import os import os.path as osp from glob import glob from random import shuffle import numpy as np import torch import torchaudio import torchvision from pytorch_fid.fid_score import calculate_frechet_distance from torch import distributed from tqdm import tqdm import trainer.eval.evaluator as evaluator from data.audio.unsupervised_audio_dataset import load_audio from models.audio.mel2vec import ContrastiveTrainingWrapper from models.audio.music.unet_diffusion_waveform_gen import DiffusionWaveformGen from models.clip.contrastive_audio import ContrastiveAudio from models.diffusion.gaussian_diffusion import get_named_beta_schedule from models.diffusion.respace import space_timesteps, SpacedDiffusion from trainer.injectors.audio_injectors import denormalize_mel, TorchMelSpectrogramInjector, pixel_shuffle_1d, \ normalize_mel from utils.music_utils import get_music_codegen, get_mel2wav_model from utils.util import opt_get, load_model_from_config 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.spec_decoder = get_mel2wav_model() self.projector = ContrastiveAudio(model_dim=512, transformer_heads=8, dropout=0, encoder_depth=8, mel_channels=256) self.projector.load_state_dict(torch.load('../experiments/music_eval_projector.pth', map_location=torch.device('cpu'))) self.local_modules = {'spec_decoder': self.spec_decoder, 'projector': self.projector} if mode == 'spec_decode': self.diffusion_fn = self.perform_diffusion_spec_decode elif 'gap_fill_' in mode: self.diffusion_fn = self.perform_diffusion_gap_fill if '_freq' in mode: self.gap_gen_fn = self.gen_freq_gap else: self.gap_gen_fn = self.gen_time_gap elif 'rerender' in mode: self.diffusion_fn = self.perform_rerender elif 'from_codes' == mode: self.diffusion_fn = self.perform_diffusion_from_codes self.local_modules['codegen'] = get_music_codegen() self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 22000, 'normalize': True, 'in': 'in', 'out': 'out'}, {}) self.spec_100_fn = TorchMelSpectrogramInjector({'n_mel_channels': 100, 'mel_fmax': 22000, 'normalize': True, 'in': 'in', 'out': 'out'}, {}) def load_data(self, path): return list(glob(f'{path}/*.wav')) def perform_diffusion_spec_decode(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 audio = audio.unsqueeze(0) output_shape = (1, 16, audio.shape[-1] // 16) mel = self.spec_fn({'in': audio})['out'] gen = self.diffuser.p_sample_loop(self.model, output_shape, noise=torch.zeros(*output_shape, device=audio.device), model_kwargs={'aligned_conditioning': mel}) gen = pixel_shuffle_1d(gen, 16) return gen, real_resampled, normalize_mel(self.spec_fn({'in': gen})['out']), normalize_mel(mel), sample_rate def gen_freq_gap(self, mel, band_range=(60,100)): gap_start, gap_end = band_range mask = torch.ones_like(mel) mask[:, gap_start:gap_end] = 0 return mel * mask, mask def gen_time_gap(self, mel): mask = torch.ones_like(mel) mask[:, :, 86*4:86*6] = 0 return mel * mask, mask def perform_diffusion_gap_fill(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 audio = audio.unsqueeze(0) # Fetch the MEL and mask out the requested bands. mel = self.spec_fn({'in': audio})['out'] mel = normalize_mel(mel) mel, mask = self.gap_gen_fn(mel) # Repair the MEL with the given model. spec = self.diffuser.p_sample_loop_with_guidance(self.model, mel, mask, model_kwargs={'truth': mel}) spec = denormalize_mel(spec) # Re-convert the resulting MEL back into audio using the spectrogram decoder. output_shape = (1, 16, audio.shape[-1] // 16) self.spec_decoder = self.spec_decoder.to(audio.device) # Cool fact: we can re-use the diffuser for the spectrogram diffuser since it has the same parametrization. gen = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, noise=torch.zeros(*output_shape, device=audio.device), model_kwargs={'aligned_conditioning': spec}) gen = pixel_shuffle_1d(gen, 16) return gen, real_resampled, normalize_mel(spec), mel, sample_rate def perform_rerender(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 