import pytorch_lightning as pl import sys, gc import random import torch import torchaudio import typing as tp import wandb from aeiou.viz import pca_point_cloud, audio_spectrogram_image, tokens_spectrogram_image import auraloss from ema_pytorch import EMA from einops import rearrange from safetensors.torch import save_file from torch import optim from torch.nn import functional as F from pytorch_lightning.utilities.rank_zero import rank_zero_only from ..inference.sampling import get_alphas_sigmas, sample, sample_discrete_euler from ..models.diffusion import DiffusionModelWrapper, ConditionedDiffusionModelWrapper from ..models.autoencoders import DiffusionAutoencoder from ..models.diffusion_prior import PriorType from .autoencoders import create_loss_modules_from_bottleneck from .losses import AuralossLoss, MSELoss, MultiLoss from .utils import create_optimizer_from_config, create_scheduler_from_config from time import time class Profiler: def __init__(self): self.ticks = [[time(), None]] def tick(self, msg): self.ticks.append([time(), msg]) def __repr__(self): rep = 80 * "=" + "\n" for i in range(1, len(self.ticks)): msg = self.ticks[i][1] ellapsed = self.ticks[i][0] - self.ticks[i - 1][0] rep += msg + f": {ellapsed*1000:.2f}ms\n" rep += 80 * "=" + "\n\n\n" return rep class DiffusionUncondTrainingWrapper(pl.LightningModule): ''' Wrapper for training an unconditional audio diffusion model (like Dance Diffusion). ''' def __init__( self, model: DiffusionModelWrapper, lr: float = 1e-4, pre_encoded: bool = False ): super().__init__() self.diffusion = model self.diffusion_ema = EMA( self.diffusion.model, beta=0.9999, power=3/4, update_every=1, update_after_step=1 ) self.lr = lr self.rng = torch.quasirandom.SobolEngine(1, scramble=True) loss_modules = [ MSELoss("v", "targets", weight=1.0, name="mse_loss" ) ] self.losses = MultiLoss(loss_modules) self.pre_encoded = pre_encoded def configure_optimizers(self): return optim.Adam([*self.diffusion.parameters()], lr=self.lr) def training_step(self, batch, batch_idx): reals = batch[0] if reals.ndim == 4 and reals.shape[0] == 1: reals = reals[0] diffusion_input = reals loss_info = {} if not self.pre_encoded: loss_info["audio_reals"] = diffusion_input if self.diffusion.pretransform is not None: if not self.pre_encoded: with torch.set_grad_enabled(self.diffusion.pretransform.enable_grad): diffusion_input = self.diffusion.pretransform.encode(diffusion_input) else: # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0: diffusion_input = diffusion_input / self.diffusion.pretransform.scale loss_info["reals"] = diffusion_input # Draw uniformly distributed continuous timesteps t = self.rng.draw(reals.shape[0])[:, 0].to(self.device) # Calculate the noise schedule parameters for those timesteps alphas, sigmas = get_alphas_sigmas(t) # Combine the ground truth data and the noise alphas = alphas[:, None, None] sigmas = sigmas[:, None, None] noise = torch.randn_like(diffusion_input) noised_inputs = diffusion_input * alphas + noise * sigmas targets = noise * alphas - diffusion_input * sigmas with torch.cuda.amp.autocast(): v = self.diffusion(noised_inputs, t) loss_info.update({ "v": v, "targets": targets }) loss, losses = self.losses(loss_info) log_dict = { 'train/loss': loss.detach(), 'train/std_data': diffusion_input.std(), } for loss_name, loss_value in losses.items(): log_dict[f"train/{loss_name}"] = loss_value.detach() self.log_dict(log_dict, prog_bar=True, on_step=True) return loss def on_before_zero_grad(self, *args, **kwargs): self.diffusion_ema.update() def export_model(self, path, use_safetensors=False): self.diffusion.model = self.diffusion_ema.ema_model if use_safetensors: save_file(self.diffusion.state_dict(), path) else: torch.save({"state_dict": self.diffusion.state_dict()}, path) class DiffusionUncondDemoCallback(pl.Callback): def __init__(self, demo_every=2000, num_demos=8, demo_steps=250, sample_rate=48000 ): super().__init__() self.demo_every = demo_every self.num_demos = num_demos self.demo_steps = demo_steps self.sample_rate = sample_rate self.last_demo_step = -1 @rank_zero_only @torch.no_grad() def on_train_batch_end(self, trainer, module, outputs, batch, batch_idx): if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step: return self.last_demo_step = trainer.global_step demo_samples = module.diffusion.sample_size if module.diffusion.pretransform is not None: demo_samples = demo_samples // module.diffusion.pretransform.downsampling_ratio noise = torch.randn([self.num_demos, module.diffusion.io_channels, demo_samples]).to(module.device) try: with torch.cuda.amp.autocast(): fakes = sample(module.diffusion_ema, noise, self.demo_steps, 0) if module.diffusion.pretransform is not None: fakes = module.diffusion.pretransform.decode(fakes) # Put the demos together fakes = rearrange(fakes, 'b d n -> d (b n)') log_dict = {} filename = f'demo_{trainer.global_step:08}.wav' fakes = fakes.to(torch.float32).div(torch.max(torch.abs(fakes))).mul(32767).to(torch.int16).cpu() torchaudio.save(filename, fakes, self.sample_rate) log_dict[f'demo'] = wandb.Audio(filename, sample_rate=self.sample_rate, caption=f'Reconstructed') log_dict[f'demo_melspec_left'] = wandb.Image(audio_spectrogram_image(fakes)) trainer.logger.experiment.log(log_dict) del fakes except Exception as e: print(f'{type(e).__name__}: {e}') finally: gc.collect() torch.cuda.empty_cache() class DiffusionCondTrainingWrapper(pl.LightningModule): ''' Wrapper for training a conditional audio diffusion model. ''' def __init__( self, model: ConditionedDiffusionModelWrapper, lr: float = None, mask_padding: bool = False, mask_padding_dropout: float = 0.0, use_ema: bool = True, log_loss_info: bool = True, optimizer_configs: dict = None, pre_encoded: bool = False, cfg_dropout_prob = 0.1, timestep_sampler: tp.Literal["uniform", "logit_normal"] = "uniform", ): super().