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85 lines
3.2 KiB
85 lines
3.2 KiB
# 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 yacs.config import CfgNode as CN
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from deepspeech.models.deepspeech2 import DeepSpeech2Model
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_C = CN()
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_C.data = CN(
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dict(
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train_manifest="",
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dev_manifest="",
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test_manifest="",
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vocab_filepath="",
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mean_std_filepath="",
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augmentation_config="",
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max_duration=float('inf'),
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min_duration=0.0,
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stride_ms=10.0, # ms
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window_ms=20.0, # ms
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n_fft=None, # fft points
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max_freq=None, # None for samplerate/2
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specgram_type='linear', # 'linear', 'mfcc'
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target_sample_rate=16000, # sample rate
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use_dB_normalization=True,
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target_dB=-20,
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random_seed=0,
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keep_transcription_text=False,
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batch_size=32, # batch size
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num_workers=0, # data loader workers
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sortagrad=False, # sorted in first epoch when True
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shuffle_method="batch_shuffle", # 'batch_shuffle', 'instance_shuffle'
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))
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_C.model = CN(
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dict(
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num_conv_layers=2, #Number of stacking convolution layers.
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num_rnn_layers=3, #Number of stacking RNN layers.
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rnn_layer_size=1024, #RNN layer size (number of RNN cells).
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use_gru=True, #Use gru if set True. Use simple rnn if set False.
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share_rnn_weights=True #Whether to share input-hidden weights between forward and backward directional RNNs.Notice that for GRU, weight sharing is not supported.
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))
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DeepSpeech2Model.params(_C.model)
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_C.training = CN(
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dict(
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lr=5e-4, # learning rate
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lr_decay=1.0, # learning rate decay
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weight_decay=1e-6, # the coeff of weight decay
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global_grad_clip=5.0, # the global norm clip
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n_epoch=50, # train epochs
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))
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_C.decoding = CN(
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dict(
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alpha=2.5, # Coef of LM for beam search.
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beta=0.3, # Coef of WC for beam search.
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cutoff_prob=1.0, # Cutoff probability for pruning.
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cutoff_top_n=40, # Cutoff number for pruning.
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lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm', # Filepath for language model.
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decoding_method='ctc_beam_search', # Decoding method. Options: ctc_beam_search, ctc_greedy
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error_rate_type='wer', # Error rate type for evaluation. Options `wer`, 'cer'
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num_proc_bsearch=8, # # of CPUs for beam search.
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beam_size=500, # Beam search width.
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batch_size=128, # decoding batch size
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))
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def get_cfg_defaults():
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"""Get a yacs CfgNode object with default values for my_project."""
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# Return a clone so that the defaults will not be altered
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# This is for the "local variable" use pattern
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return _C.clone()
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