mirror of https://github.com/coqui-ai/TTS.git
655 lines
25 KiB
Python
655 lines
25 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import argparse
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import glob
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import os
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import sys
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import time
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import traceback
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from torch.nn.parallel import DistributedDataParallel as DDP
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from TTS.tts.datasets.preprocess import load_meta_data
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from TTS.tts.datasets.TTSDataset import MyDataset
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from TTS.tts.layers.losses import GlowTTSLoss
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from TTS.utils.console_logger import ConsoleLogger
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from TTS.tts.utils.distribute import (DistributedSampler,
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init_distributed,
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reduce_tensor)
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from TTS.tts.utils.generic_utils import check_config, setup_model
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from TTS.tts.utils.io import save_best_model, save_checkpoint
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from TTS.tts.utils.measures import alignment_diagonal_score
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from TTS.tts.utils.speakers import (get_speakers,
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load_speaker_mapping,
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save_speaker_mapping)
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from TTS.tts.utils.synthesis import synthesis
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from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
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from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.generic_utils import (
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KeepAverage, count_parameters, create_experiment_folder, get_git_branch,
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remove_experiment_folder, set_init_dict)
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from TTS.utils.io import copy_config_file, load_config
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from TTS.utils.radam import RAdam
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from TTS.utils.tensorboard_logger import TensorboardLogger
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from TTS.utils.training import (NoamLR, adam_weight_decay,
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check_update,
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gradual_training_scheduler,
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set_weight_decay,
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setup_torch_training_env)
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use_cuda, num_gpus = setup_torch_training_env(True, False)
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def setup_loader(ap, r, is_val=False, verbose=False):
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if is_val and not c.run_eval:
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loader = None
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else:
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dataset = MyDataset(
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r,
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c.text_cleaner,
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compute_linear_spec=True if c.model.lower() == 'tacotron' else False,
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meta_data=meta_data_eval if is_val else meta_data_train,
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ap=ap,
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tp=c.characters if 'characters' in c.keys() else None,
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batch_group_size=0 if is_val else c.batch_group_size *
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c.batch_size,
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min_seq_len=c.min_seq_len,
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max_seq_len=c.max_seq_len,
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phoneme_cache_path=c.phoneme_cache_path,
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use_phonemes=c.use_phonemes,
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phoneme_language=c.phoneme_language,
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enable_eos_bos=c.enable_eos_bos_chars,
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verbose=verbose)
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sampler = DistributedSampler(dataset) if num_gpus > 1 else None
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loader = DataLoader(
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dataset,
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batch_size=c.eval_batch_size if is_val else c.batch_size,
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shuffle=False,
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collate_fn=dataset.collate_fn,
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drop_last=False,
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sampler=sampler,
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num_workers=c.num_val_loader_workers
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if is_val else c.num_loader_workers,
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pin_memory=False)
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return loader
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def format_data(data):
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if c.use_speaker_embedding:
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speaker_mapping = load_speaker_mapping(OUT_PATH)
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# setup input data
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text_input = data[0]
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text_lengths = data[1]
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speaker_names = data[2]
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mel_input = data[4].permute(0, 2, 1) # B x D x T
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mel_lengths = data[5]
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attn_mask = data[8]
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avg_text_length = torch.mean(text_lengths.float())
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avg_spec_length = torch.mean(mel_lengths.float())
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if c.use_speaker_embedding:
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speaker_ids = [
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speaker_mapping[speaker_name] for speaker_name in speaker_names
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]
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speaker_ids = torch.LongTensor(speaker_ids)
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else:
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speaker_ids = None
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# dispatch data to GPU
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if use_cuda:
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text_input = text_input.cuda(non_blocking=True)
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text_lengths = text_lengths.cuda(non_blocking=True)
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mel_input = mel_input.cuda(non_blocking=True)
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mel_lengths = mel_lengths.cuda(non_blocking=True)
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if speaker_ids is not None:
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speaker_ids = speaker_ids.cuda(non_blocking=True)
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if attn_mask is not None:
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attn_mask = attn_mask.cuda(non_blocking=True)
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return text_input, text_lengths, mel_input, mel_lengths, speaker_ids,\
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avg_text_length, avg_spec_length, attn_mask
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def data_depended_init(model, ap):
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"""Data depended initialization for normalization layers."""
