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