mirror of https://github.com/coqui-ai/TTS.git
666 lines
24 KiB
Python
666 lines
24 KiB
Python
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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from inspect import signature
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import torch
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from torch.utils.data import DataLoader
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from mozilla_voice_tts.utils.audio import AudioProcessor
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from mozilla_voice_tts.utils.console_logger import ConsoleLogger
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from mozilla_voice_tts.utils.generic_utils import (KeepAverage,
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count_parameters,
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create_experiment_folder,
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get_git_branch,
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remove_experiment_folder,
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set_init_dict)
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from mozilla_voice_tts.utils.io import copy_config_file, load_config
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from mozilla_voice_tts.utils.radam import RAdam
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from mozilla_voice_tts.utils.tensorboard_logger import TensorboardLogger
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from mozilla_voice_tts.utils.training import setup_torch_training_env
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from mozilla_voice_tts.vocoder.datasets.gan_dataset import GANDataset
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from mozilla_voice_tts.vocoder.datasets.preprocess import (load_wav_data,
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load_wav_feat_data)
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# from distribute import (DistributedSampler, apply_gradient_allreduce,
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# init_distributed, reduce_tensor)
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from mozilla_voice_tts.vocoder.layers.losses import (DiscriminatorLoss,
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GeneratorLoss)
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from mozilla_voice_tts.vocoder.utils.generic_utils import (check_config,
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plot_results,
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setup_discriminator,
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setup_generator)
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from mozilla_voice_tts.vocoder.utils.io import save_best_model, save_checkpoint
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use_cuda, num_gpus = setup_torch_training_env(True, True)
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def setup_loader(ap, 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 = GANDataset(ap=ap,
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items=eval_data if is_val else train_data,
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seq_len=c.seq_len,
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hop_len=ap.hop_length,
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pad_short=c.pad_short,
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conv_pad=c.conv_pad,
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is_training=not is_val,
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return_segments=not is_val,
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use_noise_augment=c.use_noise_augment,
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use_cache=c.use_cache,
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verbose=verbose)
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dataset.shuffle_mapping()
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# sampler = DistributedSampler(dataset) if num_gpus > 1 else None
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loader = DataLoader(dataset,
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batch_size=1 if is_val else c.batch_size,
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shuffle=True,
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drop_last=False,
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sampler=None,
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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 isinstance(data[0], list):
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# setup input data
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c_G, x_G = data[0]
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c_D, x_D = data[1]
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# dispatch data to GPU
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if use_cuda:
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c_G = c_G.cuda(non_blocking=True)
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x_G = x_G.cuda(non_blocking=True)
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c_D = c_D.cuda(non_blocking=True)
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x_D = x_D.cuda(non_blocking=True)
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return c_G, x_G, c_D, x_D
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# return a whole audio segment
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co, x = data
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if use_cuda:
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co = co.cuda(non_blocking=True)
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x = x.cuda(non_blocking=True)
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return co, x, None, None
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def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
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scheduler_G, scheduler_D, ap, global_step, epoch):
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data_loader = setup_loader(ap, is_val=False, verbose=(epoch == 0))
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model_G.train()
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model_D.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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c_G, y_G, c_D, y_D = format_data(data)
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loader_time = time.time() - end_time
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global_step += 1
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##############################
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# GENERATOR
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##############################
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# generator pass
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y_hat = model_G(c_G)
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y_hat_sub = None
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y_G_sub = None
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y_hat_vis = y_hat # for visualization
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# PQMF formatting
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if y_hat.shape[1] > 1:
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y_hat_sub = y_hat
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y_hat = model_G.pqmf_synthesis(y_hat)
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y_hat_vis = y_hat
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y_G_sub = model_G.pqmf_analysis(y_G)
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scores_fake, feats_fake, feats_real = None, None, None
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if global_step > c.steps_to_start_discriminator:
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# run D with or without cond. features
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if len(signature(model_D.forward).parameters) == 2:
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D_out_fake = model_D(y_hat, c_G)
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else:
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D_out_fake = model_D(y_hat)
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D_out_real = None
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if c.use_feat_match_loss:
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with torch.no_grad():
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D_out_real = model_D(y_G)
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# format D outputs
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if isinstance(D_out_fake, tuple):
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scores_fake, feats_fake = D_out_fake
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if D_out_real is None:
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feats_real = None
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else:
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_, feats_real = D_out_real
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else:
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scores_fake = D_out_fake
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# compute losses
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loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
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feats_real, y_hat_sub, y_G_sub)
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loss_G = loss_G_dict['G_loss']
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# optimizer generator
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optimizer_G.zero_grad()
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loss_G.backward()
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if c.gen_clip_grad > 0:
