# Copyright (c) 2021 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. """Contains U2 model.""" import paddle from paddle import distributed as dist from paddle.io import DataLoader from paddlespeech.s2t.io.collator import SpeechCollator from paddlespeech.s2t.io.dataset import ManifestDataset from paddlespeech.s2t.io.sampler import SortagradBatchSampler from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler from paddlespeech.s2t.models.u2 import U2Evaluator from paddlespeech.s2t.models.u2 import U2Model from paddlespeech.s2t.models.u2 import U2Updater from paddlespeech.s2t.training.extensions.snapshot import Snapshot from paddlespeech.s2t.training.extensions.visualizer import VisualDL from paddlespeech.s2t.training.optimizer import OptimizerFactory from paddlespeech.s2t.training.scheduler import LRSchedulerFactory from paddlespeech.s2t.training.timer import Timer from paddlespeech.s2t.training.trainer import Trainer from paddlespeech.s2t.training.updaters.trainer import Trainer as NewTrainer from paddlespeech.s2t.utils import layer_tools from paddlespeech.s2t.utils.log import Log from paddlespeech.s2t.utils.utility import UpdateConfig logger = Log(__name__).getlog() class U2Trainer(Trainer): def __init__(self, config, args): super().__init__(config, args) def setup_dataloader(self): config = self.config.clone() config.defrost() config.keep_transcription_text = False # train/valid dataset, return token ids config.manifest = config.train_manifest train_dataset = ManifestDataset.from_config(config) config.manifest = config.dev_manifest dev_dataset = ManifestDataset.from_config(config) collate_fn_train = SpeechCollator.from_config(config) collate_fn_dev = SpeechCollator.from_config(config) if self.parallel: batch_sampler = SortagradDistributedBatchSampler( train_dataset, batch_size=config.batch_size, num_replicas=None, rank=None, shuffle=True, drop_last=True, sortagrad=config.sortagrad, shuffle_method=config.shuffle_method) else: batch_sampler = SortagradBatchSampler( train_dataset, shuffle=True, batch_size=config.batch_size, drop_last=True, sortagrad=config.sortagrad, shuffle_method=config.shuffle_method) self.train_loader = DataLoader( train_dataset, batch_sampler=batch_sampler, collate_fn=collate_fn_train, num_workers=config.num_workers, ) self.valid_loader = DataLoader( dev_dataset, batch_size=config.batch_size, shuffle=False, drop_last=False, collate_fn=collate_fn_dev, num_workers=config.num_workers, ) # test dataset, return raw text config.manifest = config.test_manifest # filter test examples, will cause less examples, but no mismatch with training # and can use large batch size , save training time, so filter test egs now. config.min_input_len = 0.0 # second config.max_input_len = float('inf') # second config.min_output_len = 0.0 # tokens config.max_output_len = float('inf') # tokens config.min_output_input_ratio = 0.00 config.max_output_input_ratio = float('inf') test_dataset = ManifestDataset.from_config(config) # return text ord id config.keep_transcription_text = True self.test_loader = DataLoader( test_dataset, batch_size=config.decode.batch_size, shuffle=False, drop_last=False, collate_fn=SpeechCollator.from_config(config)) # return text token id config.keep_transcription_text = False self.align_loader = DataLoader( test_dataset, batch_size=config.decode.batch_size, shuffle=False, drop_last=False, collate_fn=SpeechCollator.from_config(config)) logger.info("Setup train/valid/test/align Dataloader!") def setup_model(self): config = self.config model_conf = config with UpdateConfig(model_conf): model_conf.input_dim = self.train_loader.collate_fn.feature_size model_conf.output_dim = self.train_loader.collate_fn.vocab_size model = U2Model.from_config(model_conf) if self.parallel: model = paddle.DataParallel(model) model.train() logger.info(f"{model}") layer_tools.print_params(model, logger.info) train_config = config optim_type = train_config.optim optim_conf = train_config.optim_conf scheduler_type = train_config.scheduler scheduler_conf = train_config.scheduler_conf scheduler_args = { "learning_rate": optim_conf.lr, "verbose": False, "warmup_steps": scheduler_conf.warmup_steps, "gamma": scheduler_conf.lr_decay, "d_model": model_conf.encoder_conf.output_size, } lr_scheduler = LRSchedulerFactory.from_args(scheduler_type, scheduler_args) def optimizer_args( config, parameters, lr_scheduler=None, ): train_config = config optim_type = train_config.optim optim_conf = train_config.optim_conf scheduler_type = train_config.scheduler scheduler_conf = train_config.scheduler_conf return { "grad_clip": train_config.global_grad_clip, "weight_decay": optim_conf.weight_decay, "learning_rate": lr_scheduler if lr_scheduler else optim_conf.lr, "parameters": parameters, "epsilon": 1e-9 if optim_type == 'noam' else None, "beta1": 0.9 if optim_type == 'noam' else None, "beat2": 0.98 if optim_type == 'noam' else None, } optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler) optimizer = OptimizerFactory.from_args(optim_type, optimzer_args) self.model = model self.optimizer = optimizer self.lr_scheduler = lr_scheduler logger.info("Setup model/optimizer/lr_scheduler!") def setup_updater(self): output_dir = self.output_dir config = self.config updater = U2Updater( model=self.model, optimizer=self.optimizer, scheduler=self.lr_scheduler, dataloader=self.train_loader, output_dir=output_dir, accum_grad=config.accum_grad) trainer = NewTrainer(updater, (config.n_epoch, 'epoch'), output_dir) evaluator = U2Evaluator(self.model, self.valid_loader) trainer.extend(evaluator, trigger=(1, "epoch")) if dist.get_rank() == 0: trainer.extend(VisualDL(output_dir), trigger=(1, "iteration")) num_snapshots = config.checkpoint.kbest_n trainer.extend( Snapshot( mode='kbest', max_size=num_snapshots, indicator='VALID/LOSS', less_better=True), trigger=(1, 'epoch')) # print(trainer.extensions) # trainer.run() self.trainer = trainer def run(self): """The routine of the experiment after setup. This method is intended to be used by the user. """ self.setup_updater() with Timer("Training Done: {}"): self.trainer.run()