__init__() self.diffusion = model if use_ema: self.diffusion_ema = EMA( self.diffusion.model, beta=0.9999, power=3/4, update_every=1, update_after_step=1, include_online_model=False ) else: self.diffusion_ema = None self.mask_padding = mask_padding self.mask_padding_dropout = mask_padding_dropout self.cfg_dropout_prob = cfg_dropout_prob self.rng = torch.quasirandom.SobolEngine(1, scramble=True) self.timestep_sampler = timestep_sampler self.diffusion_objective = model.diffusion_objective if 'av_loss' in optimizer_configs and optimizer_configs['av_loss']['if_add_av_loss']: av_align_weight = optimizer_configs['av_loss']['config']['weight'] self.loss_modules = [ MSELoss("output", "targets", weight=1.0 - av_align_weight, mask_key="padding_mask" if self.mask_padding else None, name="mse_loss" ) ] else: self.loss_modules = [ MSELoss("output", "targets", weight=1.0, mask_key="padding_mask" if self.mask_padding else None, name="mse_loss" ) ] self.losses = MultiLoss(self.loss_modules) self.log_loss_info = log_loss_info assert lr is not None or optimizer_configs is not None, "Must specify either lr or optimizer_configs in training config" if optimizer_configs is None: optimizer_configs = { "diffusion": { "optimizer": { "type": "Adam", "config": { "lr": lr } } } } else: if lr is not None: print(f"WARNING: learning_rate and optimizer_configs both specified in config. Ignoring learning_rate and using optimizer_configs.") self.optimizer_configs = optimizer_configs self.pre_encoded = pre_encoded def configure_optimizers(self): diffusion_opt_config = self.optimizer_configs['diffusion'] opt_diff = create_optimizer_from_config(diffusion_opt_config['optimizer'], self.diffusion.parameters()) if "scheduler" in diffusion_opt_config: sched_diff = create_scheduler_from_config(diffusion_opt_config['scheduler'], opt_diff) sched_diff_config = { "scheduler": sched_diff, "interval": "step" } return [opt_diff], [sched_diff_config] return [opt_diff] def training_step(self, batch, batch_idx): reals, metadata = batch p = Profiler() if reals.ndim == 4 and reals.shape[0] == 1: reals = reals[0] loss_info = {} diffusion_input = reals if not self.pre_encoded: loss_info["audio_reals"] = diffusion_input p.tick("setup") with torch.cuda.amp.autocast(): conditioning = self.diffusion.conditioner(metadata, self.device) use_padding_mask = self.mask_padding and random.random() > self.mask_padding_dropout # Create batch tensor of attention masks from the "mask" field of the metadata array if use_padding_mask: padding_masks = torch.stack([md["padding_mask"][0] for md in metadata], dim=0).to(self.device) p.tick("conditioning") if self.diffusion.pretransform is not None: self.diffusion.pretransform.to(self.device) if not self.pre_encoded: with torch.cuda.amp.autocast() and torch.set_grad_enabled(self.diffusion.pretransform.enable_grad): self.diffusion.pretransform.train(self.diffusion.pretransform.enable_grad) diffusion_input = self.diffusion.pretransform.encode(diffusion_input) p.tick("pretransform") # If mask_padding is on, interpolate the padding masks to the size of the pretransformed input if use_padding_mask: padding_masks = F.interpolate(padding_masks.unsqueeze(1).float(), size=diffusion_input.shape[2], mode="nearest").squeeze(1).bool() else: # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0: diffusion_input = diffusion_input / self.diffusion.pretransform.scale if self.timestep_sampler == "uniform": # Draw uniformly distributed continuous timesteps t = self.rng.draw(reals.shape[0])[:, 0].to(self.device) # [0.1360, 0.5232] elif self.timestep_sampler == "logit_normal": t = torch.sigmoid(torch.randn(reals.shape[0], device=self.device)) # Calculate the noise schedule parameters for those timesteps if self.diffusion_objective == "v": alphas, sigmas = get_alphas_sigmas(t) elif self.diffusion_objective == "rectified_flow": alphas, sigmas = 1-t, t # Combine the ground truth data and the noise alphas = alphas[:, None, None] sigmas = sigmas[:, None, None] noise = torch.randn_like(diffusion_input) noised_inputs = diffusion_input * alphas + noise * sigmas if self.diffusion_objective == "v": targets = noise * alphas - diffusion_input * sigmas elif self.diffusion_objective == "rectified_flow": targets = noise - diffusion_input p.tick("noise") extra_args = {} if use_padding_mask: extra_args["mask"] = padding_masks with torch.cuda.amp.autocast(): p.tick("amp") output = self.diffusion(noised_inputs, t, cond=conditioning, cfg_dropout_prob = self.cfg_dropout_prob, **extra_args) p.tick("diffusion") loss_info.update({ "output": output, "targets": targets, "padding_mask": padding_masks if use_padding_mask else None, }) loss, losses = self.losses(loss_info) p.tick("loss") if self.log_loss_info: # Loss debugging logs num_loss_buckets = 10 bucket_size = 1 / num_loss_buckets loss_all = F.mse_loss(output, targets, reduction="none") sigmas = rearrange(self.all_gather(sigmas), "b c n -> (b) c n").squeeze() # gather loss_all across all GPUs loss_all = rearrange(self.all_gather(loss_all), "b c n -> (b) c n") # Bucket loss values based on corresponding sigma values, bucketing sigma values by bucket_size loss_all = torch.stack([loss_all[(sigmas >= i) & (sigmas < i + bucket_size)].mean() for i in torch.arange(0, 1, bucket_size).to(self.device)]) # Log bucketed losses with corresponding sigma bucket values, if it's not NaN debug_log_dict = { f"model/loss_all_{i/num_loss_buckets:.1f}": loss_all[i].detach() for i in range(num_loss_buckets) if not torch.isnan(loss_all[i]) } self.log_dict(debug_log_dict) log_dict = { 'train/loss': loss.detach(), 'train/std_data': diffusion_input.std(), 'train/lr': self.trainer.optimizers[0].param_groups[0]['lr'] } for loss_name, loss_value in losses.items(): log_dict[f"train/{loss_name}"] = loss_value.detach() self.log_dict(log_dict, prog_bar=True, on_step=True) p.tick("log") #print(f"Profiler: {p}") return loss def validation_step(self, batch, batch_idx): reals, metadata = batch p = Profiler() if reals.ndim == 4 and reals.shape[0] == 1: reals = reals[0] loss_info = {} diffusion_input = reals if not self.pre_encoded: loss_info["audio_reals"] = diffusion_input p.tick("setup") with torch.cuda.amp.autocast(): conditioning = self.diffusion.conditioner(metadata, self.device) # If mask_padding is on, randomly drop the padding masks to allow for learning silence padding use_padding_mask = self.mask_padding and random.random() > self.mask_padding_dropout # Create batch tensor of attention masks from the "mask" field of the metadata array if use_padding_mask: padding_masks = torch.stack([md["padding_mask"][0] for md in metadata], dim=0).to(self.device) # Shape (batch_size, sequence_length) p.tick("conditioning") if self.diffusion.pretransform is not None: self.diffusion.pretransform.to(self.device) if not self.pre_encoded: with torch.cuda.amp.autocast() and torch.set_grad_enabled(self.diffusion.pretransform.enable_grad): self.diffusion.pretransform.train(self.diffusion.pretransform.enable_grad) diffusion_input = self.diffusion.pretransform.encode(diffusion_input) p.tick("pretransform") # If mask_padding is on, interpolate the padding masks to the size of the pretransformed input if use_padding_mask: padding_masks = F.interpolate(padding_masks.unsqueeze(1).float(), size=diffusion_input.shape[2], mode="nearest").squeeze(1).bool() else: # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0: diffusion_input = diffusion_input / self.diffusion.pretransform.scale if self.timestep_sampler == "uniform": # Draw uniformly distributed continuous timesteps t = self.rng.draw(reals.shape[0])[:, 0].to(self.device) elif self.timestep_sampler == "logit_normal": t = torch.sigmoid(torch.randn(reals.shape[0], device=self.device)) # Calculate the noise schedule parameters for those timesteps if self.diffusion_objective == "v": alphas, sigmas = get_alphas_sigmas(t) elif self.diffusion_objective == "rectified_flow": alphas, sigmas = 1-t, t # Combine the ground truth data and the noise alphas = alphas[:, None, None] sigmas = sigmas[:, None, None] noise = torch.randn_like(diffusion_input) noised_inputs = diffusion_input * alphas + noise * sigmas if self.diffusion_objective == "v": targets = noise * alphas - diffusion_input * sigmas elif self.diffusion_objective == "rectified_flow": targets = noise - diffusion_input p.tick("noise") extra_args = {} if use_padding_mask: extra_args["mask"] = padding_masks with torch.cuda.amp.autocast(): p.tick("amp") output = self.diffusion(noised_inputs, t, cond=conditioning, cfg_dropout_prob = self.cfg_dropout_prob, **extra_args) p.tick("diffusion") loss_info.update({ "output": output, "targets": targets, "padding_mask": padding_masks if use_padding_mask else None, }) loss, losses = self.losses(loss_info) p.tick("loss") if self.log_loss_info: # Loss debugging logs num_loss_buckets = 10 bucket_size = 1 / num_loss_buckets loss_all = F.mse_loss(output, targets, reduction="none") # loss_all = F.binary_cross_entropy_with_logits(output, targets, reduction="none") sigmas = rearrange(self.all_gather(sigmas), "b c n -> (b) c n").squeeze() # sigmas = rearrange(self.all_gather(sigmas), "w b c n -> (w b) c n").squeeze() # gather loss_all across all GPUs loss_all = rearrange(self.all_gather(loss_all), "b c n -> (b) c n") # loss_all = rearrange(self.all_gather(loss_all), "w b c n -> (w b) c n") # Bucket loss values based on corresponding sigma values, bucketing sigma values by bucket_size loss_all = torch.stack([loss_all[(sigmas >= i) & (sigmas < i + bucket_size)].mean() for i in torch.arange(0, 1, bucket_size).to(self.device)]) # Log bucketed losses with corresponding sigma bucket values, if it's not NaN debug_log_dict = { f"model/loss_all_{i/num_loss_buckets:.1f}": loss_all[i].detach() for i in range(num_loss_buckets) if not torch.isnan(loss_all[i]) } self.log_dict(debug_log_dict) log_dict = { 'valid/loss': loss.detach(), 'valid/std_data': diffusion_input.std(), 'valid/lr': self.trainer.optimizers[0].param_groups[0]['lr'] } for loss_name, loss_value in losses.items(): log_dict[f"valid/{loss_name}"] = loss_value.detach() self.log_dict(log_dict, prog_bar=True, on_step=True) # self.log('val_loss', val_loss, on_epoch=True, on_step=True) p.tick("log") #print(f"Profiler: {p}") return loss def on_before_zero_grad(self, *args, **kwargs): if self.diffusion_ema is not None: self.diffusion_ema.update() def export_model(self, path, use_safetensors=False): if self.diffusion_ema is not None: self.diffusion.model = self.diffusion_ema.ema_model if use_safetensors: save_file(self.diffusion.state_dict(), path) else: torch.save({"state_dict": self.diffusion.state_dict()}, path) class DiffusionCondDemoCallback(pl.Callback): def __init__(self, demo_every=2000, num_demos=8, sample_size=65536, demo_steps=250, sample_rate=48000, demo_conditioning: tp.Optional[tp.Dict[str, tp.Any]] = {}, demo_cfg_scales: tp.Optional[tp.List[int]] = [3, 5, 7], demo_cond_from_batch: bool = False, display_audio_cond: bool = False ): super().