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torch.nn.utils.clip_grad_norm_(model_G.parameters(),
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c.gen_clip_grad)
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optimizer_G.step()
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if scheduler_G is not None:
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scheduler_G.step()
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loss_dict = dict()
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for key, value in loss_G_dict.items():
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if isinstance(value, int):
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loss_dict[key] = value
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else:
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loss_dict[key] = value.item()
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##############################
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# DISCRIMINATOR
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##############################
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if global_step >= c.steps_to_start_discriminator:
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# discriminator pass
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with torch.no_grad():
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y_hat = model_G(c_D)
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# PQMF formatting
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if y_hat.shape[1] > 1:
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y_hat = model_G.pqmf_synthesis(y_hat)
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# run D with or without cond. features
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if len(signature(model_D.forward).parameters) == 2:
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D_out_fake = model_D(y_hat.detach(), c_D)
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D_out_real = model_D(y_D, c_D)
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else:
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D_out_fake = model_D(y_hat.detach())
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D_out_real = model_D(y_D)
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# format D outputs
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if isinstance(D_out_fake, tuple):
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scores_fake, feats_fake = D_out_fake
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if D_out_real is None:
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scores_real, feats_real = None, None
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else:
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scores_real, feats_real = D_out_real
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else:
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scores_fake = D_out_fake
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scores_real = D_out_real
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# compute losses
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loss_D_dict = criterion_D(scores_fake, scores_real)
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loss_D = loss_D_dict['D_loss']
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# optimizer discriminator
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optimizer_D.zero_grad()
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loss_D.backward()
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if c.disc_clip_grad > 0:
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torch.nn.utils.clip_grad_norm_(model_D.parameters(),
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c.disc_clip_grad)
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optimizer_D.step()
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if scheduler_D is not None:
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scheduler_D.step()
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for key, value in loss_D_dict.items():
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if isinstance(value, (int, float)):
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loss_dict[key] = value
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else:
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loss_dict[key] = value.item()
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step_time = time.time() - start_time
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epoch_time += step_time
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# get current learning rates
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current_lr_G = list(optimizer_G.param_groups)[0]['lr']
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current_lr_D = list(optimizer_D.param_groups)[0]['lr']
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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 stats
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if global_step % c.print_step == 0:
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log_dict = {
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'step_time': [step_time, 2],
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'loader_time': [loader_time, 4],
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"current_lr_G": current_lr_G,
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"current_lr_D": current_lr_D
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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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# plot step stats
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if global_step % 10 == 0:
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iter_stats = {
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"lr_G": current_lr_G,
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"lr_D": current_lr_D,
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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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# save checkpoint
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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_G,
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optimizer_G,
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scheduler_G,
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model_D,
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optimizer_D,
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scheduler_D,
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global_step,
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epoch,
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OUT_PATH,
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model_losses=loss_dict)
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# compute spectrograms
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figures = plot_results(y_hat_vis, y_G, ap, global_step,
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'train')
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tb_logger.tb_train_figures(global_step, figures)
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# Sample audio
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sample_voice = y_hat_vis[0].squeeze(0).detach().cpu().numpy()
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tb_logger.tb_train_audios(global_step,
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{'train/audio': sample_voice},
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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 Training Epoch Stats
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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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# TODO: plot model 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_G, criterion_G, model_D, criterion_D, ap, global_step, epoch):
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data_loader = setup_loader(ap, is_val=True, verbose=(epoch == 0))
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model_G.eval()
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model_D.eval()
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epoch_time = 0
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keep_avg = KeepAverage()
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end_time = time.time()
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c_logger.print_eval_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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c_G, y_G, _, _ = format_data(data)
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loader_time = time.time() - end_time
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global_step += 1
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##############################
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# GENERATOR
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##############################
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# generator pass
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y_hat = model_G(c_G)
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y_hat_sub = None
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y_G_sub = None
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# PQMF formatting
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if y_hat.shape[1] > 1:
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y_hat_sub = y_hat
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y_hat = model_G.pqmf_synthesis(y_hat)
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y_G_sub = model_G.pqmf_analysis(y_G)
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scores_fake, feats_fake, feats_real = None, None, None
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if global_step > c.steps_to_start_discriminator:
