mirror of https://github.com/coqui-ai/TTS.git
658 lines
27 KiB
Python
658 lines
27 KiB
Python
import argparse
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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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import torch.nn as nn
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from torch import optim
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from torch.utils.data import DataLoader
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from TTS.datasets.TTSDataset import MyDataset
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from distribute import (DistributedSampler, apply_gradient_allreduce,
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init_distributed, reduce_tensor)
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from TTS.layers.losses import L1LossMasked, MSELossMasked
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.generic_utils import (NoamLR, check_update, count_parameters,
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create_experiment_folder, get_git_branch,
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load_config, remove_experiment_folder,
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save_best_model, save_checkpoint, weight_decay,
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set_init_dict, copy_config_file, setup_model,
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split_dataset, gradual_training_scheduler)
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from TTS.utils.logger import Logger
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from TTS.utils.speakers import load_speaker_mapping, save_speaker_mapping, \
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get_speakers
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from TTS.utils.synthesis import synthesis
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from TTS.utils.text.symbols import phonemes, symbols
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from TTS.utils.visual import plot_alignment, plot_spectrogram
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from TTS.datasets.preprocess import get_preprocessor_by_name
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from TTS.utils.radam import RAdam
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.manual_seed(54321)
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use_cuda = torch.cuda.is_available()
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num_gpus = torch.cuda.device_count()
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print(" > Using CUDA: ", use_cuda)
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print(" > Number of GPUs: ", num_gpus)
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def setup_loader(ap, is_val=False, verbose=False):
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global meta_data_train
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global meta_data_eval
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if "meta_data_train" not in globals():
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if c.meta_file_train is not None:
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meta_data_train = get_preprocessor_by_name(c.dataset)(c.data_path, c.meta_file_train)
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else:
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meta_data_train = get_preprocessor_by_name(c.dataset)(c.data_path)
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if "meta_data_eval" not in globals() and c.run_eval:
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if c.meta_file_val is not None:
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meta_data_eval = get_preprocessor_by_name(c.dataset)(c.data_path, c.meta_file_val)
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else:
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meta_data_eval, meta_data_train = split_dataset(meta_data_train)
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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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c.r,
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c.text_cleaner,
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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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batch_group_size=0 if is_val else c.batch_group_size * 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 train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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ap, global_step, epoch):
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data_loader = setup_loader(ap, is_val=False, verbose=(epoch == 0))
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if c.use_speaker_embedding:
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speaker_mapping = load_speaker_mapping(OUT_PATH)
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model.train()
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epoch_time = 0
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avg_postnet_loss = 0
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avg_decoder_loss = 0
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avg_stop_loss = 0
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avg_step_time = 0
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avg_loader_time = 0
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print("\n > Epoch {}/{}".format(epoch, c.epochs), flush=True)
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if use_cuda:
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batch_n_iter = int(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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for num_iter, data in enumerate(data_loader):
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start_time = time.time()
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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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linear_input = data[3] if c.model in ["Tacotron", "TacotronGST"] else None
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mel_input = data[4]
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mel_lengths = data[5]
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stop_targets = data[6]
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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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loader_time = time.time() - end_time
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if c.use_speaker_embedding:
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speaker_ids = [speaker_mapping[speaker_name]
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for speaker_name in speaker_names]
