huangyuxin
4 years ago
commit
84020a0471
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Contains U2 model."""
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import paddle
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from paddle import distributed as dist
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from paddle.io import DataLoader
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from deepspeech.io.collator import SpeechCollator
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from deepspeech.io.dataset import ManifestDataset
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from deepspeech.io.sampler import SortagradBatchSampler
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from deepspeech.io.sampler import SortagradDistributedBatchSampler
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from deepspeech.models.u2 import U2Evaluator
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from deepspeech.models.u2 import U2Model
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from deepspeech.models.u2 import U2Updater
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from deepspeech.training.extensions.snapshot import Snapshot
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from deepspeech.training.extensions.visualizer import VisualDL
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from deepspeech.training.optimizer import OptimizerFactory
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from deepspeech.training.scheduler import LRSchedulerFactory
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from deepspeech.training.timer import Timer
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from deepspeech.training.trainer import Trainer
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from deepspeech.training.updaters.trainer import Trainer as NewTrainer
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from deepspeech.utils import layer_tools
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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class U2Trainer(Trainer):
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def __init__(self, config, args):
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super().__init__(config, args)
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def setup_dataloader(self):
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config = self.config.clone()
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config.defrost()
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config.collator.keep_transcription_text = False
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# train/valid dataset, return token ids
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config.data.manifest = config.data.train_manifest
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train_dataset = ManifestDataset.from_config(config)
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config.data.manifest = config.data.dev_manifest
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dev_dataset = ManifestDataset.from_config(config)
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collate_fn_train = SpeechCollator.from_config(config)
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config.collator.augmentation_config = ""
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collate_fn_dev = SpeechCollator.from_config(config)
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if self.parallel:
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batch_sampler = SortagradDistributedBatchSampler(
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train_dataset,
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batch_size=config.collator.batch_size,
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num_replicas=None,
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rank=None,
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shuffle=True,
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drop_last=True,
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sortagrad=config.collator.sortagrad,
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shuffle_method=config.collator.shuffle_method)
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else:
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batch_sampler = SortagradBatchSampler(
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train_dataset,
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shuffle=True,
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batch_size=config.collator.batch_size,
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drop_last=True,
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sortagrad=config.collator.sortagrad,
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shuffle_method=config.collator.shuffle_method)
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self.train_loader = DataLoader(
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train_dataset,
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batch_sampler=batch_sampler,
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collate_fn=collate_fn_train,
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num_workers=config.collator.num_workers, )
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self.valid_loader = DataLoader(
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dev_dataset,
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batch_size=config.collator.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=collate_fn_dev)
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# test dataset, return raw text
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config.data.manifest = config.data.test_manifest
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# filter test examples, will cause less examples, but no mismatch with training
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# and can use large batch size , save training time, so filter test egs now.
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config.data.min_input_len = 0.0 # second
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config.data.max_input_len = float('inf') # second
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config.data.min_output_len = 0.0 # tokens
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config.data.max_output_len = float('inf') # tokens
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config.data.min_output_input_ratio = 0.00
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config.data.max_output_input_ratio = float('inf')
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test_dataset = ManifestDataset.from_config(config)
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# return text ord id
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config.collator.keep_transcription_text = True
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config.collator.augmentation_config = ""
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self.test_loader = DataLoader(
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test_dataset,
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batch_size=config.decoding.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=SpeechCollator.from_config(config))
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# return text token id
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config.collator.keep_transcription_text = False
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self.align_loader = DataLoader(
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test_dataset,
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batch_size=config.decoding.batch_size,
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shuffle=False,
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drop_last=False,
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collate_fn=SpeechCollator.from_config(config))
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logger.info("Setup train/valid/test/align Dataloader!")