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if len(signature(model_D.forward).parameters) == 2:
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D_out_fake = model_D(y_hat, c_G)
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else:
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D_out_fake = model_D(y_hat)
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D_out_real = None
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if c.use_feat_match_loss:
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with torch.no_grad():
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D_out_real = model_D(y_G)
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# format D outputs
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if isinstance(D_out_fake, tuple):
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scores_fake, feats_fake = D_out_fake
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if D_out_real is None:
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feats_real = None
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else:
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_, feats_real = D_out_real
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else:
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scores_fake = D_out_fake
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feats_fake, feats_real = None, None
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# compute losses
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loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
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feats_real, y_hat_sub, y_G_sub)
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loss_dict = dict()
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for key, value in loss_G_dict.items():
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if isinstance(value, (int, float)):
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loss_dict[key] = value
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else:
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loss_dict[key] = value.item()
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##############################
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# DISCRIMINATOR
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##############################
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if global_step >= c.steps_to_start_discriminator:
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# discriminator pass
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with torch.no_grad():
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y_hat = model_G(c_G)
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# PQMF formatting
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if y_hat.shape[1] > 1:
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y_hat = model_G.pqmf_synthesis(y_hat)
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# run D with or without cond. features
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if len(signature(model_D.forward).parameters) == 2:
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D_out_fake = model_D(y_hat.detach(), c_G)
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D_out_real = model_D(y_G, c_G)
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else:
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D_out_fake = model_D(y_hat.detach())
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D_out_real = model_D(y_G)
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# format D outputs
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if isinstance(D_out_fake, tuple):
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scores_fake, feats_fake = D_out_fake
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if D_out_real is None:
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scores_real, feats_real = None, None
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else:
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scores_real, feats_real = D_out_real
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else:
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scores_fake = D_out_fake
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scores_real = D_out_real
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# compute losses
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loss_D_dict = criterion_D(scores_fake, scores_real)
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for key, value in loss_D_dict.items():
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if isinstance(value, (int, float)):
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loss_dict[key] = value
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else:
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loss_dict[key] = value.item()
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step_time = time.time() - start_time
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epoch_time += step_time
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# update avg stats
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update_eval_values = dict()
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for key, value in loss_dict.items():
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update_eval_values['avg_' + key] = value
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update_eval_values['avg_loader_time'] = loader_time
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update_eval_values['avg_step_time'] = step_time
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keep_avg.update_values(update_eval_values)
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# print eval stats
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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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# compute spectrograms
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figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
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tb_logger.tb_eval_figures(global_step, figures)
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# Sample audio
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sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
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tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
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c.audio["sample_rate"])
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# synthesize a full voice
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data_loader.return_segments = False
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tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
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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 train_data, eval_data
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print(f" > Loading wavs from: {c.data_path}")
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if c.feature_path is not None:
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print(f" > Loading features from: {c.feature_path}")
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eval_data, train_data = load_wav_feat_data(c.data_path, c.feature_path, c.eval_split_size)
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else:
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eval_data, train_data = load_wav_data(c.data_path, c.eval_split_size)
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# setup audio processor
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ap = AudioProcessor(**c.audio)
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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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# setup models
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model_gen = setup_generator(c)
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model_disc = setup_discriminator(c)
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# setup optimizers
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optimizer_gen = RAdam(model_gen.parameters(), lr=c.lr_gen, weight_decay=0)
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optimizer_disc = RAdam(model_disc.parameters(),
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lr=c.lr_disc,
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weight_decay=0)
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# schedulers
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scheduler_gen = None
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scheduler_disc = None
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if 'lr_scheduler_gen' in c:
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scheduler_gen = getattr(torch.optim.lr_scheduler, c.lr_scheduler_gen)
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scheduler_gen = scheduler_gen(optimizer_gen, **c.lr_scheduler_gen_params)
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if 'lr_scheduler_disc' in c:
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scheduler_disc = getattr(torch.optim.lr_scheduler, c.lr_scheduler_disc)
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scheduler_disc = scheduler_disc(optimizer_disc, **c.lr_scheduler_disc_params)
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# setup criterion
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criterion_gen = GeneratorLoss(c)
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criterion_disc = DiscriminatorLoss(c)
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if args.restore_path:
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checkpoint = torch.load(args.restore_path, map_location='cpu')
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try:
|
|
print(" > Restoring Generator Model...")