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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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# set stop targets view, we predict a single stop token per r frames prediction
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stop_targets = stop_targets.view(text_input.shape[0],
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stop_targets.size(1) // c.r, -1)
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stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
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global_step += 1
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# setup lr
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if c.lr_decay:
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scheduler.step()
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optimizer.zero_grad()
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if optimizer_st:
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optimizer_st.zero_grad()
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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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linear_input = linear_input.cuda(non_blocking=True) if c.model in ["Tacotron", "TacotronGST"] else None
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stop_targets = stop_targets.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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# forward pass model
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decoder_output, postnet_output, alignments, stop_tokens = model(
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text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
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# loss computation
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stop_loss = criterion_st(stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
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if c.loss_masking:
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decoder_loss = criterion(decoder_output, mel_input, mel_lengths)
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if c.model in ["Tacotron", "TacotronGST"]:
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postnet_loss = criterion(postnet_output, linear_input, mel_lengths)
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else:
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postnet_loss = criterion(postnet_output, mel_input, mel_lengths)
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else:
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decoder_loss = criterion(decoder_output, mel_input)
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if c.model in ["Tacotron", "TacotronGST"]:
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postnet_loss = criterion(postnet_output, linear_input)
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else:
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postnet_loss = criterion(postnet_output, mel_input)
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loss = decoder_loss + postnet_loss
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if not c.separate_stopnet and c.stopnet:
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loss += stop_loss
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loss.backward()
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optimizer, current_lr = weight_decay(optimizer, c.wd)
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grad_norm, _ = check_update(model, c.grad_clip)
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optimizer.step()
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# backpass and check the grad norm for stop loss
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if c.separate_stopnet:
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stop_loss.backward()
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optimizer_st, _ = weight_decay(optimizer_st, c.wd)
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grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
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optimizer_st.step()
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else:
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grad_norm_st = 0
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step_time = time.time() - start_time
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epoch_time += step_time
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if global_step % c.print_step == 0:
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print(
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" | > Step:{}/{} GlobalStep:{} TotalLoss:{:.5f} PostnetLoss:{:.5f} "
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"DecoderLoss:{:.5f} StopLoss:{:.5f} GradNorm:{:.5f} "
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"GradNormST:{:.5f} AvgTextLen:{:.1f} AvgSpecLen:{:.1f} StepTime:{:.2f} "
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"LoaderTime:{:.2f} LR:{:.6f}".format(
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num_iter, batch_n_iter, global_step, loss.item(),
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postnet_loss.item(), decoder_loss.item(), stop_loss.item(),
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grad_norm, grad_norm_st, avg_text_length, avg_spec_length, step_time,
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loader_time, current_lr),
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flush=True)
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# aggregate losses from processes
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if num_gpus > 1:
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postnet_loss = reduce_tensor(postnet_loss.data, num_gpus)
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decoder_loss = reduce_tensor(decoder_loss.data, num_gpus)
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loss = reduce_tensor(loss.data, num_gpus)
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stop_loss = reduce_tensor(stop_loss.data, num_gpus) if c.stopnet else stop_loss
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if args.rank == 0:
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avg_postnet_loss += float(postnet_loss.item())
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avg_decoder_loss += float(decoder_loss.item())
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avg_stop_loss += stop_loss if isinstance(stop_loss, float) else float(stop_loss.item())
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avg_step_time += step_time
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avg_loader_time += loader_time
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# Plot Training Iter Stats
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# reduce TB load
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if global_step % 10 == 0:
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iter_stats = {"loss_posnet": postnet_loss.item(),
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"loss_decoder": decoder_loss.item(),
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"lr": current_lr,
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"grad_norm": grad_norm,
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"grad_norm_st": grad_norm_st,
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"step_time": step_time}
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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, optimizer_st,
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postnet_loss.item(), OUT_PATH, global_step,
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epoch)
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# Diagnostic visualizations
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const_spec = postnet_output[0].data.cpu().numpy()
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gt_spec = linear_input[0].data.cpu().numpy() if c.model in ["Tacotron", "TacotronGST"] else mel_input[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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if c.model in ["Tacotron", "TacotronGST"]:
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train_audio = ap.inv_spectrogram(const_spec.T)
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else:
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train_audio = ap.inv_mel_spectrogram(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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avg_postnet_loss /= (num_iter + 1)
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avg_decoder_loss /= (num_iter + 1)
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avg_stop_loss /= (num_iter + 1)
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avg_total_loss = avg_decoder_loss + avg_postnet_loss + avg_stop_loss
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avg_step_time /= (num_iter + 1)
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avg_loader_time /= (num_iter + 1)
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# print epoch stats
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print(
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" | > EPOCH END -- GlobalStep:{} AvgTotalLoss:{:.5f} "
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"AvgPostnetLoss:{:.5f} AvgDecoderLoss:{:.5f} "
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"AvgStopLoss:{:.5f} EpochTime:{:.2f} "
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"AvgStepTime:{:.2f} AvgLoaderTime:{:.2f}".format(global_step, avg_total_loss,
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avg_postnet_loss, avg_decoder_loss,
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avg_stop_loss, epoch_time, avg_step_time,
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avg_loader_time),
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flush=True)
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# Plot Epoch Stats
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if args.rank == 0:
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# Plot Training Epoch Stats
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epoch_stats = {"loss_postnet": avg_postnet_loss,
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"loss_decoder": avg_decoder_loss,
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"stop_loss": avg_stop_loss,
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"epoch_time": epoch_time}
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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 avg_postnet_loss, global_step
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def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
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data_loader = setup_loader(ap, is_val=True)
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if c.use_speaker_embedding:
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speaker_mapping = load_speaker_mapping(OUT_PATH)
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model.eval()
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epoch_time = 0
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avg_postnet_loss = 0
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avg_decoder_loss = 0
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avg_stop_loss = 0
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print("\n > Validation")
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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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]
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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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with torch.no_grad():
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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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# 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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linear_input = data[3] if c.model in ["Tacotron", "TacotronGST"] else None
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mel_input = data[4]
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mel_lengths = data[5]
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stop_targets = data[6]
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if c.use_speaker_embedding:
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speaker_ids = [speaker_mapping[speaker_name]
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for speaker_name in speaker_names]
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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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# set stop targets view, we predict a single stop token per r frames prediction
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stop_targets = stop_targets.view(text_input.shape[0],
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stop_targets.size(1) // c.r,
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-1)
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stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
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# dispatch data to GPU
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if use_cuda:
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text_input = text_input.cuda()
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mel_input = mel_input.cuda()
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mel_lengths = mel_lengths.cuda()
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linear_input = linear_input.cuda() if c.model in ["Tacotron", "TacotronGST"] else None
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stop_targets = stop_targets.cuda()
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if speaker_ids is not None:
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speaker_ids = speaker_ids.cuda()
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# forward pass
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decoder_output, postnet_output, alignments, stop_tokens =\
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model.forward(text_input, text_lengths, mel_input,
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speaker_ids=speaker_ids)
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# loss computation
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stop_loss = criterion_st(stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
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if c.loss_masking:
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decoder_loss = criterion(decoder_output, mel_input, mel_lengths)
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if c.model in ["Tacotron", "TacotronGST"]:
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postnet_loss = criterion(postnet_output, linear_input, mel_lengths)
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else:
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postnet_loss = criterion(postnet_output, mel_input, mel_lengths)
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else:
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decoder_loss = criterion(decoder_output, mel_input)
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if c.model in ["Tacotron", "TacotronGST"]:
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postnet_loss = criterion(postnet_output, linear_input)
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else:
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postnet_loss = criterion(postnet_output, mel_input)
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loss = decoder_loss + postnet_loss + stop_loss
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step_time = time.time() - start_time
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epoch_time += step_time
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if num_iter % c.print_step == 0:
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print(
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" | > TotalLoss: {:.5f} PostnetLoss: {:.5f} DecoderLoss:{:.5f} "
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"StopLoss: {:.5f} ".format(loss.item(),
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postnet_loss.item(),
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decoder_loss.item(),
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stop_loss.item()),
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flush=True)
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# aggregate losses from processes
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if num_gpus > 1:
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postnet_loss = reduce_tensor(postnet_loss.data, num_gpus)
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decoder_loss = reduce_tensor(decoder_loss.data, num_gpus)
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if c.stopnet:
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stop_loss = reduce_tensor(stop_loss.data, num_gpus)
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avg_postnet_loss += float(postnet_loss.item())
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avg_decoder_loss += float(decoder_loss.item())
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avg_stop_loss += stop_loss.item()
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if args.rank == 0:
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# Diagnostic visualizations
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idx = np.random.randint(mel_input.shape[0])
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const_spec = postnet_output[idx].data.cpu().numpy()
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gt_spec = linear_input[idx].data.cpu().numpy() if c.model in ["Tacotron", "TacotronGST"] else mel_input[idx].data.cpu().numpy()
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align_img = alignments[idx].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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tb_logger.tb_eval_figures(global_step, eval_figures)
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# Sample audio
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if c.model in ["Tacotron", "TacotronGST"]:
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eval_audio = ap.inv_spectrogram(const_spec.T)
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else:
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eval_audio = ap.inv_mel_spectrogram(const_spec.T)
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tb_logger.tb_eval_audios(global_step, {"ValAudio": eval_audio}, c.audio["sample_rate"])
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# compute average losses
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avg_postnet_loss /= (num_iter + 1)
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avg_decoder_loss /= (num_iter + 1)
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avg_stop_loss /= (num_iter + 1)
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# Plot Validation Stats
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epoch_stats = {"loss_postnet": avg_postnet_loss,
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"loss_decoder": avg_decoder_loss,
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"stop_loss": avg_stop_loss}
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tb_logger.tb_eval_stats(global_step, epoch_stats)
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if args.rank == 0 and epoch > c.test_delay_epochs:
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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 = synthesis(
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model, test_sentence, c, use_cuda, ap,
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speaker_id=speaker_id,
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style_wav=style_wav)
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file_path = os.path.join(AUDIO_PATH, str(global_step))
|
|
os.makedirs(file_path, exist_ok=True)
|
|
file_path = os.path.join(file_path,
|
|
"TestSentence_{}.wav".format(idx))
|
|
ap.save_wav(wav, file_path)
|
|
test_audios['{}-audio'.format(idx)] = wav
|
|
test_figures['{}-prediction'.format(idx)] = plot_spectrogram(postnet_output, ap)
|
|
test_figures['{}-alignment'.format(idx)] = plot_alignment(alignment)
|
|
except:
|
|
print(" !! Error creating Test Sentence -", idx)
|
|
traceback.print_exc()
|
|
tb_logger.tb_test_audios(global_step, test_audios, c.audio['sample_rate'])
|
|
tb_logger.tb_test_figures(global_step, test_figures)
|
|
return avg_postnet_loss
|
|
|
|
|
|
#FIXME: move args definition/parsing inside of main?
|
|
def main(args): #pylint: disable=redefined-outer-name
|
|
# Audio processor
|
|
ap = AudioProcessor(**c.audio)
|
|
|
|
# DISTRUBUTED
|
|
if num_gpus > 1:
|
|
init_distributed(args.rank, num_gpus, args.group_id,
|
|
c.distributed["backend"], c.distributed["url"])
|
|
num_chars = len(phonemes) if c.use_phonemes else len(symbols)
|
|
|
|
if c.use_speaker_embedding:
|
|
speakers = get_speakers(c.data_path, c.meta_file_train, c.dataset)
|
|
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
|
|
|
|
model = setup_model(num_chars, num_speakers, c)
|
|
|
|
print(" | > Num output units : {}".format(ap.num_freq), flush=True)
|
|
|
|
optimizer = RAdam(model.parameters(), lr=c.lr, weight_decay=0)
|
|
if c.stopnet and c.separate_stopnet:
|
|
optimizer_st = RAdam(
|
|
model.decoder.stopnet.parameters(), lr=c.lr, weight_decay=0)
|
|
else:
|
|
optimizer_st = None
|
|
|
|
if c.loss_masking:
|
|
criterion = L1LossMasked() if c.model in ["Tacotron", "TacotronGST"] else MSELossMasked()
|
|
else:
|
|
criterion = nn.L1Loss() if c.model in ["Tacotron", "TacotronGST"] else nn.MSELoss()
|
|
criterion_st = nn.BCEWithLogitsLoss() if c.stopnet else None
|
|
|
|
if args.restore_path:
|
|
checkpoint = torch.load(args.restore_path)
|
|
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, c)
|
|
model.load_state_dict(model_dict)
|
|
del model_dict
|
|
for group in optimizer.param_groups:
|
|
group['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 = model.cuda()
|
|
criterion.cuda()
|
|
if criterion_st:
|
|
criterion_st.cuda()
|
|
|
|
# DISTRUBUTED
|
|
if num_gpus > 1:
|
|
model = apply_gradient_allreduce(model)
|
|
|
|
if c.lr_decay:
|
|
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
|
|
for epoch in range(0, c.epochs):
|
|
# set gradual training
|
|
if c.gradual_training is not None:
|
|
r, c.batch_size = gradual_training_scheduler(global_step, c)
|
|
c.r = r
|
|
model.decoder.set_r(r)
|
|
print(" > Number of outputs per iteration:", model.decoder.r)
|
|
|
|
train_loss, global_step = train(model, criterion, criterion_st,
|
|
optimizer, optimizer_st, scheduler,
|
|
ap, global_step, epoch)
|
|
val_loss = evaluate(model, criterion, criterion_st, ap, global_step, epoch)
|
|
print(
|
|
" | > Training Loss: {:.5f} Validation Loss: {:.5f}".format(
|
|
train_loss, val_loss),
|
|
flush=True)
|
|
target_loss = train_loss
|
|
if c.run_eval:
|
|
target_loss = val_loss
|
|
best_loss = save_best_model(model, optimizer, target_loss, best_loss,
|
|
OUT_PATH, global_step, epoch)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
'--restore_path',
|
|
type=str,
|
|
help='Path to model outputs (checkpoint, tensorboard etc.).',
|
|
default=0)
|
|
parser.add_argument(
|
|
'--config_path',
|
|
type=str,
|
|
help='Path to config file for training.',
|
|
)
|
|
parser.add_argument(
|
|
'--debug',
|
|
type=bool,
|
|
default=True,
|
|
help='Do not verify commit integrity to run training.')
|
|
parser.add_argument(
|
|
'--data_path',
|
|
type=str,
|
|
default='',
|
|
help='Defines the data path. It overwrites config.json.')
|
|
parser.add_argument(
|
|
'--output_path',
|
|
type=str,
|
|
help='path for training outputs.',
|
|
default='')
|
|
parser.add_argument(
|
|
'--output_folder',
|
|
type=str,
|
|
default='',
|
|
help='folder name for training outputs.'
|
|
)
|
|
|
|
# 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()
|
|
|
|
# setup output paths and read configs
|
|
c = load_config(args.config_path)
|
|
_ = os.path.dirname(os.path.realpath(__file__))
|
|
if args.data_path != '':
|
|
c.data_path = args.data_path
|
|
|
|
if args.output_path == '':
|
|
OUT_PATH = os.path.join(_, c.output_path)
|
|
else:
|
|
OUT_PATH = args.output_path
|
|
|
|
if args.group_id == '' and args.output_folder == '':
|
|
OUT_PATH = create_experiment_folder(OUT_PATH, c.run_name, args.debug)
|
|
else:
|
|
OUT_PATH = os.path.join(OUT_PATH, args.output_folder)
|
|
|
|
AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios')
|
|
|
|
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)
|
|
|
|
if args.rank == 0:
|
|
LOG_DIR = OUT_PATH
|
|
tb_logger = Logger(LOG_DIR)
|
|
|
|
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)
|