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def setup_model(self):
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config = self.config
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model_conf = config.model
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model_conf.defrost()
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model_conf.input_dim = self.train_loader.collate_fn.feature_size
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model_conf.output_dim = self.train_loader.collate_fn.vocab_size
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model_conf.freeze()
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model = U2Model.from_config(model_conf)
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if self.parallel:
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model = paddle.DataParallel(model)
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model.train()
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logger.info(f"{model}")
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layer_tools.print_params(model, logger.info)
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train_config = config.training
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optim_type = train_config.optim
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optim_conf = train_config.optim_conf
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scheduler_type = train_config.scheduler
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scheduler_conf = train_config.scheduler_conf
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scheduler_args = {
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"learning_rate": optim_conf.lr,
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"verbose": False,
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"warmup_steps": scheduler_conf.warmup_steps,
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"gamma": scheduler_conf.lr_decay,
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"d_model": model_conf.encoder_conf.output_size,
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}
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lr_scheduler = LRSchedulerFactory.from_args(scheduler_type,
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scheduler_args)
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def optimizer_args(
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config,
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parameters,
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lr_scheduler=None, ):
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train_config = config.training
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optim_type = train_config.optim
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optim_conf = train_config.optim_conf
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scheduler_type = train_config.scheduler
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scheduler_conf = train_config.scheduler_conf
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return {
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"grad_clip": train_config.global_grad_clip,
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"weight_decay": optim_conf.weight_decay,
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"learning_rate": lr_scheduler
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if lr_scheduler else optim_conf.lr,
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"parameters": parameters,
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"epsilon": 1e-9 if optim_type == 'noam' else None,
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"beta1": 0.9 if optim_type == 'noam' else None,
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"beat2": 0.98 if optim_type == 'noam' else None,
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}
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optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
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optimizer = OptimizerFactory.from_args(optim_type, optimzer_args)
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self.model = model
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self.optimizer = optimizer
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self.lr_scheduler = lr_scheduler
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logger.info("Setup model/optimizer/lr_scheduler!")
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def setup_updater(self):
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output_dir = self.output_dir
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config = self.config.training
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updater = U2Updater(
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model=self.model,
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optimizer=self.optimizer,
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scheduler=self.lr_scheduler,
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dataloader=self.train_loader,
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output_dir=output_dir,
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accum_grad=config.accum_grad)
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trainer = NewTrainer(updater, (config.n_epoch, 'epoch'), output_dir)
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evaluator = U2Evaluator(self.model, self.valid_loader)
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trainer.extend(evaluator, trigger=(1, "epoch"))
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if dist.get_rank() == 0:
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trainer.extend(VisualDL(output_dir), trigger=(1, "iteration"))
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num_snapshots = config.checkpoint.kbest_n
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trainer.extend(
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Snapshot(
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mode='kbest',
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max_size=num_snapshots,
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indicator='VALID/LOSS',
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less_better=True),
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trigger=(1, 'epoch'))
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# print(trainer.extensions)
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# trainer.run()
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self.trainer = trainer
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def run(self):
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"""The routine of the experiment after setup. This method is intended
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to be used by the user.
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"""
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self.setup_updater()
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with Timer("Training Done: {}"):
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self.trainer.run()
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@ -0,0 +1,19 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .u2 import U2InferModel
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from .u2 import U2Model
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from .updater import U2Evaluator
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from .updater import U2Updater
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__all__ = ["U2Model", "U2InferModel", "U2Evaluator", "U2Updater"]
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@ -0,0 +1,149 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from contextlib import nullcontext
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import paddle
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from paddle import distributed as dist
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from deepspeech.training.extensions.evaluator import StandardEvaluator
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from deepspeech.training.reporter import report
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from deepspeech.training.timer import Timer
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from deepspeech.training.updaters.standard_updater import StandardUpdater
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from deepspeech.utils import layer_tools
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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class U2Evaluator(StandardEvaluator):
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def __init__(self, model, dataloader):
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super().__init__(model, dataloader)
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self.msg = ""
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self.num_seen_utts = 0
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self.total_loss = 0.0
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def evaluate_core(self, batch):
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self.msg = "Valid: Rank: {}, ".format(dist.get_rank())
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losses_dict = {}
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loss, attention_loss, ctc_loss = self.model(*batch[1:])
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if paddle.isfinite(loss):
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num_utts = batch[1].shape[0]
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self.num_seen_utts += num_utts
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self.total_loss += float(loss) * num_utts
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losses_dict['loss'] = float(loss)
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if attention_loss:
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losses_dict['att_loss'] = float(attention_loss)
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if ctc_loss:
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losses_dict['ctc_loss'] = float(ctc_loss)
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for k, v in losses_dict.items():
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report("eval/" + k, v)
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self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in losses_dict.items())
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logger.info(self.msg)
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return self.total_loss, self.num_seen_utts
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class U2Updater(StandardUpdater):
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def __init__(self,
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model,
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optimizer,
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scheduler,
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dataloader,
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init_state=None,
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accum_grad=1,
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**kwargs):
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super().__init__(
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model, optimizer, scheduler, dataloader, init_state=init_state)
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self.accum_grad = accum_grad
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self.forward_count = 0
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self.msg = ""
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def update_core(self, batch):
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"""One Step
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Args:
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batch (List[Object]): utts, xs, xlens, ys, ylens
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"""
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losses_dict = {}
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self.msg = "Rank: {}, ".format(dist.get_rank())
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# forward
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batch_size = batch[1].shape[0]
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loss, attention_loss, ctc_loss = self.model(*batch[1:])
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# loss div by `batch_size * accum_grad`
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loss /= self.accum_grad
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# loss backward
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if (self.forward_count + 1) != self.accum_grad:
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# Disable gradient synchronizations across DDP processes.
