63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
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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 yacs.config import CfgNode
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_C = CfgNode()
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data_config = _C.data = CfgNode()
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## Audio volume normalization
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data_config.audio_norm_target_dBFS = -30
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## Audio sample rate
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data_config.sampling_rate = 16000 # Hz
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## Voice Activation Detection
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# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
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# This sets the granularity of the VAD. Should not need to be changed.
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data_config.vad_window_length = 30 # In milliseconds
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# Number of frames to average together when performing the moving average smoothing.
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# The larger this value, the larger the VAD variations must be to not get smoothed out.
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data_config.vad_moving_average_width = 8
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# Maximum number of consecutive silent frames a segment can have.
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data_config.vad_max_silence_length = 6
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## Mel-filterbank
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data_config.mel_window_length = 25 # In milliseconds
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data_config.mel_window_step = 10 # In milliseconds
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data_config.n_mels = 40 # mel bands
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# Number of spectrogram frames in a partial utterance
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data_config.partial_n_frames = 160 # 1600 ms
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data_config.min_pad_coverage = 0.75 # at least 75% of the audio is valid in a partial
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data_config.partial_overlap_ratio = 0.5 # overlap ratio between ajancent partials
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model_config = _C.model = CfgNode()
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model_config.num_layers = 3
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model_config.hidden_size = 256
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model_config.embedding_size = 256 # output size
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training_config = _C.training = CfgNode()
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training_config.learning_rate_init = 1e-4
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training_config.speakers_per_batch = 64
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training_config.utterances_per_speaker = 10
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training_config.max_iteration = 1560000
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training_config.save_interval = 10000
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training_config.valid_interval = 10000
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def get_cfg_defaults():
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return _C.clone()
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