# 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.optimizer import AdamW from yacs.config import CfgNode from paddlespeech.t2s.datasets.am_batch_fn import jets_multi_spk_batch_fn from paddlespeech.t2s.datasets.am_batch_fn import jets_single_spk_batch_fn from paddlespeech.t2s.datasets.data_table import DataTable from paddlespeech.t2s.datasets.sampler import ErnieSATSampler from paddlespeech.t2s.models.jets import JETS from paddlespeech.t2s.models.jets import JETSEvaluator from paddlespeech.t2s.models.jets import JETSUpdater from paddlespeech.t2s.modules.losses import DiscriminatorAdversarialLoss from paddlespeech.t2s.modules.losses import FeatureMatchLoss from paddlespeech.t2s.modules.losses import ForwardSumLoss from paddlespeech.t2s.modules.losses import GeneratorAdversarialLoss from paddlespeech.t2s.modules.losses import MelSpectrogramLoss from paddlespeech.t2s.modules.losses import VarianceLoss 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 from paddlespeech.t2s.utils import str2bool 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", "durations", "pitch", "energy" ] converters = { "wave": np.load, "feats": np.load, "pitch": np.load, "energy": np.load, } spk_num = None if args.speaker_dict is not None: print("multiple speaker jets!") collate_fn = jets_multi_spk_batch_fn with open(args.speaker_dict, 'rt', encoding='utf-8') as f: spk_id = [line.strip().split() for line in f.readlines()] spk_num = len(spk_id) fields += ["spk_id"] elif args.voice_cloning: print("Training voice cloning!") collate_fn = jets_multi_spk_batch_fn fields += ["spk_emb"] converters["spk_emb"] = np.load else: print("single speaker jets!") collate_fn = jets_single_spk_batch_fn print("spk_num:", spk_num) # 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 = ErnieSATSampler( train_dataset, batch_size=config.batch_size, shuffle=False, drop_last=True) dev_sampler = ErnieSATSampler( dev_dataset, batch_size=config.batch_size, shuffle=False, drop_last=False) print("samplers done!") train_dataloader = DataLoader( train_dataset, batch_sampler=train_sampler, collate_fn=collate_fn, num_workers=config.num_workers) dev_dataloader = DataLoader( dev_dataset, batch_sampler=dev_sampler, collate_fn=collate_fn, num_workers=config.num_workers) print("dataloaders done!") with open(args.phones_dict, 'rt', encoding='utf-8') 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_mels config["model"]["generator_params"]["spks"] = spk_num model = JETS(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_var = VarianceLoss() criterion_forwardsum = ForwardSumLoss() print("criterions done!") lr_schedule_g = scheduler_classes[config["generator_scheduler"]]( **config["generator_scheduler_params"]) optimizer_g = AdamW( 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 = AdamW( 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 = JETSUpdater( 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, "var": criterion_var, "forwardsum": criterion_forwardsum, }, schedulers={ "generator": lr_schedule_g, "discriminator": lr_schedule_d, }, dataloader=train_dataloader, lambda_adv=config.lambda_adv, lambda_mel=config.lambda_mel, lambda_feat_match=config.lambda_feat_match, lambda_var=config.lambda_var, lambda_align=config.lambda_align, generator_first=config.generator_first, use_alignment_module=config.use_alignment_module, output_dir=output_dir) evaluator = JETSEvaluator( model=model, criterions={ "mel": criterion_mel, "feat_match": criterion_feat_match, "gen_adv": criterion_gen_adv, "dis_adv": criterion_dis_adv, "var": criterion_var, "forwardsum": criterion_forwardsum, }, dataloader=dev_dataloader, lambda_adv=config.lambda_adv, lambda_mel=config.lambda_mel, lambda_feat_match=config.lambda_feat_match, lambda_var=config.lambda_var, lambda_align=config.lambda_align, generator_first=config.generator_first, use_alignment_module=config.use_alignment_module, output_dir=output_dir) trainer = Trainer( updater, stop_trigger=(config.train_max_steps, "iteration"), out=output_dir) if dist.get_rank() == 0: trainer.extend( evaluator, trigger=(config.eval_interval_steps, 'iteration')) trainer.extend(VisualDL(output_dir), trigger=(1, 'iteration')) trainer.extend( Snapshot(max_size=config.num_snapshots), trigger=(config.save_interval_steps, 'iteration')) print("Trainer Done!") trainer.run() def main(): # parse args and config and redirect to train_sp parser = argparse.ArgumentParser(description="Train a JETS model.") parser.add_argument("--config", type=str, help="JETS 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.") parser.add_argument( "--speaker-dict", type=str, default=None, help="speaker id map file for multiple speaker model.") parser.add_argument( "--voice-cloning", type=str2bool, default=False, help="whether training voice cloning model.") 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()