# 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()