diff --git a/codes/trainer/eval/music_diffusion_fid.py b/codes/trainer/eval/music_diffusion_fid.py index cc4890ec..53eb9d51 100644 --- a/codes/trainer/eval/music_diffusion_fid.py +++ b/codes/trainer/eval/music_diffusion_fid.py @@ -37,7 +37,7 @@ class MusicDiffusionFid(evaluator.Evaluator): self.data = self.load_data(self.real_path) self.clip = opt_get(opt_eval, ['clip_audio'], True) # Recommend setting true for more efficient eval passes. self.ddim = opt_get(opt_eval, ['use_ddim'], False) - self.causal = opt_get(opt_eval, ['causal'], True) + self.causal = opt_get(opt_eval, ['causal'], False) self.causal_slope = opt_get(opt_eval, ['causal_slope'], 1) if distributed.is_initialized() and distributed.get_world_size() > 1: self.skip = distributed.get_world_size() # One batch element per GPU. @@ -84,11 +84,18 @@ class MusicDiffusionFid(evaluator.Evaluator): self.cheater_decoder_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon', model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000), conditioning_free=True, conditioning_free_k=1) + self.spectral_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [16]), model_mean_type='epsilon', + model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000), + conditioning_free=False, conditioning_free_k=1) self.spec_decoder = get_mel2wav_v3_model() # The only reason the other functions don't use v3 is because earlier models were trained with v1 and I want to keep metrics consistent. self.local_modules['spec_decoder'] = self.spec_decoder elif 'from_ar_prior' == mode: self.diffusion_fn = self.perform_diffusion_from_codes_ar_prior self.local_modules['cheater_encoder'] = get_cheater_encoder() + self.local_modules['cheater_decoder'] = get_cheater_decoder() + self.cheater_decoder_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon', + model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000), + conditioning_free=True, conditioning_free_k=1) self.kmeans_inj = KmeansQuantizerInjector({'centroids': '../experiments/music_k_means_centroids.pth', 'in': 'in', 'out': 'out'}, {}) self.local_modules['ar_prior'] = get_ar_prior() self.spec_decoder = get_mel2wav_v3_model() @@ -215,7 +222,6 @@ class MusicDiffusionFid(evaluator.Evaluator): return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate def perform_reconstruction_from_cheater_gen(self, audio, sample_rate=22050): - #assert self.ddim, "DDIM mode expected for reconstructing cheater gen. Do you like to waste resources??" audio = audio.unsqueeze(0) mel = self.spec_fn({'in': audio})['out'] @@ -236,18 +242,17 @@ class MusicDiffusionFid(evaluator.Evaluator): output_shape = (1,16,audio.shape[-1]//16) self.spec_decoder = self.spec_decoder.to(audio.device) gen_mel_denorm = denormalize_mel(gen_mel) - gen_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape, + gen_wav = self.spectral_diffuser.ddim_sample_loop(self.spec_decoder, output_shape, model_kwargs={'codes': gen_mel_denorm}) gen_wav = pixel_shuffle_1d(gen_wav, 16) - real_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape, + real_wav = self.spectral_diffuser.ddim_sample_loop(self.spec_decoder, output_shape, model_kwargs={'codes': mel}) real_wav = pixel_shuffle_1d(real_wav, 16) return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate def perform_diffusion_from_codes_ar_prior(self, audio, sample_rate=22050): - assert self.ddim, "DDIM mode expected for reconstructing cheater gen. Do you like to waste resources??" audio = audio.unsqueeze(0) mel = self.spec_fn({'in': audio})['out'] @@ -256,17 +261,24 @@ class MusicDiffusionFid(evaluator.Evaluator): cheater_codes = self.kmeans_inj({'in': cheater})['out'] ar_latent = self.local_modules['ar_prior'].to(audio.device)(cheater_codes, cheater, return_latent=True) - gen_mel = self.diffuser.ddim_sample_loop(self.model, mel_norm.shape, model_kwargs={'codes': ar_latent}, progress=True) + # 1. Generate the cheater latent using the input as a reference. + sampler = self.diffuser.ddim_sample_loop if self.ddim else self.diffuser.p_sample_loop + gen_cheater = sampler(self.model, cheater.shape, progress=True, + causal=self.causal, causal_slope=self.causal_slope, + model_kwargs={'codes': ar_latent}) + # 2. Decode the cheater into a MEL + gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,gen_cheater.shape[-1]*16), progress=True, + model_kwargs={'codes': gen_cheater.permute(0,2,1)}) gen_mel_denorm = denormalize_mel(gen_mel) + + # 3. Decode into waveform. output_shape = (1,16,audio.shape[-1]//16) self.spec_decoder = self.spec_decoder.to(audio.device) - gen_wav = self.spectral_diffuser.ddim_sample_loop(self.spec_decoder, output_shape, - model_kwargs={'codes': gen_mel_denorm}) + gen_wav = self.spectral_diffuser.ddim_sample_loop(self.spec_decoder, output_shape, model_kwargs={'codes': gen_mel_denorm}) gen_wav = pixel_shuffle_1d(gen_wav, 16) - real_wav = self.spectral_diffuser.ddim_sample_loop(self.spec_decoder, output_shape, - model_kwargs={'codes': mel}) + real_wav = self.spectral_diffuser.ddim_sample_loop(self.spec_decoder, output_shape, model_kwargs={'codes': mel}) real_wav = pixel_shuffle_1d(real_wav, 16) return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate @@ -424,16 +436,23 @@ class MusicDiffusionFid(evaluator.Evaluator): if __name__ == '__main__': diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_cheater_gen.yml', 'generator', also_load_savepoint=False, - load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v5_causal_retrain\\models\\22000_generator_ema.pth' + load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v5_causal_retrain\\models\\53000_generator_ema.pth' ).cuda() opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :) #'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety. - 'diffusion_steps': 100, + 'diffusion_steps': 220, # basis: 192 'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': False, 'diffusion_schedule': 'linear', 'diffusion_type': 'cheater_gen', - 'causal': True, 'causal_slope': 1, + # Slope 1: 1.03x, 2: 1.06, 4: 1.135, 8: 1.27, 16: 1.54 + 'causal': True, 'causal_slope': 3, # DONT FORGET TO INCREMENT THE STEP! #'partial_low': 128, 'partial_high': 192 } - env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 236, 'device': 'cuda', 'opt': {}} + env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 3, 'device': 'cuda', 'opt': {}} eval = MusicDiffusionFid(diffusion, opt_eval, env) - print(eval.perform_eval()) + fds = [] + for i in range(2): + res = eval.perform_eval() + print(res) + fds.append(res['frechet_distance']) + print(fds) +