__init__() self.demo_every = demo_every self.num_demos = num_demos self.demo_samples = sample_size self.demo_steps = demo_steps self.sample_rate = sample_rate self.last_demo_step = -1 self.demo_conditioning = demo_conditioning self.demo_cfg_scales = demo_cfg_scales # If true, the callback will use the metadata from the batch to generate the demo conditioning self.demo_cond_from_batch = demo_cond_from_batch # If true, the callback will display the audio conditioning self.display_audio_cond = display_audio_cond @rank_zero_only @torch.no_grad() def on_train_batch_end(self, trainer, module: DiffusionCondTrainingWrapper, outputs, batch, batch_idx): if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step: return module.eval() print(f"Generating demo") self.last_demo_step = trainer.global_step demo_samples = self.demo_samples demo_cond = self.demo_conditioning if self.demo_cond_from_batch: # Get metadata from the batch demo_cond = batch[1][:self.num_demos] if module.diffusion.pretransform is not None: demo_samples = demo_samples // module.diffusion.pretransform.downsampling_ratio noise = torch.randn([self.num_demos, module.diffusion.io_channels, demo_samples]).to(module.device) try: print("Getting conditioning") with torch.cuda.amp.autocast(): conditioning = module.diffusion.conditioner(demo_cond, module.device) cond_inputs = module.diffusion.get_conditioning_inputs(conditioning) log_dict = {} if self.display_audio_cond: audio_inputs = torch.cat([cond["audio"] for cond in demo_cond], dim=0) audio_inputs = rearrange(audio_inputs, 'b d n -> d (b n)') filename = f'demo_audio_cond_{trainer.global_step:08}.wav' audio_inputs = audio_inputs.to(torch.float32).mul(32767).to(torch.int16).cpu() torchaudio.save(filename, audio_inputs, self.sample_rate) log_dict[f'demo_audio_cond'] = wandb.Audio(filename, sample_rate=self.sample_rate, caption="Audio conditioning") log_dict[f"demo_audio_cond_melspec_left"] = wandb.Image(audio_spectrogram_image(audio_inputs)) trainer.logger.experiment.log(log_dict) for cfg_scale in self.demo_cfg_scales: print(f"Generating demo for cfg scale {cfg_scale}") with torch.cuda.amp.autocast(): model = module.diffusion_ema.model if module.diffusion_ema is not None else module.diffusion.model if module.diffusion_objective == "v": fakes = sample(model, noise, self.demo_steps, 0, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True) elif module.diffusion_objective == "rectified_flow": fakes = sample_discrete_euler(model, noise, self.demo_steps, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True) if module.diffusion.pretransform is not None: fakes = module.diffusion.pretransform.decode(fakes) # Put the demos together fakes = rearrange(fakes, 'b d n -> d (b n)') log_dict = {} filename = f'demo_cfg_{cfg_scale}_{trainer.global_step:08}.wav' fakes = fakes.div(torch.max(torch.abs(fakes))).mul(32767).to(torch.int16).cpu() torchaudio.save(filename, fakes, self.sample_rate) log_dict[f'demo_cfg_{cfg_scale}'] = wandb.Audio(filename, sample_rate=self.sample_rate, caption=f'Reconstructed') log_dict[f'demo_melspec_left_cfg_{cfg_scale}'] = wandb.Image(audio_spectrogram_image(fakes)) trainer.logger.experiment.log(log_dict) del fakes except Exception as e: raise e finally: gc.collect() torch.cuda.empty_cache() module.train() class DiffusionCondInpaintTrainingWrapper(pl.LightningModule): ''' Wrapper for training a conditional audio diffusion model. ''' def __init__( self, model: ConditionedDiffusionModelWrapper, lr: float = 1e-4, max_mask_segments = 10, log_loss_info: bool = False, optimizer_configs: dict = None, use_ema: bool = True, pre_encoded: bool = False, cfg_dropout_prob = 0.1, timestep_sampler: tp.Literal["uniform", "logit_normal"] = "uniform", ): super().__init__() self.diffusion = model self.use_ema = use_ema if self.use_ema: self.diffusion_ema = EMA( self.diffusion.model, beta=0.9999, power=3/4, update_every=1, update_after_step=1, include_online_model=False ) else: self.diffusion_ema = None self.cfg_dropout_prob = cfg_dropout_prob self.lr = lr self.max_mask_segments = max_mask_segments self.rng = torch.quasirandom.SobolEngine(1, scramble=True) self.timestep_sampler = timestep_sampler self.diffusion_objective = model.diffusion_objective self.loss_modules = [ MSELoss("output", "targets", weight=1.0, name="mse_loss" ) ] self.losses = MultiLoss(self.loss_modules) self.log_loss_info = log_loss_info assert lr is not None or optimizer_configs is not None, "Must specify either lr or optimizer_configs in training config" if optimizer_configs is None: optimizer_configs = { "diffusion": { "optimizer": { "type": "Adam", "config": { "lr": lr } } } } else: if lr is not None: print(f"WARNING: learning_rate and optimizer_configs both specified in config. Ignoring learning_rate and using optimizer_configs.") self.optimizer_configs = optimizer_configs self.pre_encoded = pre_encoded def configure_optimizers(self): diffusion_opt_config = self.optimizer_configs['diffusion'] opt_diff = create_optimizer_from_config(diffusion_opt_config['optimizer'], self.diffusion.parameters()) if "scheduler" in diffusion_opt_config: sched_diff = create_scheduler_from_config(diffusion_opt_config['scheduler'], opt_diff) sched_diff_config = { "scheduler": sched_diff, "interval": "step" } return [opt_diff], [sched_diff_config] return [opt_diff] def random_mask(self, sequence, max_mask_length): b, _, sequence_length = sequence.size() # Create a mask tensor for each batch element masks = [] for i in range(b): mask_type = random.randint(0, 2) if mask_type == 0: # Random mask with multiple segments num_segments = random.randint(1, self.max_mask_segments) max_segment_length = max_mask_length // num_segments segment_lengths = random.sample(range(1, max_segment_length + 1), num_segments) mask = torch.ones((1, 1, sequence_length)) for length in segment_lengths: mask_start = random.randint(0, sequence_length - length) mask[:, :, mask_start:mask_start + length] = 0 elif mask_type == 1: # Full mask mask = torch.zeros((1, 1, sequence_length)) elif mask_type == 2: # Causal mask mask = torch.ones((1, 1, sequence_length)) mask_length = random.randint(1, max_mask_length) mask[:, :, -mask_length:] = 0 mask = mask.to(sequence.device) masks.append(mask) # Concatenate the mask tensors into a single tensor mask = torch.cat(masks, dim=0).to(sequence.device) # Apply the mask to the sequence tensor for each batch element masked_sequence = sequence * mask return masked_sequence, mask def training_step(self, batch, batch_idx): reals, metadata = batch p = Profiler() if reals.ndim == 4 and reals.shape[0] == 1: reals = reals[0] loss_info = {} diffusion_input = reals if not self.pre_encoded: loss_info["audio_reals"] = diffusion_input p.tick("setup") with torch.cuda.amp.autocast(): conditioning = self.diffusion.conditioner(metadata, self.device) p.tick("conditioning") if self.diffusion.pretransform is not None: self.diffusion.pretransform.to(self.device) if not self.pre_encoded: with torch.cuda.amp.autocast() and torch.set_grad_enabled(self.diffusion.pretransform.enable_grad): diffusion_input = self.diffusion.pretransform.encode(diffusion_input) p.tick("pretransform") # If mask_padding is on, interpolate the padding masks to the size of the pretransformed input # if use_padding_mask: # padding_masks = F.interpolate(padding_masks.unsqueeze(1).float(), size=diffusion_input.shape[2], mode="nearest").squeeze(1).bool() else: # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0: diffusion_input = diffusion_input / self.diffusion.pretransform.scale # Max mask size is the full sequence length max_mask_length = diffusion_input.shape[2] # Create a mask of random length for a random slice of the input masked_input, mask = self.random_mask(diffusion_input, max_mask_length) conditioning['inpaint_mask'] = [mask] conditioning['inpaint_masked_input'] = [masked_input] if self.timestep_sampler == "uniform": # Draw uniformly distributed continuous timesteps t = self.rng.draw(reals.shape[0])[:, 0].to(self.device) elif self.timestep_sampler == "logit_normal": t = torch.sigmoid(torch.randn(reals.shape[0], device=self.device)) # Calculate the noise schedule parameters for those timesteps if self.diffusion_objective == "v": alphas, sigmas = get_alphas_sigmas(t) elif self.diffusion_objective == "rectified_flow": alphas, sigmas = 1-t, t # Combine the ground truth data and the noise alphas = alphas[:, None, None] sigmas = sigmas[:, None, None] noise = torch.randn_like(diffusion_input) noised_inputs = diffusion_input * alphas + noise * sigmas if self.diffusion_objective == "v": targets = noise * alphas - diffusion_input * sigmas elif self.diffusion_objective == "rectified_flow": targets = noise - diffusion_input p.tick("noise") extra_args = {} with torch.cuda.amp.autocast(): p.tick("amp") output = self.diffusion(noised_inputs, t, cond=conditioning, cfg_dropout_prob = self.cfg_dropout_prob, **extra_args) p.tick("diffusion") loss_info.update({ "output": output, "targets": targets, }) loss, losses = self.losses(loss_info) if self.log_loss_info: # Loss debugging logs num_loss_buckets = 10 bucket_size = 1 / num_loss_buckets loss_all = F.mse_loss(output, targets, reduction="none") sigmas = rearrange(self.all_gather(sigmas), "w b c n -> (w b) c n").squeeze() # gather loss_all across all GPUs loss_all = rearrange(self.all_gather(loss_all), "w b c n -> (w b) c n") # Bucket loss values based on corresponding sigma values, bucketing sigma values by bucket_size loss_all = torch.stack([loss_all[(sigmas >= i) & (sigmas < i + bucket_size)].mean() for i in torch.arange(0, 1, bucket_size).to(self.device)]) # Log bucketed losses with corresponding sigma bucket values, if it's not NaN debug_log_dict = { f"model/loss_all_{i/num_loss_buckets:.1f}": loss_all[i].detach() for i in range(num_loss_buckets) if not torch.isnan(loss_all[i]) } self.log_dict(debug_log_dict) log_dict = { 'train/loss': loss.detach(), 'train/std_data': diffusion_input.std(), 'train/lr': self.trainer.optimizers[0].param_groups[0]['lr'] } for loss_name, loss_value in losses.items(): log_dict[f"train/{loss_name}"] = loss_value.detach() self.log_dict(log_dict, prog_bar=True, on_step=True) p.tick("log") #print(f"Profiler: {p}") return loss def on_before_zero_grad(self, *args, **kwargs): if self.diffusion_ema is not None: self.diffusion_ema.update() def export_model(self, path, use_safetensors=False): if self.diffusion_ema is not None: self.diffusion.model = self.diffusion_ema.ema_model if use_safetensors: save_file(self.diffusion.state_dict(), path) else: torch.save({"state_dict": self.diffusion.state_dict()}, path) class DiffusionCondInpaintDemoCallback(pl.Callback): def __init__( self, demo_dl, demo_every=2000, demo_steps=250, sample_size=65536, sample_rate=48000, demo_cfg_scales: tp.Optional[tp.List[int]] = [3, 5, 7] ): super().__init__() self.demo_every = demo_every self.demo_steps = demo_steps self.demo_samples = sample_size self.demo_dl = iter(demo_dl) self.sample_rate = sample_rate self.demo_cfg_scales = demo_cfg_scales self.last_demo_step = -1 @rank_zero_only @torch.no_grad() def on_train_batch_end(self, trainer, module: DiffusionCondTrainingWrapper, outputs, batch, batch_idx): if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step: return self.last_demo_step = trainer.global_step try: log_dict = {} demo_reals, metadata = next(self.demo_dl) # Remove extra dimension added by WebDataset if demo_reals.ndim == 4 and demo_reals.shape[0] == 1: demo_reals = demo_reals[0] demo_reals = demo_reals.to(module.device) if not module.pre_encoded: # Log the real audio log_dict[f'demo_reals_melspec_left'] = wandb.Image(audio_spectrogram_image(rearrange(demo_reals, "b d n -> d (b n)").mul(32767).to(torch.int16).cpu())) # log_dict[f'demo_reals'] = wandb.Audio(rearrange(demo_reals, "b d n -> d (b n)").mul(32767).to(torch.int16).cpu(), sample_rate=self.sample_rate, caption="demo reals") if module.diffusion.pretransform is not None: module.diffusion.pretransform.to(module.device) with torch.cuda.amp.autocast(): demo_reals = module.diffusion.pretransform.encode(demo_reals) demo_samples = demo_reals.shape[2] # Get conditioning conditioning = module.diffusion.conditioner(metadata, module.device) masked_input, mask = module.random_mask(demo_reals, demo_reals.shape[2]) conditioning['inpaint_mask'] = [mask] conditioning['inpaint_masked_input'] = [masked_input] if module.diffusion.pretransform is not None: log_dict[f'demo_masked_input'] = wandb.Image(tokens_spectrogram_image(masked_input.cpu())) else: log_dict[f'demo_masked_input'] = wandb.Image(audio_spectrogram_image(rearrange(masked_input, "b c t -> c (b t)").mul(32767).to(torch.int16).cpu())) cond_inputs = module.diffusion.get_conditioning_inputs(conditioning) noise = torch.randn([demo_reals.shape[0], module.diffusion.io_channels, demo_samples]).to(module.device) trainer.logger.experiment.log(log_dict) for cfg_scale in self.demo_cfg_scales: model = module.diffusion_ema.model if module.diffusion_ema is not None else module.diffusion.model print(f"Generating demo for cfg scale {cfg_scale}") if module.diffusion_objective == "v": fakes = sample(model, noise, self.demo_steps, 0, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True) elif module.diffusion_objective == "rectified_flow": fakes = sample_discrete_euler(model, noise, self.demo_steps, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True) if module.diffusion.pretransform is not None: with torch.cuda.amp.autocast(): fakes = module.diffusion.pretransform.decode(fakes) # Put the demos together fakes = rearrange(fakes, 'b d n -> d (b n)') log_dict = {} filename = f'demo_cfg_{cfg_scale}_{trainer.global_step:08}.wav' fakes = fakes.to(torch.float32).div(torch.max(torch.abs(fakes))).mul(32767).to(torch.int16).cpu() torchaudio.save(filename, fakes, self.sample_rate) log_dict[f'demo_cfg_{cfg_scale}'] = wandb.Audio(filename, sample_rate=self.sample_rate, caption=f'Reconstructed') log_dict[f'demo_melspec_left_cfg_{cfg_scale}'] = wandb.Image(audio_spectrogram_image(fakes)) trainer.logger.experiment.log(log_dict) except Exception as e: print(f'{type(e).__name__}: {e}') raise e class DiffusionAutoencoderTrainingWrapper(pl.LightningModule): ''' Wrapper for training a diffusion autoencoder ''' def __init__( self, model: DiffusionAutoencoder, lr: float = 1e-4, ema_copy = None, use_reconstruction_loss: bool = False ): super().__init__() self.diffae = model self.diffae_ema = EMA( self.diffae, ema_model=ema_copy, beta=0.9999, power=3/4, update_every=1, update_after_step=1, include_online_model=False ) self.lr = lr self.rng = torch.quasirandom.SobolEngine(1, scramble=True) loss_modules = [ MSELoss("v", "targets", weight=1.0, name="mse_loss" ) ] if model.bottleneck is not None: # TODO: Use loss config for configurable bottleneck weights and reconstruction losses loss_modules += create_loss_modules_from_bottleneck(model.bottleneck, {}) self.use_reconstruction_loss = use_reconstruction_loss if use_reconstruction_loss: scales = [2048, 1024, 512, 256, 128, 64, 32] hop_sizes = [] win_lengths = [] overlap = 0.75 for s in scales: hop_sizes.append(int(s * (1 - overlap))) win_lengths.append(s) sample_rate = model.sample_rate stft_loss_args = { "fft_sizes": scales, "hop_sizes": hop_sizes, "win_lengths": win_lengths, "perceptual_weighting": True } out_channels = model.out_channels if model.pretransform is not None: out_channels = model.pretransform.io_channels if out_channels == 2: self.sdstft = auraloss.freq.SumAndDifferenceSTFTLoss(sample_rate=sample_rate, **stft_loss_args) else: self.sdstft = auraloss.freq.MultiResolutionSTFTLoss(sample_rate=sample_rate, **stft_loss_args) loss_modules.append( AuralossLoss(self.sdstft, 'audio_reals', 'audio_pred', name='mrstft_loss', weight=0.1), # Reconstruction loss ) self.losses = MultiLoss(loss_modules) def configure_optimizers(self): return optim.Adam([*self.diffae.parameters()], lr=self.lr) def training_step(self, batch, batch_idx): reals = batch[0] if reals.ndim == 4 and reals.shape[0] == 1: reals = reals[0] loss_info = {} loss_info["audio_reals"] = reals if self.diffae.pretransform is not None: with torch.no_grad(): reals = self.diffae.pretransform.encode(reals) loss_info["reals"] = reals #Encode reals, skipping the pretransform since it was already applied latents, encoder_info = self.diffae.encode(reals, return_info=True, skip_pretransform=True) loss_info["latents"] = latents loss_info.update(encoder_info) if self.diffae.decoder is not None: latents = self.diffae.decoder(latents) # Upsample latents to match diffusion length if latents.shape[2] != reals.shape[2]: latents = F.interpolate(latents, size=reals.shape[2], mode='nearest') loss_info["latents_upsampled"] = latents # Draw uniformly distributed continuous timesteps t = self.rng.draw(reals.shape[0])[:, 0].to(self.device) # Calculate the noise schedule parameters for those timesteps alphas, sigmas = get_alphas_sigmas(t) # Combine the ground truth data and the noise alphas = alphas[:, None, None] sigmas = sigmas[:, None, None] noise = torch.randn_like(reals) noised_reals = reals * alphas + noise * sigmas targets = noise * alphas - reals * sigmas with torch.cuda.amp.autocast(): v = self.diffae.diffusion(noised_reals, t, input_concat_cond=latents) loss_info.update({ "v": v, "targets": targets }) if self.use_reconstruction_loss: pred = noised_reals * alphas - v * sigmas loss_info["pred"] = pred if self.diffae.pretransform is not None: pred = self.diffae.pretransform.decode(pred) loss_info["audio_pred"] = pred loss, losses = self.losses(loss_info) log_dict = { 'train/loss': loss.detach(), 'train/std_data': reals.std(), 'train/latent_std': latents.std(), } for loss_name, loss_value in losses.items(): log_dict[f"train/{loss_name}"] = loss_value.detach() self.log_dict(log_dict, prog_bar=True, on_step=True) return loss def on_before_zero_grad(self, *args, **kwargs): self.diffae_ema.update() def export_model(self, path, use_safetensors=False): model = self.diffae_ema.ema_model if use_safetensors: save_file(model.state_dict(), path) else: torch.save({"state_dict": model.state_dict()}, path) class DiffusionAutoencoderDemoCallback(pl.Callback): def __init__( self, demo_dl, demo_every=2000, demo_steps=250, sample_size=65536, sample_rate=48000 ): super().