# Copyright (c) 2023 Amphion. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import os import sys import time import torch import json import itertools import accelerate import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F from tqdm import tqdm from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from torch.utils.tensorboard import SummaryWriter from torch.optim import AdamW from torch.optim.lr_scheduler import ExponentialLR from librosa.filters import mel as librosa_mel_fn from accelerate.logging import get_logger from pathlib import Path from utils.io import save_audio from utils.data_utils import * from utils.util import ( Logger, ValueWindow, remove_older_ckpt, set_all_random_seed, save_config, ) from utils.mel import extract_mel_features from models.vocoders.vocoder_trainer import VocoderTrainer from models.vocoders.diffusion.diffusion_vocoder_dataset import ( DiffusionVocoderDataset, DiffusionVocoderCollator, ) from models.vocoders.diffusion.diffwave.diffwave import DiffWave from models.vocoders.diffusion.diffusion_vocoder_inference import vocoder_inference supported_models = { "diffwave": DiffWave, } class DiffusionVocoderTrainer(VocoderTrainer): def __init__(self, args, cfg): super().__init__() self.args = args self.cfg = cfg cfg.exp_name = args.exp_name # Diffusion self.cfg.model.diffwave.noise_schedule = np.linspace( self.cfg.model.diffwave.noise_schedule_factors[0], self.cfg.model.diffwave.noise_schedule_factors[1], self.cfg.model.diffwave.noise_schedule_factors[2], ) beta = np.array(self.cfg.model.diffwave.noise_schedule) noise_level = np.cumprod(1 - beta) self.noise_level = torch.tensor(noise_level.astype(np.float32)) # Init accelerator self._init_accelerator() self.accelerator.wait_for_everyone() # Init logger with self.accelerator.main_process_first(): self.logger = get_logger(args.exp_name, log_level=args.log_level) self.logger.info("=" * 56) self.logger.info("||\t\t" + "New training process started." + "\t\t||") self.logger.info("=" * 56) self.logger.info("\n") self.logger.debug(f"Using {args.log_level.upper()} logging level.") self.logger.info(f"Experiment name: {args.exp_name}") self.logger.info(f"Experiment directory: {self.exp_dir}") self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") if self.accelerator.is_main_process: os.makedirs(self.checkpoint_dir, exist_ok=True) self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") # Init training status self.batch_count: int = 0 self.step: int = 0 self.epoch: int = 0 self.max_epoch = ( self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") ) self.logger.info( "Max epoch: {}".format( self.max_epoch if self.max_epoch < float("inf") else "Unlimited" ) ) # Check potential erorrs if self.accelerator.is_main_process: self._check_basic_configs() self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride self.checkpoints_path = [ [] for _ in range(len(self.save_checkpoint_stride)) ] self.run_eval = self.cfg.train.run_eval # Set random seed with self.accelerator.main_process_first(): start = time.monotonic_ns() self._set_random_seed(self.cfg.train.random_seed) end = time.monotonic_ns() self.logger.debug( f"Setting random seed done in {(end - start) / 1e6:.2f}ms" ) self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") # Build dataloader with self.accelerator.main_process_first(): self.logger.info("Building dataset...") start = time.monotonic_ns() self.train_dataloader, self.valid_dataloader = self._build_dataloader() end = time.monotonic_ns() self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") # Build model with self.accelerator.main_process_first(): self.logger.info("Building model...") start = time.monotonic_ns() self.model = self._build_model() end = time.monotonic_ns() self.logger.debug(self.model) self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") self.logger.info(f"Model parameters: {self._count_parameters()/1e6:.2f}M") # Build optimizers and schedulers with self.accelerator.main_process_first(): self.logger.info("Building optimizer and scheduler...") start = time.monotonic_ns() self.optimizer = self._build_optimizer() self.scheduler = self._build_scheduler() end = time.monotonic_ns() self.logger.info( f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" ) # Accelerator preparing self.logger.info("Initializing accelerate...") start = time.monotonic_ns() ( self.train_dataloader, self.valid_dataloader, self.model, self.optimizer, self.scheduler, ) = self.accelerator.prepare( self.train_dataloader, self.valid_dataloader, self.model, self.optimizer, self.scheduler, ) end = time.monotonic_ns() self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") # Build criterions with