audio = audio.unsqueeze(0) # Fetch the MEL and mask out the requested bands. mel = self.spec_fn({'in': audio})['out'] mel = normalize_mel(mel) segments = [(0,10),(10,25),(25,45),(45,60),(60,80),(80,100),(100,130),(130,170),(170,210),(210,256)] shuffle(segments) spec = mel for i, segment in enumerate(segments): mel, mask = self.gen_freq_gap(mel, band_range=segment) # Repair the MEL with the given model. spec = self.diffuser.p_sample_loop_with_guidance(self.model, spec, mask, model_kwargs={'truth': spec}) torchvision.utils.save_image((spec.unsqueeze(1) + 1) / 2, f"{i}_rerender.png") spec = denormalize_mel(spec) # Re-convert the resulting MEL back into audio using the spectrogram decoder. output_shape = (1, 16, audio.shape[-1] // 16) self.spec_decoder = self.spec_decoder.to(audio.device) # Cool fact: we can re-use the diffuser for the spectrogram diffuser since it has the same parametrization. gen = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, noise=torch.zeros(*output_shape, device=audio.device), model_kwargs={'aligned_conditioning': spec}) gen = pixel_shuffle_1d(gen, 16) return gen, real_resampled, normalize_mel(spec), mel, sample_rate def perform_diffusion_from_codes(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 audio = audio.unsqueeze(0) # Fetch the MEL and mask out the requested bands. mel = self.spec_fn({'in': audio})['out'] codegen = self.local_modules['codegen'].to(mel.device) codes = codegen.get_codes(mel) mel100 = self.spec_100_fn({'in': audio})['out'] mel100_norm = normalize_mel(mel100) precomputed_codes, precomputed_cond = self.model.timestep_independent(codes=codes, conditioning_input=mel100_norm[:,:,:112], expected_seq_len=mel100_norm.shape[-1], return_code_pred=False) gen_mel = self.diffuser.p_sample_loop(self.model, mel100_norm.shape, model_kwargs={'precomputed_code_embeddings': precomputed_codes, 'precomputed_cond_embeddings': precomputed_cond}) #gen_mel_denorm = denormalize_mel(gen_mel) #output_shape = (1,16,audio.shape[-1]//16) #self.spec_decoder = self.spec_decoder.to(audio.device) #gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, model_kwargs={'aligned_conditioning': gen_mel_denorm}) #gen_wav = pixel_shuffle_1d(gen_wav, 16) #return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate return real_resampled.unsqueeze(0), real_resampled, gen_mel, mel100_norm, sample_rate def project(self, sample, sample_rate): sample = torchaudio.functional.resample(sample, sample_rate, 22050) mel = self.spec_fn({'in': sample})['out'] projection = self.projector.project(mel) return projection.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) self.projector = self.projector.to(self.dev) self.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() 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) audio = audio[:, :22050*10] sample, ref, sample_mel, ref_mel, sample_rate = self.diffusion_fn(audio) gen_projections.append(self.project(sample, sample_rate).cpu()) # Store on CPU to avoid wasting GPU memory. real_projections.append(self.project(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) torchvision.utils.save_image((sample_mel.unsqueeze(1) + 1) / 2, os.path.join(save_path, f"{self.env['rank']}_{i}_gen_mel.png")) torchvision.utils.save_image((ref_mel.unsqueeze(1) + 1) / 2, os.path.join(save_path, f"{self.env['rank']}_{i}_real_mel.png")) 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() 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_diffusion_flat.yml', 'generator', also_load_savepoint=False, load_path='X:\\dlas\\experiments\\train_music_diffusion_flat\\models\\33000_generator_ema.pth').cuda() opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 100, 'conditioning_free': False, 'conditioning_free_k': 1, 'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes'} env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 25, 'device': 'cuda', 'opt': {}} eval = MusicDiffusionFid(diffusion, opt_eval, env) print(eval.perform_eval())