__init__() self.demo_every = demo_every self.demo_steps = demo_steps self.demo_samples = sample_size self.demo_dl = iter(demo_dl) self.sample_rate = sample_rate self.last_demo_step = -1 @rank_zero_only @torch.no_grad() def on_train_batch_end(self, trainer, module: DiffusionAutoencoderTrainingWrapper, outputs, batch, batch_idx): if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step: return self.last_demo_step = trainer.global_step demo_reals, _ = next(self.demo_dl) # Remove extra dimension added by WebDataset if demo_reals.ndim == 4 and demo_reals.shape[0] == 1: demo_reals = demo_reals[0] encoder_input = demo_reals encoder_input = encoder_input.to(module.device) demo_reals = demo_reals.to(module.device) with torch.no_grad() and torch.cuda.amp.autocast(): latents = module.diffae_ema.ema_model.encode(encoder_input).float() fakes = module.diffae_ema.ema_model.decode(latents, steps=self.demo_steps) #Interleave reals and fakes reals_fakes = rearrange([demo_reals, fakes], 'i b d n -> (b i) d n') # Put the demos together reals_fakes = rearrange(reals_fakes, 'b d n -> d (b n)') log_dict = {} filename = f'recon_{trainer.global_step:08}.wav' reals_fakes = reals_fakes.to(torch.float32).div(torch.max(torch.abs(reals_fakes))).mul(32767).to(torch.int16).cpu() torchaudio.save(filename, reals_fakes, self.sample_rate) log_dict[f'recon'] = wandb.Audio(filename, sample_rate=self.sample_rate, caption=f'Reconstructed') log_dict[f'embeddings_3dpca'] = pca_point_cloud(latents) log_dict[f'embeddings_spec'] = wandb.Image(tokens_spectrogram_image(latents)) log_dict[f'recon_melspec_left'] = wandb.Image(audio_spectrogram_image(reals_fakes)) if module.diffae_ema.ema_model.pretransform is not None: with torch.no_grad() and torch.cuda.amp.autocast(): initial_latents = module.diffae_ema.ema_model.pretransform.encode(encoder_input) first_stage_fakes = module.diffae_ema.ema_model.pretransform.decode(initial_latents) first_stage_fakes = rearrange(first_stage_fakes, 'b d n -> d (b n)') first_stage_fakes = first_stage_fakes.to(torch.float32).mul(32767).to(torch.int16).cpu() first_stage_filename = f'first_stage_{trainer.global_step:08}.wav' torchaudio.save(first_stage_filename, first_stage_fakes, self.sample_rate) log_dict[f'first_stage_latents'] = wandb.Image(tokens_spectrogram_image(initial_latents)) log_dict[f'first_stage'] = wandb.Audio(first_stage_filename, sample_rate=self.sample_rate, caption=f'First Stage Reconstructed') log_dict[f'first_stage_melspec_left'] = wandb.Image(audio_spectrogram_image(first_stage_fakes)) trainer.logger.experiment.log(log_dict) def create_source_mixture(reals, num_sources=2): # Create a fake mixture source by mixing elements from the training batch together with random offsets source = torch.zeros_like(reals) for i in range(reals.shape[0]): sources_added = 0 js = list(range(reals.shape[0])) random.shuffle(js) for j in js: if i == j or (i != j and sources_added < num_sources): # Randomly offset the mixed element between 0 and the length of the source seq_len = reals.shape[2] offset = random.randint(0, seq_len-1) source[i, :, offset:] += reals[j, :, :-offset] if i == j: # If this is the real one, shift the reals as well to ensure alignment new_reals = torch.zeros_like(reals[i]) new_reals[:, offset:] = reals[i, :, :-offset] reals[i] = new_reals sources_added += 1 return source class DiffusionPriorTrainingWrapper(pl.LightningModule): ''' Wrapper for training a diffusion prior for inverse problems Prior types: mono_stereo: The prior is conditioned on a mono version of the audio to generate a stereo version ''' def __init__( self, model: ConditionedDiffusionModelWrapper, lr: float = 1e-4, ema_copy = None, prior_type: PriorType = PriorType.MonoToStereo, use_reconstruction_loss: bool = False, log_loss_info: bool = False, ): super().__init__() self.diffusion = model self.diffusion_ema = EMA( self.diffusion, ema_model=ema_copy, beta=0.9999, power=3/4, update_every=1, update_after_step=1, include_online_model=False ) self.lr = lr self.rng = torch.quasirandom.SobolEngine(1, scramble=True) self.log_loss_info = log_loss_info loss_modules = [ MSELoss("v", "targets", weight=1.0, name="mse_loss" ) ] self.use_reconstruction_loss = use_reconstruction_loss if use_reconstruction_loss: scales = [2048, 1024, 512, 256, 128, 64, 32] hop_sizes = [] win_lengths = [] overlap = 0.75 for s in scales: hop_sizes.append(int(s * (1 - overlap))) win_lengths.append(s) sample_rate = model.sample_rate stft_loss_args = { "fft_sizes": scales, "hop_sizes": hop_sizes, "win_lengths": win_lengths, "perceptual_weighting": True } out_channels = model.io_channels self.audio_out_channels = out_channels if model.pretransform is not None: out_channels = model.pretransform.io_channels if self.audio_out_channels == 2: self.sdstft = auraloss.freq.SumAndDifferenceSTFTLoss(sample_rate=sample_rate, **stft_loss_args) self.lrstft = auraloss.freq.MultiResolutionSTFTLoss(sample_rate=sample_rate, **stft_loss_args) # Add left and right channel reconstruction losses in addition to the sum and difference self.loss_modules += [ AuralossLoss(self.lrstft, 'audio_reals_left', 'pred_left', name='stft_loss_left', weight=0.05), AuralossLoss(self.lrstft, 'audio_reals_right', 'pred_right', name='stft_loss_right', weight=0.05), ] else: self.sdstft = auraloss.freq.MultiResolutionSTFTLoss(sample_rate=sample_rate, **stft_loss_args) self.loss_modules.append( AuralossLoss(self.sdstft, 'audio_reals', 'audio_pred', name='mrstft_loss', weight=0.1), # Reconstruction loss ) self.losses = MultiLoss(loss_modules) self.prior_type = prior_type def configure_optimizers(self): return optim.Adam([*self.diffusion.parameters()], lr=self.lr) def training_step(self, batch, batch_idx): reals, metadata = batch if reals.ndim == 4 and reals.shape[0] == 1: reals = reals[0] loss_info = {} loss_info["audio_reals"] = reals if self.prior_type == PriorType.MonoToStereo: source = reals.mean(dim=1, keepdim=True).repeat(1, reals.shape[1], 