|
|
model_gen.load_state_dict(checkpoint['model'])
|
|
print(" > Restoring Generator Optimizer...")
|
|
optimizer_gen.load_state_dict(checkpoint['optimizer'])
|
|
print(" > Restoring Discriminator Model...")
|
|
model_disc.load_state_dict(checkpoint['model_disc'])
|
|
print(" > Restoring Discriminator Optimizer...")
|
|
optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
|
|
if 'scheduler' in checkpoint:
|
|
print(" > Restoring Generator LR Scheduler...")
|
|
scheduler_gen.load_state_dict(checkpoint['scheduler'])
|
|
# NOTE: Not sure if necessary
|
|
scheduler_gen.optimizer = optimizer_gen
|
|
if 'scheduler_disc' in checkpoint:
|
|
print(" > Restoring Discriminator LR Scheduler...")
|
|
scheduler_disc.load_state_dict(checkpoint['scheduler_disc'])
|
|
scheduler_disc.optimizer = optimizer_disc
|
|
except RuntimeError:
|
|
# retore only matching layers.
|
|
print(" > Partial model initialization...")
|
|
model_dict = model_gen.state_dict()
|
|
model_dict = set_init_dict(model_dict, checkpoint['model'], c)
|
|
model_gen.load_state_dict(model_dict)
|
|
|
|
model_dict = model_disc.state_dict()
|
|
model_dict = set_init_dict(model_dict, checkpoint['model_disc'], c)
|
|
model_disc.load_state_dict(model_dict)
|
|
del model_dict
|
|
|
|
# reset lr if not countinuining training.
|
|
for group in optimizer_gen.param_groups:
|
|
group['lr'] = c.lr_gen
|
|
|
|
for group in optimizer_disc.param_groups:
|
|
group['lr'] = c.lr_disc
|
|
|
|
print(" > Model restored from step %d" % checkpoint['step'],
|
|
flush=True)
|
|
args.restore_step = checkpoint['step']
|
|
else:
|
|
args.restore_step = 0
|
|
|
|
if use_cuda:
|
|
model_gen.cuda()
|
|
criterion_gen.cuda()
|
|
model_disc.cuda()
|
|
criterion_disc.cuda()
|
|
|
|
# DISTRUBUTED
|
|
# if num_gpus > 1:
|
|
# model = apply_gradient_allreduce(model)
|
|
|
|
num_params = count_parameters(model_gen)
|
|
print(" > Generator has {} parameters".format(num_params), flush=True)
|
|
num_params = count_parameters(model_disc)
|
|
print(" > Discriminator 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):
|
|
c_logger.print_epoch_start(epoch, c.epochs)
|
|
_, global_step = train(model_gen, criterion_gen, optimizer_gen,
|
|
model_disc, criterion_disc, optimizer_disc,
|
|
scheduler_gen, scheduler_disc, ap, global_step,
|
|
epoch)
|
|
eval_avg_loss_dict = evaluate(model_gen, criterion_gen, model_disc, criterion_disc, ap,
|
|
global_step, epoch)
|
|
c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
|
|
target_loss = eval_avg_loss_dict[c.target_loss]
|
|
best_loss = save_best_model(target_loss,
|
|
best_loss,
|
|
model_gen,
|
|
optimizer_gen,
|
|
scheduler_gen,
|
|
model_disc,
|
|
optimizer_disc,
|
|
scheduler_disc,
|
|
global_step,
|
|
epoch,
|
|
OUT_PATH,
|
|
model_losses=eval_avg_loss_dict)
|
|
|
|
|
|
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__))
|
|
|
|
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='VOCODER')
|
|
|
|
# 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)
|