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if hasattr(model, 'module'):
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for f in model.module.decoder.flows:
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if getattr(f, "set_ddi", False):
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f.set_ddi(True)
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else:
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for f in model.decoder.flows:
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if getattr(f, "set_ddi", False):
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f.set_ddi(True)
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data_loader = setup_loader(ap, 1, is_val=False)
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model.train()
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print(" > Data depended initialization ... ")
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with torch.no_grad():
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for num_iter, data in enumerate(data_loader):
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# format data
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text_input, text_lengths, mel_input, mel_lengths, speaker_ids,\
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avg_text_length, avg_spec_length, attn_mask = format_data(data)
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# forward pass model
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_ = model.forward(
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text_input, text_lengths, mel_input, mel_lengths, attn_mask)
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break
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if hasattr(model, 'module'):
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for f in model.module.decoder.flows:
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if getattr(f, "set_ddi", False):
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f.set_ddi(False)
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else:
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for f in model.decoder.flows:
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if getattr(f, "set_ddi", False):
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f.set_ddi(False)
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return model
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def train(model, criterion, optimizer, scheduler,
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ap, global_step, epoch, amp):
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data_loader = setup_loader(ap, 1, is_val=False,
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verbose=(epoch == 0))
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model.train()
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epoch_time = 0
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keep_avg = KeepAverage()
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if use_cuda:
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batch_n_iter = int(
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len(data_loader.dataset) / (c.batch_size * num_gpus))
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else:
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batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
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end_time = time.time()
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c_logger.print_train_start()
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for num_iter, data in enumerate(data_loader):
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start_time = time.time()
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# format data
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text_input, text_lengths, mel_input, mel_lengths, speaker_ids,\
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avg_text_length, avg_spec_length, attn_mask = format_data(data)
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loader_time = time.time() - end_time
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global_step += 1
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# setup lr
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if c.noam_schedule:
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scheduler.step()
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optimizer.zero_grad()
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# forward pass model
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z, logdet, y_mean, y_log_scale, alignments, o_dur_log, o_total_dur = model.forward(
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text_input, text_lengths, mel_input, mel_lengths, attn_mask)
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# compute loss
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loss_dict = criterion(z, y_mean, y_log_scale, logdet, mel_lengths,
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o_dur_log, o_total_dur, text_lengths)
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# backward pass
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if amp is not None:
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with amp.scale_loss( loss_dict['loss'], optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss_dict['loss'].backward()
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if amp:
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amp_opt_params = amp.master_params(optimizer)
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else:
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amp_opt_params = None
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grad_norm, _ = check_update(model, c.grad_clip, ignore_stopnet=True, amp_opt_params=amp_opt_params)
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optimizer.step()
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# current_lr
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current_lr = optimizer.param_groups[0]['lr']
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# compute alignment error (the lower the better )
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align_error = 1 - alignment_diagonal_score(alignments)
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loss_dict['align_error'] = align_error
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step_time = time.time() - start_time
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epoch_time += step_time
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# aggregate losses from processes
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if num_gpus > 1:
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loss_dict['log_mle'] = reduce_tensor(loss_dict['log_mle'].data, num_gpus)
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loss_dict['loss_dur'] = reduce_tensor(loss_dict['loss_dur'].data, num_gpus)
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loss_dict['loss'] = reduce_tensor(loss_dict['loss'] .data, num_gpus)
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# detach loss values
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loss_dict_new = dict()
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for key, value in loss_dict.items():
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if isinstance(value, (int, float)):
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loss_dict_new[key] = value
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else:
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loss_dict_new[key] = value.item()
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loss_dict = loss_dict_new
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# update avg stats
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update_train_values = dict()
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for key, value in loss_dict.items():