1).to(self.device) loss_info["audio_reals_mono"] = source else: raise ValueError(f"Unknown prior type {self.prior_type}") if self.diffusion.pretransform is not None: with torch.no_grad(): reals = self.diffusion.pretransform.encode(reals) if self.prior_type in [PriorType.MonoToStereo]: source = self.diffusion.pretransform.encode(source) if self.diffusion.conditioner is not None: with torch.cuda.amp.autocast(): conditioning = self.diffusion.conditioner(metadata, self.device) else: conditioning = {} loss_info["reals"] = reals # Draw uniformly distributed continuous timesteps t = self.rng.draw(reals.shape[0])[:, 0].to(self.device) # Calculate the noise schedule parameters for those timesteps alphas, sigmas = get_alphas_sigmas(t) # Combine the ground truth data and the noise alphas = alphas[:, None, None] sigmas = sigmas[:, None, None] noise = torch.randn_like(reals) noised_reals = reals * alphas + noise * sigmas targets = noise * alphas - reals * sigmas with torch.cuda.amp.autocast(): conditioning['source'] = [source] v = self.diffusion(noised_reals, t, cond=conditioning, cfg_dropout_prob = 0.1) loss_info.update({ "v": v, "targets": targets }) if self.use_reconstruction_loss: pred = noised_reals * alphas - v * sigmas loss_info["pred"] = pred if self.diffusion.pretransform is not None: pred = self.diffusion.pretransform.decode(pred) loss_info["audio_pred"] = pred if self.audio_out_channels == 2: loss_info["pred_left"] = pred[:, 0:1, :] loss_info["pred_right"] = pred[:, 1:2, :] loss_info["audio_reals_left"] = loss_info["audio_reals"][:, 0:1, :] loss_info["audio_reals_right"] = loss_info["audio_reals"][:, 1:2, :] loss, losses = self.losses(loss_info) if self.log_loss_info: # Loss debugging logs num_loss_buckets = 10 bucket_size = 1 / num_loss_buckets loss_all = F.mse_loss(v, targets, reduction="none") sigmas = rearrange(self.all_gather(sigmas), "w b c n -> (w b) c n").squeeze() # gather loss_all across all GPUs loss_all = rearrange(self.all_gather(loss_all), "w b c n -> (w b) c n") # Bucket loss values based on corresponding sigma values, bucketing sigma values by bucket_size loss_all = torch.stack([loss_all[(sigmas >= i) & (sigmas < i + bucket_size)].mean() for i in torch.arange(0, 1, bucket_size).to(self.device)]) # Log bucketed losses with corresponding sigma bucket values, if it's not NaN debug_log_dict = { f"model/loss_all_{i/num_loss_buckets:.1f}": loss_all[i].detach() for i in range(num_loss_buckets) if not torch.isnan(loss_all[i]) } self.log_dict(debug_log_dict) log_dict = { 'train/loss': loss.detach(), 'train/std_data': reals.std() } for loss_name, loss_value in losses.items(): log_dict[f"train/{loss_name}"] = loss_value.detach() self.log_dict(log_dict, prog_bar=True, on_step=True) return loss def on_before_zero_grad(self, *args, **kwargs): self.diffusion_ema.update() def export_model(self, path, use_safetensors=False): #model = self.diffusion_ema.ema_model model = self.diffusion if use_safetensors: save_file(model.state_dict(), path) else: torch.save({"state_dict": model.state_dict()}, path) class DiffusionPriorDemoCallback(pl.Callback): def __init__( self, demo_dl, demo_every=2000, demo_steps=250, sample_size=65536, sample_rate=48000 ): super().__init__() self.demo_every = demo_every self.demo_steps = demo_steps self.demo_samples = sample_size self.demo_dl = iter(demo_dl) self.sample_rate = sample_rate self.last_demo_step = -1 @rank_zero_only @torch.no_grad() def on_train_batch_end(self, trainer, module: DiffusionAutoencoderTrainingWrapper, outputs, batch, batch_idx): if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step: return self.last_demo_step = trainer.global_step demo_reals, metadata = next(self.demo_dl) # Remove extra dimension added by WebDataset if demo_reals.ndim == 4 and demo_reals.shape[0] == 1: demo_reals = demo_reals[0] demo_reals = demo_reals.to(module.device) encoder_input = demo_reals if module.diffusion.conditioner is not None: with torch.cuda.amp.autocast(): conditioning_tensors = module.diffusion.conditioner(metadata, module.device) else: conditioning_tensors = {} with torch.no_grad() and torch.cuda.amp.autocast(): if module.prior_type == PriorType.MonoToStereo and encoder_input.shape[1] > 1: source = encoder_input.mean(dim=1, keepdim=True).repeat(1, encoder_input.shape[1], 1).to(module.device) if module.diffusion.pretransform is not None: encoder_input = module.diffusion.pretransform.encode(encoder_input) source_input = module.diffusion.pretransform.encode(source) else: source_input = source conditioning_tensors['source'] = [source_input] fakes = sample(module.diffusion_ema.model, torch.randn_like(encoder_input), self.demo_steps, 0, cond=conditioning_tensors) if module.diffusion.pretransform is not None: fakes = module.diffusion.pretransform.decode(fakes) #Interleave reals and fakes reals_fakes = rearrange([demo_reals, fakes], 'i b d n -> (b i) d n') # Put the demos together reals_fakes = rearrange(reals_fakes, 'b d n -> d (b n)') log_dict = {} filename = f'recon_{trainer.global_step:08}.wav' reals_fakes = reals_fakes.to(torch.float32).div(torch.max(torch.abs(reals_fakes))).mul(32767).to(torch.int16).cpu() torchaudio.save(filename, reals_fakes, self.sample_rate) log_dict[f'recon'] = wandb.Audio(filename, sample_rate=self.sample_rate, caption=f'Reconstructed') log_dict[f'recon_melspec_left'] = wandb.Image(audio_spectrogram_image(reals_fakes)) #Log the source filename = f'source_{trainer.global_step:08}.wav' source = rearrange(source, 'b d n -> d (b n)') source = source.to(torch.float32).mul(32767).to(torch.int16).cpu() torchaudio.save(filename, source, self.sample_rate) log_dict[f'source'] = wandb.Audio(filename, sample_rate=self.sample_rate, caption=f'Source') log_dict[f'source_melspec_left'] = wandb.Image(audio_spectrogram_image(source)) trainer.logger.experiment.log(log_dict)