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# Within this context, gradients will be accumulated on module
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# variables, which will later be synchronized.
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context = self.model.no_sync
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else:
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# Used for single gpu training and DDP gradient synchronization
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# processes.
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context = nullcontext
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with context():
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loss.backward()
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layer_tools.print_grads(self.model, print_func=None)
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# loss info
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losses_dict['loss'] = float(loss) * self.accum_grad
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if attention_loss:
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losses_dict['att_loss'] = float(attention_loss)
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if ctc_loss:
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losses_dict['ctc_loss'] = float(ctc_loss)
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# report loss
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for k, v in losses_dict.items():
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report("train/" + k, v)
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# loss msg
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self.msg += "batch size: {}, ".format(batch_size)
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self.msg += "accum: {}, ".format(self.accum_grad)
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self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in losses_dict.items())
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# Truncate the graph
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loss.detach()
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# update parameters
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self.forward_count += 1
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if self.forward_count != self.accum_grad:
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return
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self.forward_count = 0
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self.optimizer.step()
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self.optimizer.clear_grad()
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self.scheduler.step()
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def update(self):
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# model is default in train mode
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# training for a step is implemented here
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with Timer("data time cost:{}"):
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batch = self.read_batch()
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with Timer("step time cost:{}"):
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self.update_core(batch)
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# #iterations with accum_grad > 1
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# Ref.: https://github.com/espnet/espnet/issues/777
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if self.forward_count == 0:
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self.state.iteration += 1
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if self.updates_per_epoch is not None:
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if self.state.iteration % self.updates_per_epoch == 0:
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self.state.epoch += 1
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@ -0,0 +1,50 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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||||
# 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 datetime
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import time
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from deepspeech.utils.log import Log
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__all__ = ["Timer"]
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logger = Log(__name__).getlog()
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class Timer():
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"""To be used like this:
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with Timer("Message") as value:
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do some thing
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"""
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def __init__(self, message=None):
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self.message = message
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def duration(self) -> str:
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elapsed_time = time.time() - self.start
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time_str = str(datetime.timedelta(seconds=elapsed_time))
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return time_str
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def __enter__(self):
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self.start = time.time()
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return self
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def __exit__(self, type, value, traceback):
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if self.message:
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logger.info(self.message.format(self.duration()))
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def __call__(self) -> float:
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return time.time() - self.start
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def __str__(self):
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return self.duration()
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|
Before Width: | Height: | Size: 206 KiB |
|
Before Width: | Height: | Size: 108 KiB |
@ -1,16 +0,0 @@
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# Benchmarks
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## Acceleration with Multi-GPUs
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We compare the training time with 1, 2, 4, 8 Tesla V100 GPUs (with a subset of LibriSpeech samples whose audio durations are between 6.0 and 7.0 seconds). And it shows that a **near-linear** acceleration with multiple GPUs has been achieved. In the following figure, the time (in seconds) cost for training is printed on the blue bars.
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<img src="../images/multi_gpu_speedup.png" width=450>
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| # of GPU | Acceleration Rate |
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| -------- | --------------: |
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| 1 | 1.00 X |
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| 2 | 1.98 X |
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| 4 | 3.73 X |
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| 8 | 6.95 X |
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`utils/profile.sh` provides such a demo profiling tool, you can change it as need.