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update_train_values['avg_' + key] = value
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update_train_values['avg_loader_time'] = loader_time
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update_train_values['avg_step_time'] = step_time
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keep_avg.update_values(update_train_values)
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# print training progress
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if global_step % c.print_step == 0:
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log_dict = {
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"avg_spec_length": [avg_spec_length, 1], # value, precision
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"avg_text_length": [avg_text_length, 1],
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"step_time": [step_time, 4],
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"loader_time": [loader_time, 2],
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"current_lr": current_lr,
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}
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c_logger.print_train_step(batch_n_iter, num_iter, global_step,
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log_dict, loss_dict, keep_avg.avg_values)
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if args.rank == 0:
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# Plot Training Iter Stats
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# reduce TB load
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if global_step % c.tb_plot_step == 0:
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iter_stats = {
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"lr": current_lr,
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"grad_norm": grad_norm,
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"step_time": step_time
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}
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iter_stats.update(loss_dict)
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tb_logger.tb_train_iter_stats(global_step, iter_stats)
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if global_step % c.save_step == 0:
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if c.checkpoint:
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# save model
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save_checkpoint(model, optimizer, global_step, epoch, 1, OUT_PATH,
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model_loss=loss_dict['loss'],
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amp_state_dict=amp.state_dict() if amp else None)
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# Diagnostic visualizations
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# direct pass on model for spec predictions
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spec_pred, *_ = model.inference(text_input[:1], text_lengths[:1])
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spec_pred = spec_pred.permute(0, 2, 1)
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gt_spec = mel_input.permute(0, 2, 1)
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const_spec = spec_pred[0].data.cpu().numpy()
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gt_spec = gt_spec[0].data.cpu().numpy()
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align_img = alignments[0].data.cpu().numpy()
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figures = {
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"prediction": plot_spectrogram(const_spec, ap),
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"ground_truth": plot_spectrogram(gt_spec, ap),
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"alignment": plot_alignment(align_img),
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}
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tb_logger.tb_train_figures(global_step, figures)
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# Sample audio
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train_audio = ap.inv_melspectrogram(const_spec.T)
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tb_logger.tb_train_audios(global_step,
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{'TrainAudio': train_audio},
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c.audio["sample_rate"])
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end_time = time.time()
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# print epoch stats
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c_logger.print_train_epoch_end(global_step, epoch, epoch_time, keep_avg)
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# Plot Epoch Stats
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if args.rank == 0:
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epoch_stats = {"epoch_time": epoch_time}
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epoch_stats.update(keep_avg.avg_values)
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tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
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if c.tb_model_param_stats:
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tb_logger.tb_model_weights(model, global_step)
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return keep_avg.avg_values, global_step
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@torch.no_grad()
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def evaluate(model, criterion, ap, global_step, epoch):
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data_loader = setup_loader(ap, 1, is_val=True)
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model.eval()
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epoch_time = 0
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keep_avg = KeepAverage()
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c_logger.print_eval_start()
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if data_loader is not None:
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for num_iter, data in enumerate(data_loader):
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start_time = time.time()
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# format data
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text_input, text_lengths, mel_input, mel_lengths, speaker_ids,\
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avg_text_length, avg_spec_length, attn_mask = format_data(data)
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# forward pass model
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z, logdet, y_mean, y_log_scale, alignments, o_dur_log, o_total_dur = model.forward(
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text_input, text_lengths, mel_input, mel_lengths, attn_mask)
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# compute loss
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loss_dict = criterion(z, y_mean, y_log_scale, logdet, mel_lengths,
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o_dur_log, o_total_dur, text_lengths)
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# step time
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step_time = time.time() - start_time
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epoch_time += step_time
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# compute alignment score
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align_error = 1 - alignment_diagonal_score(alignments)
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loss_dict['align_error'] = align_error
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# aggregate losses from processes
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if num_gpus > 1:
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loss_dict['log_mle'] = reduce_tensor(loss_dict['log_mle'].data, num_gpus)
