# 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 os from tqdm import tqdm from tensorboardX import SummaryWriter #from pathlib import Path from collections import OrderedDict import argparse from parse import add_config_options_to_parser from pprint import pprint from ruamel import yaml from matplotlib import cm import numpy as np import paddle.fluid as fluid import paddle.fluid.dygraph as dg import paddle.fluid.layers as layers from parakeet.models.transformer_tts.utils import cross_entropy from data import LJSpeechLoader from parakeet.models.transformer_tts.transformer_tts import TransformerTTS def load_checkpoint(step, model_path): model_dict, opti_dict = fluid.dygraph.load_dygraph( os.path.join(model_path, step)) new_state_dict = OrderedDict() for param in model_dict: if param.startswith('_layers.'): new_state_dict[param[8:]] = model_dict[param] else: new_state_dict[param] = model_dict[param] return new_state_dict, opti_dict def main(args): local_rank = dg.parallel.Env().local_rank if args.use_data_parallel else 0 nranks = dg.parallel.Env().nranks if args.use_data_parallel else 1 with open(args.config_path) as f: cfg = yaml.load(f, Loader=yaml.Loader) global_step = 0 place = (fluid.CUDAPlace(dg.parallel.Env().dev_id) if args.use_data_parallel else fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()) if not os.path.exists(args.log_dir): os.mkdir(args.log_dir) path = os.path.join(args.log_dir, 'transformer') writer = SummaryWriter(path) if local_rank == 0 else None with dg.guard(place): model = TransformerTTS(cfg) model.train() optimizer = fluid.optimizer.AdamOptimizer( learning_rate=dg.NoamDecay(1 / ( cfg['warm_up_step'] * (args.lr**2)), cfg['warm_up_step']), parameter_list=model.parameters()) reader = LJSpeechLoader( cfg, args, nranks, local_rank, shuffle=True).reader() if args.checkpoint_path is not None: model_dict, opti_dict = load_checkpoint( str(args.transformer_step), os.path.join(args.checkpoint_path, "transformer")) model.set_dict(model_dict) optimizer.set_dict(opti_dict) global_step = args.transformer_step print("load checkpoint!!!") if args.use_data_parallel: strategy = dg.parallel.prepare_context() model = fluid.dygraph.parallel.DataParallel(model, strategy) for epoch in range(args.epochs): pbar = tqdm(reader) for i, data in enumerate(pbar): pbar.set_description('Processing at epoch %d' % epoch) character, mel, mel_input, pos_text, pos_mel, text_length, _, enc_slf_mask, enc_query_mask, dec_slf_mask, enc_dec_mask, dec_query_slf_mask, dec_query_mask = data global_step += 1 mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model( character, mel_input, pos_text, pos_mel, dec_slf_mask=dec_slf_mask, enc_slf_mask=enc_slf_mask, enc_query_mask=enc_query_mask, enc_dec_mask=enc_dec_mask, dec_query_slf_mask=dec_query_slf_mask, dec_query_mask=dec_query_mask) mel_loss = layers.mean( layers.abs(layers.elementwise_sub(mel_pred, mel))) post_mel_loss = layers.mean( layers.abs(layers.elementwise_sub(postnet_pred, mel))) loss = mel_loss + post_mel_loss # Note: When used stop token loss the learning did not work. if args.stop_token: label = (pos_mel == 0).astype(np.float32) stop_loss = cross_entropy(stop_preds, label) loss = loss + stop_loss if local_rank == 0: writer.add_scalars('training_loss', { 'mel_loss': mel_loss.numpy(), 'post_mel_loss': post_mel_loss.numpy() }, global_step) if args.stop_token: writer.add_scalar('stop_loss', stop_loss.numpy(), global_step) if args.use_data_parallel: writer.add_scalars('alphas', { 'encoder_alpha': model._layers.encoder.alpha.numpy(), 'decoder_alpha': model._layers.decoder.alpha.numpy(), }, global_step) else: writer.add_scalars('alphas', { 'encoder_alpha': model.encoder.alpha.numpy(), 'decoder_alpha': model.decoder.alpha.numpy(), }, global_step) writer.add_scalar('learning_rate', optimizer._learning_rate.step().numpy(), global_step) if global_step % args.image_step == 1: for i, prob in enumerate(attn_probs): for j in range(4): x = np.uint8( cm.viridis(prob.numpy()[j * 16]) * 255) writer.add_image( 'Attention_%d_0' % global_step, x, i * 4 + j, dataformats="HWC") for i, prob in enumerate(attn_enc): for j in range(4): x = np.uint8( cm.viridis(prob.numpy()[j * 16]) * 255) writer.add_image( 'Attention_enc_%d_0' % global_step, x, i * 4 + j, dataformats="HWC") for i, prob in enumerate(attn_dec): for j in range(4): x = np.uint8( cm.viridis(prob.numpy()[j * 16]) * 255) writer.add_image( 'Attention_dec_%d_0' % global_step, x, i * 4 + j, dataformats="HWC") if args.use_data_parallel: loss = model.scale_loss(loss) loss.backward() model.apply_collective_grads() else: loss.backward() optimizer.minimize( loss, grad_clip=fluid.dygraph_grad_clip.GradClipByGlobalNorm(cfg[ 'grad_clip_thresh'])) model.clear_gradients() # save checkpoint if local_rank == 0 and global_step % args.save_step == 0: if not os.path.exists(args.save_path): os.mkdir(args.save_path) save_path = os.path.join(args.save_path, 'transformer/%d' % global_step) dg.save_dygraph(model.state_dict(), save_path) dg.save_dygraph(optimizer.state_dict(), save_path) if local_rank == 0: writer.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description="Train TransformerTTS model") add_config_options_to_parser(parser) args = parser.parse_args() # Print the whole config setting. pprint(args) main(args)