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|
Before Width: | Height: | Size: 93 KiB After Width: | Height: | Size: 93 KiB |
|
Before Width: | Height: | Size: 93 KiB After Width: | Height: | Size: 93 KiB |
@ -1,20 +0,0 @@
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#!/bin/bash
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source path.sh
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# run on MacOS
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# brew install portaudio
|
||||
# pip install pyaudio
|
||||
# pip install keyboard
|
||||
|
||||
# start demo client
|
||||
python3 -u ${BIN_DIR}/deploy/client.py \
|
||||
--host_ip="localhost" \
|
||||
--host_port=8086 \
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in starting demo client!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
exit 0
|
||||
@ -1,40 +0,0 @@
|
||||
#!/bin/bash
|
||||
# TODO: replace the model with a mandarin model
|
||||
|
||||
if [[ $# != 1 ]];then
|
||||
echo "usage: $1 checkpoint_path"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
source path.sh
|
||||
|
||||
# download language model
|
||||
bash local/download_lm_ch.sh
|
||||
if [ $? -ne 0 ]; then
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# download well-trained model
|
||||
#bash local/download_model.sh
|
||||
#if [ $? -ne 0 ]; then
|
||||
# exit 1
|
||||
#fi
|
||||
|
||||
# start demo server
|
||||
CUDA_VISIBLE_DEVICES=0 \
|
||||
python3 -u ${BIN_DIR}/deploy/server.py \
|
||||
--device 'gpu' \
|
||||
--nproc 1 \
|
||||
--config conf/deepspeech2.yaml \
|
||||
--host_ip="localhost" \
|
||||
--host_port=8086 \
|
||||
--speech_save_dir="demo_cache" \
|
||||
--checkpoint_path ${1}
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in starting demo server!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
||||
exit 0
|
||||
@ -1,28 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# grid-search for hyper-parameters in language model
|
||||
python3 -u ${BIN_DIR}/tune.py \
|
||||
--device 'gpu' \
|
||||
--nproc 1 \
|
||||
--config conf/deepspeech2.yaml \
|
||||
--num_batches=10 \
|
||||
--batch_size=128 \
|
||||
--beam_size=300 \
|
||||
--num_proc_bsearch=8 \
|
||||
--num_alphas=10 \
|
||||
--num_betas=10 \
|
||||
--alpha_from=0.0 \
|
||||
--alpha_to=5.0 \
|
||||
--beta_from=-6 \
|
||||
--beta_to=6 \
|
||||
--cutoff_prob=1.0 \
|
||||
--cutoff_top_n=40 \
|
||||
--checkpoint_path ${1}
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in tuning!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,58 @@
|
||||
# [CC-CEDICT](https://cc-cedict.org/wiki/)
|
||||
|
||||
What is CC-CEDICT?
|
||||
CC-CEDICT is a continuation of the CEDICT project.
|
||||
The objective of the CEDICT project was to create an online, downloadable (as opposed to searchable-only) public-domain Chinese-English dictionary.
|
||||
CEDICT was started by Paul Andrew Denisowski in October 1997.
|
||||
For the most part, the project is modeled on Jim Breen's highly successful EDICT (Japanese-English dictionary) project and is intended to be a collaborative effort,
|
||||
with users providing entries and corrections to the main file.