self.accelerator.main_process_first(): self.logger.info("Building criterion...") start = time.monotonic_ns() self.criterion = self._build_criterion() end = time.monotonic_ns() self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") # Resume checkpoints with self.accelerator.main_process_first(): if args.resume_type: self.logger.info("Resuming from checkpoint...") start = time.monotonic_ns() ckpt_path = Path(args.checkpoint) if self._is_valid_pattern(ckpt_path.parts[-1]): ckpt_path = self._load_model( None, args.checkpoint, args.resume_type ) else: ckpt_path = self._load_model( args.checkpoint, resume_type=args.resume_type ) end = time.monotonic_ns() self.logger.info( f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" ) self.checkpoints_path = json.load( open(os.path.join(ckpt_path, "ckpts.json"), "r") ) self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") if self.accelerator.is_main_process: os.makedirs(self.checkpoint_dir, exist_ok=True) self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") # Save config self.config_save_path = os.path.join(self.exp_dir, "args.json") # Device self.device = next(self.model.parameters()).device self.noise_level = self.noise_level.to(self.device) def _build_dataset(self): return DiffusionVocoderDataset, DiffusionVocoderCollator def _build_criterion(self): criterion = nn.L1Loss() return criterion def _build_model(self): model = supported_models[self.cfg.model.generator](self.cfg) return model def _build_optimizer(self): optimizer = AdamW( self.model.parameters(), lr=self.cfg.train.adamw.lr, betas=(self.cfg.train.adamw.adam_b1, self.cfg.train.adamw.adam_b2), ) return optimizer def _build_scheduler(self): scheduler = ExponentialLR( self.optimizer, gamma=self.cfg.train.exponential_lr.lr_decay, last_epoch=self.epoch - 1, ) return scheduler def train_loop(self): """Training process""" self.accelerator.wait_for_everyone() # Dump config if self.accelerator.is_main_process: self._dump_cfg(self.config_save_path) self.model.train() self.optimizer.zero_grad() # Sync and start training self.accelerator.wait_for_everyone() while self.epoch < self.max_epoch: self.logger.info("\n") self.logger.info("-" * 32) self.logger.info("Epoch {}: ".format(self.epoch)) # Train and Validate train_total_loss = self._train_epoch() valid_total_loss = self._valid_epoch() self.accelerator.log( { "Epoch/Train Total Loss": train_total_loss, "Epoch/Valid Total Loss": valid_total_loss, }, step=self.epoch, ) # Update scheduler self.accelerator.wait_for_everyone() self.scheduler.step() # Check save checkpoint interval run_eval = False if self.accelerator.is_main_process: save_checkpoint = False for i, num in enumerate(self.save_checkpoint_stride): if self.epoch % num == 0: save_checkpoint = True run_eval |= self.run_eval[i] # Save checkpoints self.accelerator.wait_for_everyone() if self.accelerator.is_main_process and save_checkpoint: path = os.path.join( self.checkpoint_dir, "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( self.epoch, self.step, valid_total_loss ), ) self.accelerator.save_state(path) json.dump( self.checkpoints_path, open(os.path.join(path, "ckpts.json"), "w"), ensure_ascii=False, indent=4, ) # Save eval audios self.accelerator.wait_for_everyone() if self.accelerator.is_main_process and run_eval: for i in range(len(self.valid_dataloader.dataset.eval_audios)): if self.cfg.preprocess.use_frame_pitch: eval_audio = self._inference( self.valid_dataloader.dataset.eval_mels[i], eval_pitch=self.valid_dataloader.dataset.eval_pitchs[i], use_pitch=True, ) else: eval_audio = self._inference( self.valid_dataloader.dataset.eval_mels[i] ) path = os.path.join( self.checkpoint_dir, "epoch-{:04d}_step-{:07d}_loss-{:.6f}_eval_audio_{}.wav".format( self.epoch, self.step, valid_total_loss, self.valid_dataloader.dataset.eval_dataset_names[i], ), ) path_gt = os.path.join( self.checkpoint_dir, "epoch-{:04d}_step-{:07d}_loss-{:.6f}_eval_audio_{}_gt.wav".format( self.epoch, self.step, valid_total_loss, self.valid_dataloader.dataset.eval_dataset_names[i], ), ) save_audio(path, eval_audio, self.cfg.preprocess.sample_rate) save_audio( path_gt, self.valid_dataloader.dataset.eval_audios[i], self.cfg.preprocess.sample_rate, ) self.accelerator.wait_for_everyone() self.epoch += 1 # Finish training self.accelerator.wait_for_everyone() path = os.path.join( self.checkpoint_dir, "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( self.epoch, self.step, valid_total_loss ), ) self.accelerator.save_state(path) def _train_epoch(self): """Training epoch. Should return average loss of a batch (sample) over one epoch. See ``train_loop`` for usage. """ self.model.train() epoch_total_loss: int = 0 for batch in tqdm( self.train_dataloader, desc=f"Training