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loss_dict['loss_dur'] = reduce_tensor(loss_dict['loss_dur'].data, num_gpus)
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loss_dict['loss'] = reduce_tensor(loss_dict['loss'] .data, num_gpus)
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# detach loss values
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loss_dict_new = dict()
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for key, value in loss_dict.items():
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if isinstance(value, (int, float)):
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loss_dict_new[key] = value
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else:
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loss_dict_new[key] = value.item()
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loss_dict = loss_dict_new
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# update avg stats
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update_train_values = dict()
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for key, value in loss_dict.items():
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update_train_values['avg_' + key] = value
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keep_avg.update_values(update_train_values)
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if c.print_eval:
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c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
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if args.rank == 0:
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# Diagnostic visualizations
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# direct pass on model for spec predictions
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if hasattr(model, 'module'):
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spec_pred, *_ = model.module.inference(text_input[:1], text_lengths[:1])
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else:
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spec_pred, *_ = model.inference(text_input[:1], text_lengths[:1])
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spec_pred = spec_pred.permute(0, 2, 1)
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gt_spec = mel_input.permute(0, 2, 1)
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const_spec = spec_pred[0].data.cpu().numpy()
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gt_spec = gt_spec[0].data.cpu().numpy()
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align_img = alignments[0].data.cpu().numpy()
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eval_figures = {
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"prediction": plot_spectrogram(const_spec, ap),
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"ground_truth": plot_spectrogram(gt_spec, ap),
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"alignment": plot_alignment(align_img)
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}
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# Sample audio
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eval_audio = ap.inv_melspectrogram(const_spec.T)
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tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio},
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c.audio["sample_rate"])
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# Plot Validation Stats
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tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
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tb_logger.tb_eval_figures(global_step, eval_figures)
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if args.rank == 0 and epoch >= c.test_delay_epochs:
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if c.test_sentences_file is None:
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test_sentences = [
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"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
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"Be a voice, not an echo.",
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"I'm sorry Dave. I'm afraid I can't do that.",
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"This cake is great. It's so delicious and moist.",
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"Prior to November 22, 1963."
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]
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else:
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with open(c.test_sentences_file, "r") as f:
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test_sentences = [s.strip() for s in f.readlines()]
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# test sentences
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test_audios = {}
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test_figures = {}
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print(" | > Synthesizing test sentences")
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speaker_id = 0 if c.use_speaker_embedding else None
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style_wav = c.get("style_wav_for_test")
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for idx, test_sentence in enumerate(test_sentences):
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try:
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wav, alignment, decoder_output, postnet_output, stop_tokens, inputs = synthesis(
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model,
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test_sentence,
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c,
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use_cuda,
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ap,
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speaker_id=speaker_id,
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style_wav=style_wav,
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truncated=False,
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enable_eos_bos_chars=c.enable_eos_bos_chars, #pylint: disable=unused-argument
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use_griffin_lim=True,
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do_trim_silence=False)
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file_path = os.path.join(AUDIO_PATH, str(global_step))
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os.makedirs(file_path, exist_ok=True)
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file_path = os.path.join(file_path,
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"TestSentence_{}.wav".format(idx))
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ap.save_wav(wav, file_path)
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test_audios['{}-audio'.format(idx)] = wav
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test_figures['{}-prediction'.format(idx)] = plot_spectrogram(
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postnet_output, ap)
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test_figures['{}-alignment'.format(idx)] = plot_alignment(
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alignment)
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except:
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print(" !! Error creating Test Sentence -", idx)
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traceback.print_exc()
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tb_logger.tb_test_audios(global_step, test_audios,
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c.audio['sample_rate'])
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tb_logger.tb_test_figures(global_step, test_figures)
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return keep_avg.avg_values
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# FIXME: move args definition/parsing inside of main?
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def main(args): # pylint: disable=redefined-outer-name
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# pylint: disable=global-variable-undefined
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global meta_data_train, meta_data_eval, symbols, phonemes
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# Audio processor
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ap = AudioProcessor(**c.audio)
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if 'characters' in c.keys():
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symbols, phonemes = make_symbols(**c.characters)
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# DISTRUBUTED
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if num_gpus > 1:
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init_distributed(args.rank, num_gpus, args.group_id,
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c.distributed["backend"], c.distributed["url"])
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num_chars = len(phonemes) if c.use_phonemes else len(symbols)
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# load data instances
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meta_data_train, meta_data_eval = load_meta_data(c.datasets)
|
|
|
|
# set the portion of the data used for training
|
|
if 'train_portion' in c.keys():
|
|
meta_data_train = meta_data_train[:int(len(meta_data_train) * c.train_portion)]
|
|
if 'eval_portion' in c.keys():
|
|
meta_data_eval = meta_data_eval[:int(len(meta_data_eval) * c.eval_portion)]
|
|
|
|
# parse speakers
|
|
if c.use_speaker_embedding:
|
|
speakers = get_speakers(meta_data_train)
|
|
if args.restore_path:
|
|
prev_out_path = os.path.dirname(args.restore_path)
|
|
speaker_mapping = load_speaker_mapping(prev_out_path)
|
|
assert all([speaker in speaker_mapping
|
|
for speaker in speakers]), "As of now you, you cannot " \
|
|
"introduce new speakers to " \
|
|
"a previously trained model."
|
|
else:
|
|
speaker_mapping = {name: i for i, name in enumerate(speakers)}
|
|
save_speaker_mapping(OUT_PATH, speaker_mapping)
|
|
num_speakers = len(speaker_mapping)
|
|
print("Training with {} speakers: {}".format(num_speakers,
|
|
", ".join(speakers)))
|
|
else:
|
|
num_speakers = 0
|
|
|
|
# setup model
|
|
model = setup_model(num_chars, num_speakers, c)
|
|
optimizer = RAdam(model.parameters(), lr=c.lr, weight_decay=0, betas=(0.9, 0.98), eps=1e-9)
|
|
criterion = GlowTTSLoss()
|
|
|
|
if c.apex_amp_level:
|
|
# pylint: disable=import-outside-toplevel
|
|
from apex import amp
|
|
from apex.parallel import DistributedDataParallel as DDP
|
|
model.cuda()
|
|
model, optimizer = amp.initialize(model, optimizer, opt_level=c.apex_amp_level)
|
|
else:
|
|
amp = None
|
|
|
|
if args.restore_path:
|
|
checkpoint = torch.load(args.restore_path, map_location='cpu')
|
|
try:
|
|
# TODO: fix optimizer init, model.cuda() needs to be called before
|
|
# optimizer restore
|
|
optimizer.load_state_dict(checkpoint['optimizer'])
|
|
if c.reinit_layers:
|
|
raise RuntimeError
|
|
model.load_state_dict(checkpoint['model'])
|
|
except:
|
|
print(" > Partial model initialization.")
|
|
model_dict = model.state_dict()
|
|
model_dict = set_init_dict(model_dict, checkpoint['model'], c)
|
|
model.load_state_dict(model_dict)
|
|
del model_dict
|
|
|
|
if amp and 'amp' in checkpoint:
|
|
amp.load_state_dict(checkpoint['amp'])
|
|
|
|
for group in optimizer.param_groups:
|
|
group['initial_lr'] = c.lr
|
|
print(" > Model restored from step %d" % checkpoint['step'],
|
|
flush=True)
|
|
args.restore_step = checkpoint['step']
|
|
else:
|
|
args.restore_step = 0
|
|
|
|
if use_cuda:
|
|
model.cuda()
|
|
criterion.cuda()
|
|
|
|
# DISTRUBUTED
|
|
if num_gpus > 1:
|
|
model = DDP(model)
|
|
|
|
if c.noam_schedule:
|
|
scheduler = NoamLR(optimizer,
|
|
warmup_steps=c.warmup_steps,
|
|
last_epoch=args.restore_step - 1)
|
|
else:
|
|
scheduler = None
|
|
|
|
num_params = count_parameters(model)
|
|
print("\n > Model has {} parameters".format(num_params), flush=True)
|
|
|
|
if 'best_loss' not in locals():
|
|
best_loss = float('inf')
|
|
|
|
global_step = args.restore_step
|
|
model = data_depended_init(model, ap)
|
|
for epoch in range(0, c.epochs):
|
|
c_logger.print_epoch_start(epoch, c.epochs)
|
|
train_avg_loss_dict, global_step = train(model, criterion, optimizer,
|
|
scheduler, ap, global_step,
|
|
epoch, amp)
|
|
eval_avg_loss_dict = evaluate(model, criterion, ap, global_step, epoch)
|
|
c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
|
|
target_loss = train_avg_loss_dict['avg_loss']
|
|
if c.run_eval:
|
|
target_loss = eval_avg_loss_dict['avg_loss']
|
|
best_loss = save_best_model(target_loss, best_loss, model, optimizer, global_step, epoch, c.r,
|
|
OUT_PATH, amp_state_dict=amp.state_dict() if amp else None)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
'--continue_path',
|
|
type=str,
|
|
help='Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
|
|
default='',
|
|
required='--config_path' not in sys.argv)
|
|
parser.add_argument(
|
|
'--restore_path',
|
|
type=str,
|
|
help='Model file to be restored. Use to finetune a model.',
|
|
default='')
|
|
parser.add_argument(
|
|
'--config_path',
|
|
type=str,
|
|
help='Path to config file for training.',
|
|
required='--continue_path' not in sys.argv
|
|
)
|
|
parser.add_argument('--debug',
|
|
type=bool,
|
|
default=False,
|
|
help='Do not verify commit integrity to run training.')
|
|
|
|
# DISTRUBUTED
|
|
parser.add_argument(
|
|
'--rank',
|
|
type=int,
|
|
default=0,
|
|
help='DISTRIBUTED: process rank for distributed training.')
|
|
parser.add_argument('--group_id',
|
|
type=str,
|
|
default="",
|
|
help='DISTRIBUTED: process group id.')
|
|
args = parser.parse_args()
|
|
|
|
if args.continue_path != '':
|
|
args.output_path = args.continue_path
|
|
args.config_path = os.path.join(args.continue_path, 'config.json')
|
|
list_of_files = glob.glob(args.continue_path + "/*.pth.tar") # * means all if need specific format then *.csv
|
|
latest_model_file = max(list_of_files, key=os.path.getctime)
|
|
args.restore_path = latest_model_file
|
|
print(f" > Training continues for {args.restore_path}")
|
|
|
|
# setup output paths and read configs
|
|
c = load_config(args.config_path)
|
|
# check_config(c)
|
|
_ = os.path.dirname(os.path.realpath(__file__))
|
|
|
|
if c.apex_amp_level:
|
|
print(" > apex AMP level: ", c.apex_amp_level)
|
|
|
|
OUT_PATH = args.continue_path
|
|
if args.continue_path == '':
|
|
OUT_PATH = create_experiment_folder(c.output_path, c.run_name, args.debug)
|
|
|
|
AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios')
|
|
|
|
c_logger = ConsoleLogger()
|
|
|
|
if args.rank == 0:
|
|
os.makedirs(AUDIO_PATH, exist_ok=True)
|
|
new_fields = {}
|
|
if args.restore_path:
|
|
new_fields["restore_path"] = args.restore_path
|
|
new_fields["github_branch"] = get_git_branch()
|
|
copy_config_file(args.config_path,
|
|
os.path.join(OUT_PATH, 'config.json'), new_fields)
|
|
os.chmod(AUDIO_PATH, 0o775)
|
|
os.chmod(OUT_PATH, 0o775)
|
|
|
|
LOG_DIR = OUT_PATH
|
|
tb_logger = TensorboardLogger(LOG_DIR, model_name='TTS')
|
|
|
|
# write model desc to tensorboard
|
|
tb_logger.tb_add_text('model-description', c['run_description'], 0)
|
|
|
|
try:
|
|
main(args)
|
|
except KeyboardInterrupt:
|
|
remove_experiment_folder(OUT_PATH)
|
|
try:
|
|
sys.exit(0)
|
|
except SystemExit:
|
|
os._exit(0) # pylint: disable=protected-access
|
|
except Exception: # pylint: disable=broad-except
|
|
remove_experiment_folder(OUT_PATH)
|
|
traceback.print_exc()
|
|
sys.exit(1)
|