|
||||
|
||||
|
||||
## Parse CC-CEDICT to Json format
|
||||
|
||||
1. Parse to Json
|
||||
|
||||
```
|
||||
run.sh
|
||||
```
|
||||
|
||||
2. Result
|
||||
|
||||
```
|
||||
exp/
|
||||
|-- cedict
|
||||
`-- cedict.json
|
||||
|
||||
0 directories, 2 files
|
||||
```
|
||||
|
||||
```
|
||||
4c4bffc84e24467fe1b2ea9ba37ed6b6 exp/cedict
|
||||
3adf504dacd13886f88cc9fe3b37c75d exp/cedict.json
|
||||
```
|
||||
|
||||
```
|
||||
==> exp/cedict <==
|
||||
# CC-CEDICT
|
||||
# Community maintained free Chinese-English dictionary.
|
||||
#
|
||||
# Published by MDBG
|
||||
#
|
||||
# License:
|
||||
# Creative Commons Attribution-ShareAlike 4.0 International License
|
||||
# https://creativecommons.org/licenses/by-sa/4.0/
|
||||
#
|
||||
# Referenced works:
|
||||
|
||||
==> exp/cedict.json <==
|
||||
{"traditional": "2019\u51a0\u72c0\u75c5\u6bd2\u75c5", "simplified": "2019\u51a0\u72b6\u75c5\u6bd2\u75c5", "pinyin": "er4 ling2 yi1 jiu3 guan1 zhuang4 bing4 du2 bing4", "english": "COVID-19, the coronavirus disease identified in 2019"}
|
||||
{"traditional": "21\u4e09\u9ad4\u7d9c\u5408\u75c7", "simplified": "21\u4e09\u4f53\u7efc\u5408\u75c7", "pinyin": "er4 shi2 yi1 san1 ti3 zong1 he2 zheng4", "english": "trisomy"}
|
||||
{"traditional": "3C", "simplified": "3C", "pinyin": "san1 C", "english": "abbr. for computers, communications, and consumer electronics"}
|
||||
{"traditional": "3P", "simplified": "3P", "pinyin": "san1 P", "english": "(slang) threesome"}
|
||||
{"traditional": "3Q", "simplified": "3Q", "pinyin": "san1 Q", "english": "(Internet slang) thank you (loanword)"}
|
||||
{"traditional": "421", "simplified": "421", "pinyin": "si4 er4 yi1", "english": "four grandparents, two parents and an only child"}
|
||||
{"traditional": "502\u81a0", "simplified": "502\u80f6", "pinyin": "wu3 ling2 er4 jiao1", "english": "cyanoacrylate glue"}
|
||||
{"traditional": "88", "simplified": "88", "pinyin": "ba1 ba1", "english": "(Internet slang) bye-bye (alternative for \u62dc\u62dc[bai2 bai2])"}
|
||||
{"traditional": "996", "simplified": "996", "pinyin": "jiu3 jiu3 liu4", "english": "9am-9pm, six days a week (work schedule)"}
|
||||
{"traditional": "A", "simplified": "A", "pinyin": "A", "english": "(slang) (Tw) to steal"}
|
||||
```
|
||||
@ -1,5 +0,0 @@
|
||||
# Download Baker dataset
|
||||
|
||||
Baker dataset has to be downloaded mannually and moved to 'data/', because you will have to pass the CATTCHA from a browswe to download the dataset.
|
||||
|
||||
Download URL https://test.data-baker.com/#/data/index/source.
|
||||
@ -0,0 +1,3 @@
|
||||
# G2P
|
||||
|
||||
* zh - Chinese G2P
|
||||
@ -1,4 +1,4 @@
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../`
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
@ -1,10 +1,17 @@
|
||||
# LibriSpeech
|
||||
|
||||
## Data
|
||||
| Data Subset | Duration in Seconds |
|
||||
| --- | --- |
|
||||
| data/manifest.train | 0.83s ~ 29.735s |
|
||||
| data/manifest.dev | 1.065 ~ 35.155s |
|
||||
| data/manifest.test-clean | 1.285s ~ 34.955s |
|
||||
|
||||
## Deepspeech2
|
||||
|
||||
| Model | Params | release | Config | Test set | Loss | WER |
|
||||
| --- | --- | --- | --- | --- | --- | --- |
|
||||
| DeepSpeech2 | 42.96M | 2.2.0 | conf/deepspeech2.yaml + spec_aug | 14.49190807 | test-clean | 0.067283 |
|
||||
| DeepSpeech2 | 42.96M | 2.1.0 | conf/deepspeech2.yaml | 15.184467315673828 | test-clean | 0.072154 |
|
||||
| DeepSpeech2 | 42.96M | 2.0.0 | conf/deepspeech2.yaml | - | test-clean | 0.073973 |
|
||||
| DeepSpeech2 | 42.96M | 2.2.0 | conf/deepspeech2.yaml + spec_aug | test-clean | 14.49190807 | 0.067283 |
|
||||
| DeepSpeech2 | 42.96M | 2.1.0 | conf/deepspeech2.yaml | test-clean | 15.184467315673828 | 0.072154 |
|
||||
| DeepSpeech2 | 42.96M | 2.0.0 | conf/deepspeech2.yaml | test-clean | - | 0.073973 |
|
||||
| DeepSpeech2 | 42.96M | 1.8.5 | - | test-clean | - | 0.074939 |
|
||||
|
||||
@ -1,33 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
if [ $# != 1 ];then
|
||||
echo "usage: tune ckpt_path"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# grid-search for hyper-parameters in language model
|
||||
python3 -u ${BIN_DIR}/tune.py \
|
||||
--device 'gpu' \
|
||||
--nproc 1 \
|
||||
--config conf/deepspeech2.yaml \
|
||||
--num_batches=-1 \
|
||||
--batch_size=128 \
|
||||
--beam_size=500 \
|
||||
--num_proc_bsearch=12 \
|
||||
--num_alphas=45 \
|
||||
--num_betas=8 \
|
||||
--alpha_from=1.0 \
|
||||
--alpha_to=3.2 \
|
||||
--beta_from=0.1 \
|
||||
--beta_to=0.45 \
|
||||
--cutoff_prob=1.0 \
|
||||
--cutoff_top_n=40 \
|
||||
--checkpoint_path ${1}
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in tuning!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,3 @@
|
||||
# Ngram LM
|
||||
|
||||
* s0 - kenlm ngram lm
|
||||
@ -0,0 +1 @@
|
||||
data/lm
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue