mirror of https://github.com/coqui-ai/TTS.git
622 lines
26 KiB
Python
622 lines
26 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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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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from random import randrange
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import numpy as np
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import torch
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from torch.nn.parallel import DistributedDataParallel as DDP_th
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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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 AlignTTSLoss
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from TTS.tts.utils.generic_utils import 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 parse_speakers
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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.arguments import parse_arguments, process_args
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.distribute import init_distributed, reduce_tensor
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from TTS.utils.generic_utils import (KeepAverage, count_parameters,
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remove_experiment_folder, set_init_dict)
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from TTS.utils.radam import RAdam
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from TTS.utils.training import NoamLR, setup_torch_training_env
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if __name__ == '__main__':
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use_cuda, num_gpus = setup_torch_training_env(True, False)
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# torch.autograd.set_detect_anomaly(True)
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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=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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add_blank=c['add_blank'] if 'add_blank' in c.keys() else False,
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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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use_noise_augment=not is_val,
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verbose=verbose,
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speaker_mapping=speaker_mapping if c.use_speaker_embedding
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and c.use_external_speaker_embedding_file else None)
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if c.use_phonemes and c.compute_input_seq_cache:
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# precompute phonemes to have a better estimate of sequence lengths.
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dataset.compute_input_seq(c.num_loader_workers)
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dataset.sort_items()
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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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# 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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item_idx = data[7]
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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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if c.use_external_speaker_embedding_file:
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# return precomputed embedding vector
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speaker_c = data[8]
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else:
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# return speaker_id to be used by an embedding layer
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speaker_c = [
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speaker_mapping[speaker_name]
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for speaker_name in speaker_names
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]
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speaker_c = torch.LongTensor(speaker_c)
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else:
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speaker_c = 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_c is not None:
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speaker_c = speaker_c.cuda(non_blocking=True)
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return text_input, text_lengths, mel_input, mel_lengths, speaker_c,\
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avg_text_length, avg_spec_length, item_idx
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def train(data_loader, model, criterion, optimizer, scheduler, ap,
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global_step, epoch, training_phase):
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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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scaler = torch.cuda.amp.GradScaler() if c.mixed_precision else 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_targets, mel_lengths, speaker_c,\
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avg_text_length, avg_spec_length, _ = format_data(data)
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loader_time = time.time() - end_time
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global_step += 1
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optimizer.zero_grad()
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# forward pass model
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with torch.cuda.amp.autocast(enabled=c.mixed_precision):
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decoder_output, dur_output, dur_mas_output, alignments, mu, log_sigma, logp = model.forward(
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text_input,
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text_lengths,
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mel_targets,
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mel_lengths,
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g=speaker_c,
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phase=training_phase)
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# compute loss
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loss_dict = criterion(mu,
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log_sigma,
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logp,
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decoder_output,
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mel_targets,
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mel_lengths,
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dur_output,
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dur_mas_output,
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text_lengths,
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global_step,
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phase=training_phase)
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# backward pass with loss scaling
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if c.mixed_precision:
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scaler.scale(loss_dict['loss']).backward()
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scaler.unscale_(optimizer)
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grad_norm = torch.nn.utils.clip_grad_norm_(
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model.parameters(), c.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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else:
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loss_dict['loss'].backward()
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grad_norm = torch.nn.utils.clip_grad_norm_(
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model.parameters(), c.grad_clip)
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optimizer.step()
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# setup lr
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if c.noam_schedule:
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scheduler.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, binary=True)
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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['loss_l1'] = reduce_tensor(loss_dict['loss_l1'].data,
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num_gpus)
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loss_dict['loss_ssim'] = reduce_tensor(
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loss_dict['loss_ssim'].data, num_gpus)
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loss_dict['loss_dur'] = reduce_tensor(
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loss_dict['loss_dur'].data, num_gpus)
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loss_dict['loss'] = reduce_tensor(loss_dict['loss'].data,
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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,
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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,
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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,
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optimizer,
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global_step,
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epoch,
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1,
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OUT_PATH,
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model_characters,
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model_loss=loss_dict['loss'])
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# wait all kernels to be completed
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torch.cuda.synchronize()
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# Diagnostic visualizations
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if decoder_output is not None:
