# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import logging import os import shutil from pathlib import Path import jsonlines import numpy as np import paddle import yaml from paddle import DataParallel from paddle import distributed as dist from paddle.io import DataLoader from paddle.io import DistributedBatchSampler from paddle.optimizer import Adam from yacs.config import CfgNode from paddlespeech.t2s.datasets.am_batch_fn import vits_single_spk_batch_fn from paddlespeech.t2s.datasets.data_table import DataTable from paddlespeech.t2s.models.vits import VITS from paddlespeech.t2s.models.vits import VITSEvaluator from paddlespeech.t2s.models.vits import VITSUpdater from paddlespeech.t2s.modules.losses import DiscriminatorAdversarialLoss from paddlespeech.t2s.modules.losses import FeatureMatchLoss from paddlespeech.t2s.modules.losses import GeneratorAdversarialLoss from paddlespeech.t2s.modules.losses import KLDivergenceLoss from paddlespeech.t2s.modules.losses import MelSpectrogramLoss from paddlespeech.t2s.training.extensions.snapshot import Snapshot from paddlespeech.t2s.training.extensions.visualizer import VisualDL from paddlespeech.t2s.training.optimizer import scheduler_classes from paddlespeech.t2s.training.seeding import seed_everything from paddlespeech.t2s.training.trainer import Trainer def train_sp(args, config): # decides device type and whether to run in parallel # setup running environment correctly world_size = paddle.distributed.get_world_size() if (not paddle.is_compiled_with_cuda()) or args.ngpu == 0: paddle.set_device("cpu") else: paddle.set_device("gpu") if world_size > 1: paddle.distributed.init_parallel_env() # set the random seed, it is a must for multiprocess training seed_everything(config.seed) print( f"rank: {dist.get_rank()}, pid: {os.getpid()}, parent_pid: {os.getppid()}", ) # dataloader has been too verbose logging.getLogger("DataLoader").disabled = True fields = ["text", "text_lengths", "feats", "feats_lengths", "wave"] converters = { "wave": np.load, "feats": np.load, } # construct dataset for training and validation with jsonlines.open(args.train_metadata, 'r') as reader: train_metadata = list(reader) train_dataset = DataTable( data=train_metadata, fields=fields, converters=converters, ) with jsonlines.open(args.dev_metadata, 'r') as reader: dev_metadata = list(reader) dev_dataset = DataTable( data=dev_metadata, fields=fields, converters=converters, ) # collate function and dataloader train_sampler = DistributedBatchSampler( train_dataset, batch_size=config.batch_size, shuffle=True, drop_last=True) dev_sampler = DistributedBatchSampler( dev_dataset, batch_size=config.batch_size, shuffle=False, drop_last=False) print("samplers done!") train_batch_fn = vits_single_spk_batch_fn train_dataloader = DataLoader( train_dataset, batch_sampler=train_sampler, collate_fn=train_batch_fn, num_workers=config.num_workers) dev_dataloader = DataLoader( dev_dataset, batch_sampler=dev_sampler, collate_fn=train_batch_fn, num_workers=config.num_workers) print("dataloaders done!") with open(args.phones_dict, "r") as f: phn_id = [line.strip().split() for line in f.readlines()] vocab_size = len(phn_id) print("vocab_size:", vocab_size) odim = config.n_fft // 2 + 1 model = VITS(idim=vocab_size, odim=odim, **config["model"]) gen_parameters = model.generator.parameters() dis_parameters = model.discriminator.parameters() if world_size > 1: model = DataParallel(model) gen_parameters = model._layers.generator.parameters() dis_parameters = model._layers.discriminator.parameters() print("model done!") # loss criterion_mel = MelSpectrogramLoss( **config["mel_loss_params"], ) criterion_feat_match = FeatureMatchLoss( **config["feat_match_loss_params"], ) criterion_gen_adv = GeneratorAdversarialLoss( **config["generator_adv_loss_params"], ) criterion_dis_adv = DiscriminatorAdversarialLoss( **config["discriminator_adv_loss_params"], ) criterion_kl = KLDivergenceLoss() print("criterions done!") lr_schedule_g = scheduler_classes[config["generator_scheduler"]]( **config["generator_scheduler_params"]) optimizer_g = Adam( learning_rate=lr_schedule_g, parameters=gen_parameters, **config["generator_optimizer_params"]) lr_schedule_d = scheduler_classes[config["discriminator_scheduler"]]( **config["discriminator_scheduler_params"]) optimizer_d = Adam( learning_rate=lr_schedule_d, parameters=dis_parameters, **config["discriminator_optimizer_params"]) print("optimizers done!") output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) if dist.get_rank() == 0: config_name = args.config.split("/")[-1] # copy conf to output_dir shutil.copyfile(args.config, output_dir / config_name) updater = VITSUpdater( model=model, optimizers={ "generator": optimizer_g, "discriminator": optimizer_d, }, criterions={ "mel": criterion_mel, "feat_match": criterion_feat_match, "gen_adv": criterion_gen_adv, "dis_adv": criterion_dis_adv, "kl": criterion_kl, }, schedulers={ "generator": lr_schedule_g, "discriminator": lr_schedule_d, }, dataloader=train_dataloader, lambda_adv=config.lambda_adv, lambda_mel=config.lambda_mel, lambda_kl=config.lambda_kl, lambda_feat_match=config.lambda_feat_match, lambda_dur=config.lambda_dur, generator_first=config.generator_first, output_dir=output_dir) evaluator = VITSEvaluator( model=model, criterions={ "mel": criterion_mel, "feat_match": criterion_feat_match, "gen_adv": criterion_gen_adv, "dis_adv": criterion_dis_adv, "kl": criterion_kl, }, dataloader=dev_dataloader, lambda_adv=config.lambda_adv, lambda_mel=config.lambda_mel, lambda_kl=config.lambda_kl, lambda_feat_match=config.lambda_feat_match, lambda_dur=config.lambda_dur, generator_first=config.generator_first, output_dir=output_dir) trainer = Trainer(updater, (config.max_epoch, 'epoch'), output_dir) if dist.get_rank() == 0: trainer.extend(evaluator, trigger=(1, "epoch")) trainer.extend(VisualDL(output_dir), trigger=(1, "iteration")) trainer.extend( Snapshot(max_size=config.num_snapshots), trigger=(1, 'epoch')) print("Trainer Done!") trainer.run() def main(): # parse args and config and redirect to train_sp parser = argparse.ArgumentParser(description="Train a VITS model.") parser.add_argument("--config", type=str, help="VITS config file") parser.add_argument("--train-metadata", type=str, help="training data.") parser.add_argument("--dev-metadata", type=str, help="dev data.") parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument( "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") parser.add_argument( "--phones-dict", type=str, default=None, help="phone vocabulary file.") args = parser.parse_args() with open(args.config, 'rt') as f: config = CfgNode(yaml.safe_load(f)) print("========Args========") print(yaml.safe_dump(vars(args))) print("========Config========") print(config) print( f"master see the word size: {dist.get_world_size()}, from pid: {os.getpid()}" ) # dispatch if args.ngpu > 1: dist.spawn(train_sp, (args, config), nprocs=args.ngpu) else: train_sp(args, config) if __name__ == "__main__": main()