']]
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audio = load_audio(path, 22050).to(self.dev)
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audio = audio[:, :22050*10]
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sample, ref, sample_mel, ref_mel, sample_rate = self.diffusion_fn(audio)
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gen_projections.append(self.project(sample, sample_rate).cpu()) # Store on CPU to avoid wasting GPU memory.
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real_projections.append(self.project(ref, sample_rate).cpu())
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torchaudio.save(os.path.join(save_path, f"{self.env['rank']}_{i}_gen.wav"), sample.squeeze(0).cpu(), sample_rate)
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torchaudio.save(os.path.join(save_path, f"{self.env['rank']}_{i}_real.wav"), ref.cpu(), sample_rate)
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torchvision.utils.save_image((sample_mel.unsqueeze(1) + 1) / 2, os.path.join(save_path, f"{self.env['rank']}_{i}_gen_mel.png"))
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torchvision.utils.save_image((ref_mel.unsqueeze(1) + 1) / 2, os.path.join(save_path, f"{self.env['rank']}_{i}_real_mel.png"))
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gen_projections = torch.stack(gen_projections, dim=0)
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real_projections = torch.stack(real_projections, dim=0)
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frechet_distance = torch.tensor(self.compute_frechet_distance(gen_projections, real_projections), device=self.env['device'])
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if distributed.is_initialized() and distributed.get_world_size() > 1:
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distributed.all_reduce(frechet_distance)
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frechet_distance = frechet_distance / distributed.get_world_size()
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self.model.train()
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torch.set_rng_state(rng_state)
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# Put modules used for evaluation back into CPU memory.
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for k, mod in self.local_modules.items():
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self.local_modules[k] = mod.cpu()
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return {"frechet_distance": frechet_distance}
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if __name__ == '__main__':
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diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_flat.yml', 'generator',
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also_load_savepoint=False,
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load_path='X:\\dlas\\experiments\\train_music_diffusion_flat\\models\\33000_generator_ema.pth').cuda()
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opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 100,
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'conditioning_free': False, 'conditioning_free_k': 1,
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'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes'}
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env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 25, 'device': 'cuda', 'opt': {}}
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eval = MusicDiffusionFid(diffusion, opt_eval, env)
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print(eval.perform_eval())
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