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idx = np.random.randint(mel_targets.shape[0])
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pred_spec = decoder_output[idx].detach().data.cpu(
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).numpy().T
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gt_spec = mel_targets[idx].data.cpu().numpy().T
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align_img = alignments[idx].data.cpu()
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figures = {
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"prediction": plot_spectrogram(pred_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(pred_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,
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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(data_loader, model, criterion, ap, global_step, epoch,
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training_phase):
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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_targets, mel_lengths, speaker_c,\
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_, _, _ = format_data(data)
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# forward pass model
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with torch.cuda.amp.autocast(enabled=c.mixed_precision):
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decoder_output, dur_output, dur_mas_output, alignments, mu, log_sigma, logp_max_path = model.forward(
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text_input,
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text_lengths,
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mel_targets,
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mel_lengths,
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g=speaker_c)
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# compute loss
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loss_dict = criterion(mu, log_sigma, logp_max_path,
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decoder_output, mel_targets,
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mel_lengths, dur_output,
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dur_mas_output, text_lengths,
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global_step, training_phase)
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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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binary=True)
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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['loss_l1'] = reduce_tensor(
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loss_dict['loss_l1'].data, num_gpus)
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loss_dict['loss_ssim'] = reduce_tensor(
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loss_dict['loss_ssim'].data, num_gpus)
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loss_dict['loss_dur'] = reduce_tensor(
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loss_dict['loss_dur'].data, num_gpus)
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loss_dict['loss'] = reduce_tensor(loss_dict['loss'].data,
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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,
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keep_avg.avg_values)
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if args.rank == 0:
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# Diagnostic visualizations
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idx = np.random.randint(mel_targets.shape[0])
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pred_spec = decoder_output[idx].detach().data.cpu().numpy().T
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gt_spec = mel_targets[idx].data.cpu().numpy().T
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align_img = alignments[idx].data.cpu()
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eval_figures = {
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"prediction": plot_spectrogram(pred_spec,
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ap,
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output_fig=False),
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"ground_truth": plot_spectrogram(gt_spec,
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ap,
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output_fig=False),
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"alignment": plot_alignment(align_img, output_fig=False)
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}
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# Sample audio
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eval_audio = ap.inv_melspectrogram(pred_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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if c.use_speaker_embedding:
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if c.use_external_speaker_embedding_file:
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speaker_embedding = speaker_mapping[list(
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speaker_mapping.keys())[randrange(
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len(speaker_mapping) - 1)]]['embedding']
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speaker_id = None
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else:
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speaker_id = 0
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speaker_embedding = None
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else:
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speaker_id = None
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speaker_embedding = 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, _, postnet_output, _, _ = 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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speaker_embedding=speaker_embedding,
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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(
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idx)] = plot_spectrogram(postnet_output, ap)
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test_figures['{}-alignment'.format(idx)] = plot_alignment(
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alignment)
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except: #pylint: disable=bare-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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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, model_characters, speaker_mapping
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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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# set model characters
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model_characters = phonemes if c.use_phonemes else symbols
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num_chars = len(model_characters)
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# load data instances
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meta_data_train, meta_data_eval = load_meta_data(c.datasets,
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eval_split=True)
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|
|
# set the portion of the data used for training if set in config.json
|
|
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
|
|
num_speakers, speaker_embedding_dim, speaker_mapping = parse_speakers(
|
|
c, args, meta_data_train, OUT_PATH)
|
|
|
|
# setup model
|
|
model = setup_model(num_chars,
|
|
num_speakers,
|
|
c,
|
|
speaker_embedding_dim=speaker_embedding_dim)
|
|
optimizer = RAdam(model.parameters(),
|
|
lr=c.lr,
|
|
weight_decay=0,
|
|
betas=(0.9, 0.98),
|
|
eps=1e-9)
|
|
criterion = AlignTTSLoss(c)
|
|
|
|
if args.restore_path:
|
|
print(
|
|
f" > Restoring from {os.path.basename(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: #pylint: disable=bare-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
|
|
|
|
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_th(model, device_ids=[args.rank])
|
|
|
|
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 args.restore_step == 0 or not args.best_path:
|
|
best_loss = float('inf')
|
|
print(" > Starting with inf best loss.")
|
|
else:
|
|
print(" > Restoring best loss from "
|
|
f"{os.path.basename(args.best_path)} ...")
|
|
best_loss = torch.load(args.best_path,
|
|
map_location='cpu')['model_loss']
|
|
print(f" > Starting with loaded last best loss {best_loss}.")
|
|
keep_all_best = c.get('keep_all_best', False)
|
|
keep_after = c.get('keep_after', 10000) # void if keep_all_best False
|
|
|
|
# define dataloaders
|
|
train_loader = setup_loader(ap, 1, is_val=False, verbose=True)
|
|
eval_loader = setup_loader(ap, 1, is_val=True, verbose=True)
|
|
|
|
global_step = args.restore_step
|
|
|
|
def set_phase():
|
|
"""Set AlignTTS training phase"""
|
|
if isinstance(c.phase_start_steps, list):
|
|
vals = [i < global_step for i in c.phase_start_steps]
|
|
if not True in vals:
|
|
phase = 0
|
|
else:
|
|
phase = len(c.phase_start_steps) - [
|
|
i < global_step for i in c.phase_start_steps
|
|
][::-1].index(True) - 1
|
|
else:
|
|
phase = None
|
|
return phase
|
|
|
|
for epoch in range(0, c.epochs):
|
|
cur_phase = set_phase()
|
|
print(f"\n > Current AlignTTS phase: {cur_phase}")
|
|
c_logger.print_epoch_start(epoch, c.epochs)
|
|
train_avg_loss_dict, global_step = train(train_loader, model,
|
|
criterion, optimizer,
|
|
scheduler, ap,
|
|
global_step, epoch,
|
|
cur_phase)
|
|
eval_avg_loss_dict = evaluate(eval_loader, model, criterion, ap,
|
|
global_step, epoch, cur_phase)
|
|
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,
|
|
1,
|
|
OUT_PATH,
|
|
model_characters,
|
|
keep_all_best=keep_all_best,
|
|
keep_after=keep_after)
|
|
|
|
args = parse_arguments(sys.argv)
|
|
c, OUT_PATH, AUDIO_PATH, c_logger, tb_logger = process_args(
|
|
args, model_class='tts')
|
|
|
|
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)
|