diff --git a/codes/trainer/eval/music_diffusion_fid.py b/codes/trainer/eval/music_diffusion_fid.py index 014f1a19..456d938f 100644 --- a/codes/trainer/eval/music_diffusion_fid.py +++ b/codes/trainer/eval/music_diffusion_fid.py @@ -69,14 +69,6 @@ class MusicDiffusionFid(evaluator.Evaluator): elif 'from_codes' == mode: self.diffusion_fn = self.perform_diffusion_from_codes self.local_modules['codegen'] = get_music_codegen() - elif 'from_codes_quant' == mode: - self.diffusion_fn = self.perform_diffusion_from_codes_quant - elif 'partial_from_codes_quant' == mode: - self.diffusion_fn = functools.partial(self.perform_partial_diffusion_from_codes_quant, - partial_low=opt_eval['partial_low'], - partial_high=opt_eval['partial_high']) - elif 'from_codes_quant_gradual_decode' == mode: - self.diffusion_fn = self.perform_diffusion_from_codes_quant_gradual_decode elif 'cheater_gen' == mode: self.diffusion_fn = self.perform_reconstruction_from_cheater_gen self.local_modules['cheater_encoder'] = get_cheater_encoder() @@ -140,87 +132,6 @@ class MusicDiffusionFid(evaluator.Evaluator): return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate - def perform_diffusion_from_codes_quant(self, audio, sample_rate=22050): - real_resampled = audio - audio = audio.unsqueeze(0) - - mel = self.spec_fn({'in': audio})['out'] - mel_norm = normalize_mel(mel) - #def denoising_fn(x): - # q9 = torch.quantile(x, q=.95, dim=-1).unsqueeze(-1) - # s = q9.clamp(1, 9999999999) - # x = x.clamp(-s, s) / s - # return x - gen_mel = self.diffuser.p_sample_loop(self.model, mel_norm.shape, #denoised_fn=denoising_fn, clip_denoised=False, - model_kwargs={'truth_mel': mel_norm, - 'conditioning_input': mel_norm, - 'disable_diversity': True}) - - 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.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape, - model_kwargs={'aligned_conditioning': gen_mel_denorm}) - gen_wav = pixel_shuffle_1d(gen_wav, 16) - - real_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape, - model_kwargs={'aligned_conditioning': mel}) - real_wav = pixel_shuffle_1d(real_wav, 16) - - return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate - - def perform_partial_diffusion_from_codes_quant(self, audio, sample_rate=22050, partial_low=0, partial_high=256): - real_resampled = audio - audio = audio.unsqueeze(0) - - mel = self.spec_fn({'in': audio})['out'] - mel_norm = normalize_mel(mel) - mask = torch.ones_like(mel_norm) - mask[:, partial_low:partial_high] = 0 # This is the channel region that the model will predict. - gen_mel = self.diffuser.p_sample_loop_with_guidance(self.model, - guidance_input=mel_norm, mask=mask, - model_kwargs={'truth_mel': mel, - 'conditioning_input': torch.zeros_like(mel_norm[:,:,:390]), - 'disable_diversity': True}) - - 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.spectral_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 - - def perform_diffusion_from_codes_quant_gradual_decode(self, audio, sample_rate=22050): - real_resampled = audio - audio = audio.unsqueeze(0) - - mel = self.spec_fn({'in': audio})['out'] - mel_norm = normalize_mel(mel) - guidance = torch.zeros_like(mel_norm) - mask = torch.zeros_like(mel_norm) - GRADS = 4 - for k in range(GRADS): - gen_mel = self.diffuser.p_sample_loop_with_guidance(self.model, - guidance_input=guidance, mask=mask, - model_kwargs={'truth_mel': mel, - 'conditioning_input': torch.zeros_like(mel_norm[:,:,:390]), - 'disable_diversity': True}) - pk = int(k*(mel_norm.shape[1]/GRADS)) - ek = int((k+1)*(mel_norm.shape[1]/GRADS)) - guidance[:, pk:ek] = gen_mel[:, pk:ek] - mask[:, :ek] = 