Epoch {self.epoch}", unit="batch", colour="GREEN", leave=False, dynamic_ncols=True, smoothing=0.04, disable=not self.accelerator.is_main_process, ): # Get losses total_loss = self._train_step(batch) self.batch_count += 1 # Log info if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: self.accelerator.log( { "Step/Learning Rate": self.optimizer.param_groups[0]["lr"], }, step=self.step, ) epoch_total_loss += total_loss self.step += 1 # Get and log total losses self.accelerator.wait_for_everyone() epoch_total_loss = ( epoch_total_loss / len(self.train_dataloader) * self.cfg.train.gradient_accumulation_step ) return epoch_total_loss def _train_step(self, data): """Training forward step. Should return average loss of a sample over one batch. Provoke ``_forward_step`` is recommended except for special case. See ``_train_epoch`` for usage. """ # Init losses total_loss = 0 # Use input feature to get predictions mel_input = data["mel"] audio_gt = data["audio"] if self.cfg.preprocess.use_frame_pitch: pitch_input = data["frame_pitch"] self.optimizer.zero_grad() N = audio_gt.shape[0] t = torch.randint( 0, len(self.cfg.model.diffwave.noise_schedule), [N], device=self.device ) noise_scale = self.noise_level[t].unsqueeze(1) noise_scale_sqrt = noise_scale**0.5 noise = torch.randn_like(audio_gt).to(self.device) noisy_audio = noise_scale_sqrt * audio_gt + (1.0 - noise_scale) ** 0.5 * noise audio_pred = self.model(noisy_audio, t, mel_input) total_loss = self.criterion(noise, audio_pred.squeeze(1)) self.accelerator.backward(total_loss) self.optimizer.step() return total_loss.item() def _valid_epoch(self): """Testing epoch. Should return average loss of a batch (sample) over one epoch. See ``train_loop`` for usage. """ self.model.eval() epoch_total_loss: int = 0 for batch in tqdm( self.valid_dataloader, desc=f"Validating Epoch {self.epoch}", unit="batch", colour="GREEN", leave=False, dynamic_ncols=True, smoothing=0.04, disable=not self.accelerator.is_main_process, ): # Get losses total_loss = self._valid_step(batch) # Log info epoch_total_loss += total_loss # Get and log total losses self.accelerator.wait_for_everyone() epoch_total_loss = epoch_total_loss / len(self.valid_dataloader) return epoch_total_loss def _valid_step(self, data): """Testing forward step. Should return average loss of a sample over one batch. Provoke ``_forward_step`` is recommended except for special case. See ``_test_epoch`` for usage. """ # Init losses total_loss = 0 # Use feature inputs to get the predicted audio mel_input = data["mel"] audio_gt = data["audio"] if self.cfg.preprocess.use_frame_pitch: pitch_input = data["frame_pitch"] N = audio_gt.shape[0] t = torch.randint( 0, len(self.cfg.model.diffwave.noise_schedule), [N], device=self.device ) noise_scale = self.noise_level[t].unsqueeze(1) noise_scale_sqrt = noise_scale**0.5 noise = torch.randn_like(audio_gt) noisy_audio = noise_scale_sqrt * audio_gt + (1.0 - noise_scale) ** 0.5 * noise audio_pred = self.model(noisy_audio, t, mel_input) total_loss = self.criterion(noise, audio_pred.squeeze(1)) return total_loss.item() def _inference(self, eval_mel, eval_pitch=None, use_pitch=False): """Inference during training for test audios.""" if use_pitch: eval_pitch = align_length(eval_pitch, eval_mel.shape[1]) eval_audio = vocoder_inference( self.cfg, self.model, torch.from_numpy(eval_mel).unsqueeze(0), f0s=torch.from_numpy(eval_pitch).unsqueeze(0).float(), device=next(self.model.parameters()).device, ).squeeze(0) else: eval_audio = vocoder_inference( self.cfg, self.model, torch.from_numpy(eval_mel).unsqueeze(0), device=next(self.model.parameters()).device, ).squeeze(0) return eval_audio def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): """Load model from checkpoint. If checkpoint_path is None, it will load the latest checkpoint in checkpoint_dir. If checkpoint_path is not None, it will load the checkpoint specified by checkpoint_path. **Only use this method after** ``accelerator.prepare()``. """ if checkpoint_path is None: ls = [str(i) for i in Path(checkpoint_dir).glob("*")] ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) checkpoint_path = ls[0] if resume_type == "resume": self.accelerator.load_state(checkpoint_path) self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 elif resume_type == "finetune": accelerate.load_checkpoint_and_dispatch( self.accelerator.unwrap_model(self.model), os.path.join(checkpoint_path, "pytorch_model.bin"), ) self.logger.info("Load model weights for finetune SUCCESS!") else: raise ValueError("Unsupported resume type: {}".format(resume_type)) return checkpoint_path def _count_parameters(self): result = sum(p.numel() for p in self.model.parameters()) return result