# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time from pathlib import Path from collections import defaultdict import numpy as np from matplotlib import pyplot as plt import paddle from paddle import distributed as dist from paddle.io import DataLoader, DistributedBatchSampler from parakeet.data import dataset from parakeet.training.cli import default_argument_parser from parakeet.training.experiment import ExperimentBase from parakeet.utils import display, mp_tools from parakeet.models.tacotron2 import Tacotron2, Tacotron2Loss from config import get_cfg_defaults from aishell3 import AiShell3, collate_aishell3_examples class Experiment(ExperimentBase): def compute_losses(self, inputs, outputs): texts, tones, mel_targets, utterance_embeds, text_lens, output_lens, stop_tokens = inputs mel_outputs = outputs["mel_output"] mel_outputs_postnet = outputs["mel_outputs_postnet"] alignments = outputs["alignments"] losses = self.criterion(mel_outputs, mel_outputs_postnet, mel_targets, alignments, output_lens, text_lens) 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() texts, tones, mels, utterance_embeds, text_lens, output_lens, stop_tokens = batch outputs = self.model( texts, text_lens, mels, output_lens, tones=tones, global_condition=utterance_embeds) 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 key, value in losses_np.items(): self.visualizer.add_scalar(f"train_loss/{key}", value, 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): texts, tones, mels, utterance_embeds, text_lens, output_lens, stop_tokens = batch outputs = self.model( texts, text_lens, mels, output_lens, tones=tones, global_condition=utterance_embeds) losses = self.compute_losses(batch, outputs) for key, value in losses.items(): valid_losses[key].append(float(value)) attention_weights = outputs["alignments"] self.visualizer.add_figure( f"valid_sentence_{i}_alignments", display.plot_alignment(attention_weights[0].numpy().T), self.iteration) self.visualizer.add_figure( f"valid_sentence_{i}_target_spectrogram", display.plot_spectrogram(mels[0].numpy().T), self.iteration) mel_pred = outputs['mel_outputs_postnet'] self.visualizer.add_figure( f"valid_sentence_{i}_predicted_spectrogram", display.plot_spectrogram(mel_pred[0].numpy().T), self.iteration) # write visual log valid_losses = {k: np.mean(v) for k, v in valid_losses.items()} # logging msg = "Valid: " msg += "step: {}, ".format(self.iteration) msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in valid_losses.items()) self.logger.info(msg) for key, value in valid_losses.items(): self.visualizer.add_scalar(f"valid/{key}", value, self.iteration) @mp_tools.rank_zero_only @paddle.no_grad() def eval(self): """Evaluation of Tacotron2 in autoregressive manner.""" self.model.eval() mel_dir = Path(self.output_dir / ("eval_{}".format(self.iteration))) mel_dir.mkdir(parents=True, exist_ok=True) for i, batch in enumerate(self.test_loader): texts, tones, mels, utterance_embeds, *_ = batch outputs = self.model.infer( texts, tones=tones, global_condition=utterance_embeds) display.plot_alignment(outputs["alignments"][0].numpy().T) plt.savefig(mel_dir / f"sentence_{i}.png") plt.close() np.save(mel_dir / f"sentence_{i}", outputs["mel_outputs_postnet"][0].numpy().T) print(f"sentence_{i}") def setup_model(self): config = self.config model = Tacotron2( vocab_size=config.model.vocab_size, n_tones=config.model.n_tones, d_mels=config.data.d_mels, d_encoder=config.model.d_encoder, encoder_conv_layers=config.model.encoder_conv_layers, encoder_kernel_size=config.model.encoder_kernel_size, d_prenet=config.model.d_prenet, d_attention_rnn=config.model.d_attention_rnn, d_decoder_rnn=config.model.d_decoder_rnn, attention_filters=config.model.attention_filters, attention_kernel_size=config.model.attention_kernel_size, d_attention=config.model.d_attention, d_postnet=config.model.d_postnet, postnet_kernel_size=config.model.postnet_kernel_size, postnet_conv_layers=config.model.postnet_conv_layers, reduction_factor=config.model.reduction_factor, p_encoder_dropout=config.model.p_encoder_dropout, p_prenet_dropout=config.model.p_prenet_dropout, p_attention_dropout=config.model.p_attention_dropout, p_decoder_dropout=config.model.p_decoder_dropout, p_postnet_dropout=config.model.p_postnet_dropout, d_global_condition=config.model.d_global_condition, use_stop_token=config.model.use_stop_token, ) if self.parallel: model = paddle.DataParallel(model) grad_clip = paddle.nn.ClipGradByGlobalNorm( config.training.grad_clip_thresh) optimizer = paddle.optimizer.Adam( learning_rate=config.training.lr, parameters=model.parameters(), weight_decay=paddle.regularizer.L2Decay( config.training.weight_decay), grad_clip=grad_clip) criterion = Tacotron2Loss( use_stop_token_loss=config.model.use_stop_token, use_guided_attention_loss=config.model.use_guided_attention_loss, sigma=config.model.guided_attention_loss_sigma) self.model = model self.optimizer = optimizer self.criterion = criterion def setup_dataloader(self): args = self.args config = self.config ljspeech_dataset = AiShell3(args.data) valid_set, train_set = dataset.split(ljspeech_dataset, config.data.valid_size) batch_fn = collate_aishell3_examples if not self.parallel: self.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, shuffle=True, drop_last=True) self.train_loader = DataLoader( train_set, batch_sampler=sampler, collate_fn=batch_fn) self.valid_loader = DataLoader( valid_set, batch_size=config.data.batch_size, shuffle=False, drop_last=False, collate_fn=batch_fn) self.test_loader = DataLoader( valid_set, batch_size=1, shuffle=False, drop_last=False, collate_fn=batch_fn) def main_sp(config, args): exp = Experiment(config, args) exp.setup() exp.resume_or_load() if not args.test: exp.run() else: exp.eval() 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() parser.add_argument("--test", action="store_true") 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)