1 - - 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 - def perform_reconstruction_from_cheater_gen(self, audio, sample_rate=22050): audio = audio.unsqueeze(0) @@ -285,87 +196,6 @@ class MusicDiffusionFid(evaluator.Evaluator): return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate - def perform_fake_ar_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'] - mel_norm = normalize_mel(mel) - cheater = self.local_modules['cheater_encoder'].to(audio.device)(mel_norm) - - # 1. Generate the cheater latent using the input as a reference. - def diffuse(i, ref): - mask = torch.zeros_like(ref) - mask[:,:,:i] = 1 - return self.diffuser.p_sample_loop_with_guidance(self.model, ref, mask, model_kwargs={'conditioning_input': cheater}) - gen_cheater = torch.randn_like(cheater) - for i in range(cheater.shape[-1]): - gen_cheater = diffuse(i, gen_cheater) - if i > 128: - # abort early. - gen_cheater = gen_cheater[:,:,:128] - break - - # 2. Decode the cheater into a MEL. This operation and the next need to be chunked to make them feasible to perform within GPU memory. - chunks = torch.split(gen_cheater, 64, dim=-1) - gen_wavs = [] - for chunk in tqdm(chunks): - gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,chunk.shape[-1]*16), progress=True, - model_kwargs={'codes': chunk.permute(0,2,1)}) - - # 3. And then the MEL back into a spectrogram - output_shape = (1,16,audio.shape[-1]//(16*len(chunks))) - 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, - model_kwargs={'codes': gen_mel_denorm}) - gen_wav = pixel_shuffle_1d(gen_wav, 16) - gen_wavs.append(gen_wav) - gen_wav = torch.cat(gen_wavs, dim=-1) - - """ How to do progressive, causal decoding of the TFD diffuser: - MAX_CONTEXT = 64 - def diffuse(start, len, guidance): - mask = torch.zeros_like(guidance) - mask[:,:,:(len-start)] = 1 - return self.cheater_decoder_diffuser.p_sample_loop_with_guidance(self.local_modules['cheater_decoder'].diff.to(audio.device), - guidance_input=guidance, mask=mask, - model_kwargs={'codes': gen_cheater[:,:,start:start+MAX_CONTEXT].permute(0,2,1)}) - guidance_mel = torch.zeros((1,256,MAX_CONTEXT*16), device=mel.device) - gen_mel = torch.zeros((1,256,0), device=mel.device) - for i in tqdm(list(range(gen_cheater.shape[-1]))): - start = max(0, i-MAX_CONTEXT-1) - l = min(16*(MAX_CONTEXT-1), i*16) - ngm = diffuse(start, l, guidance_mel) - gen_mel = torch.cat([gen_mel, ngm[:,:,l:l+16]], dim=-1) - if gen_mel.shape[-1] < guidance_mel.shape[-1]: - guidance_mel[:,:,:gen_mel.shape[-1]] = gen_mel - else: - guidance_mel = gen_mel[:,:,-guidance_mel.shape[-1]:] - - chunks = torch.split(gen_mel, MAX_CONTEXT*16, dim=-1) - gen_wavs = [] - for chunk_mel in tqdm(chunks): - # 3. And then the MEL back into a spectrogram - output_shape = (1,16,audio.shape[-1]//(16*len(chunks))) - self.spec_decoder = self.spec_decoder.to(audio.device) - gen_mel_denorm = denormalize_mel(chunk_mel) - gen_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape, - model_kwargs={'codes': gen_mel_denorm}) - gen_wav = pixel_shuffle_1d(gen_wav, 16) - gen_wavs.append(gen_wav) - gen_wav = torch.cat(gen_wavs, dim=-1) - """ - - if audio.shape[-1] < 40 * 22050: - real_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape, - model_kwargs={'codes': mel}) - real_wav = pixel_shuffle_1d(real_wav, 16) - else: - real_wav = audio # TODO: chunk like above. - - return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate - def project(self, sample, sample_rate): sample = torchaudio.functional.resample(sample, sample_rate, 22050) mel = self.spec_fn({'in': sample})['out']