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') self.diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_steps, schedule=diffusion_schedule) 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: frechet_distance = distributed.all_reduce(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\\5500_generator_ema.pth').cuda() opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 50, 'diffusion_schedule': 'linear', 'diffusion_type': 'vocoder'} env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 500, 'device': 'cuda', 'opt': {}} eval = AudioDiffusionFid(diffusion, opt_eval, env) print(eval.perform_eval())