import time import logging from pathlib import Path import numpy as np import paddle from paddle import distributed as dist from paddle.io import DataLoader, DistributedBatchSampler from tensorboardX import SummaryWriter from collections import defaultdict import parakeet from parakeet.data import dataset from parakeet.frontend import English from parakeet.models.transformer_tts import TransformerTTS, TransformerTTSLoss from parakeet.utils import scheduler, checkpoint, mp_tools, display from parakeet.training.cli import default_argument_parser from parakeet.training.experiment import ExperimentBase from config import get_cfg_defaults from ljspeech import LJSpeech, LJSpeechCollector, Transform class Experiment(ExperimentBase): def setup_model(self): config = self.config frontend = English() model = TransformerTTS( frontend, d_encoder=config.model.d_encoder, d_decoder=config.model.d_decoder, d_mel=config.data.d_mel, n_heads=config.model.n_heads, d_ffn=config.model.d_ffn, encoder_layers=config.model.encoder_layers, decoder_layers=config.model.decoder_layers, d_prenet=config.model.d_prenet, d_postnet=config.model.d_postnet, postnet_layers=config.model.postnet_layers, postnet_kernel_size=config.model.postnet_kernel_size, max_reduction_factor=config.model.max_reduction_factor, decoder_prenet_dropout=config.model.decoder_prenet_dropout, dropout=config.model.dropout) if self.parallel: model = paddle.DataParallel(model) optimizer = paddle.optimizer.Adam( learning_rate=config.training.lr, beta1=0.9, beta2=0.98, epsilon=1e-9, parameters=model.parameters() ) criterion = TransformerTTSLoss(config.model.stop_loss_scale) drop_n_heads = scheduler.StepWise(config.training.drop_n_heads) reduction_factor = scheduler.StepWise(config.training.reduction_factor) self.model = model self.optimizer = optimizer self.criterion = criterion self.drop_n_heads = drop_n_heads self.reduction_factor = reduction_factor def setup_dataloader(self): args = self.args config = self.config ljspeech_dataset = LJSpeech(args.data) transform = Transform(config.data.mel_start_value, config.data.mel_end_value) ljspeech_dataset = dataset.TransformDataset(ljspeech_dataset, transform) valid_set, train_set = dataset.split(ljspeech_dataset, config.data.valid_size) batch_fn = LJSpeechCollector(padding_idx=config.data.padding_idx) if not self.parallel: train_loader = DataLoader( train_set, batch_size=config.data.batch_size, shuffle=True, drop_last=True, collate_fn=batch_fn) else: sampler = DistributedBatchSampler( train_set, batch_size=config.data.batch_size, num_replicas=dist.get_world_size(), rank=dist.get_rank(), shuffle=True, drop_last=True) train_loader = DataLoader( train_set, batch_sampler=sampler, collate_fn=batch_fn) valid_loader = DataLoader( valid_set, batch_size=config.data.batch_size, collate_fn=batch_fn) self.train_loader = train_loader self.valid_loader = valid_loader def compute_outputs(self, text, mel, stop_label): model_core = self.model._layers if self.parallel else self.model model_core.set_constants( self.reduction_factor(self.iteration), self.drop_n_heads(self.iteration)) # TODO(chenfeiyu): we can combine these 2 slices mel_input = mel[:,:-1, :] reduced_mel_input = mel_input[:, ::model_core.r, :] outputs = self.model(text, reduced_mel_input) return outputs def compute_losses(self, inputs, outputs): _, mel, stop_label = inputs mel_target = mel[:, 1:, :] stop_label_target = stop_label[:, 1:] mel_output = outputs["mel_output"] mel_intermediate = outputs["mel_intermediate"] stop_logits = outputs["stop_logits"] time_steps = mel_target.shape[1] losses = self.criterion( mel_output[:,:time_steps, :], mel_intermediate[:,:time_steps, :], mel_target, stop_logits[:,:time_steps, :], stop_label_target) return losses def train_batch(self): start = time.time() batch = self.read_batch() data_loader_time = time.time() - start self.optimizer.clear_grad() self.model.train() text, mel, stop_label = batch outputs = self.compute_outputs(text, mel, stop_label) losses = self.compute_losses(batch, outputs) loss = losses["loss"] loss.backward() self.optimizer.step() iteration_time = time.time() - start losses_np = {k: float(v) for k, v in losses.items()} # logging msg = "Rank: {}, ".format(dist.get_rank()) msg += "step: {}, ".format(self.iteration) msg += "time: {:>.3f}s/{:>.3f}s, ".format(data_loader_time, iteration_time) msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_np.items()) self.logger.info(msg) if dist.get_rank() == 0: for k, v in losses_np.items(): self.visualizer.add_scalar(f"train_loss/{k}", v, self.iteration) @mp_tools.rank_zero_only @paddle.no_grad() def valid(self): valid_losses = defaultdict(list) for i, batch in enumerate(self.valid_loader): text, mel, stop_label = batch outputs = self.compute_outputs(text, mel, stop_label) losses = self.compute_losses(batch, outputs) for k, v in losses.items(): valid_losses[k].append(float(v)) if i < 2: attention_weights = outputs["cross_attention_weights"] display.add_multi_attention_plots( self.visualizer, f"valid_sentence_{i}_cross_attention_weights", attention_weights, self.iteration) # write visual log valid_losses = {k: np.mean(v) for k, v in valid_losses.items()} for k, v in valid_losses.items(): self.visualizer.add_scalar(f"valid/{k}", v, self.iteration) def main_sp(config, args): exp = Experiment(config, args) exp.setup() exp.run() def main(config, args): if args.nprocs > 1 and args.device == "gpu": dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs) else: main_sp(config, args) if __name__ == "__main__": config = get_cfg_defaults() parser = default_argument_parser() args = parser.parse_args() if args.config: config.merge_from_file(args.config) if args.opts: config.merge_from_list(args.opts) config.freeze() print(config) print(args) main(config, args)