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# network architecture
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model: SanmKWS
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model_conf:
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ctc_weight: 1.0
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# encoder
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encoder: SANMEncoder
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 320 # the number of units of position-wise feed forward
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num_blocks: 6 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.1
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input_layer: pe
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pos_enc_class: SinusoidalPositionEncoder
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normalize_before: true
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kernel_size: 11
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sanm_shift: 0
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selfattention_layer_type: sanm
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# frontend related
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frontend: WavFrontend
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frontend_conf:
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fs: 16000
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window: hamming
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n_mels: 40
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frame_length: 25
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frame_shift: 10
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lfr_m: 7
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lfr_n: 6
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specaug: SpecAugLFR
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specaug_conf:
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apply_time_warp: false
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time_warp_window: 5
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time_warp_mode: bicubic
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apply_freq_mask: true
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freq_mask_width_range:
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- 0
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- 30
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lfr_rate: 6
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num_freq_mask: 1
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apply_time_mask: true
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time_mask_width_range:
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- 0
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- 12
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num_time_mask: 1
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train_conf:
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accum_grad: 1
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grad_clip: 5
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max_epoch: 100
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keep_nbest_models: 20
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avg_nbest_model: 10
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avg_keep_nbest_models_type: loss
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validate_interval: 50000
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save_checkpoint_interval: 50000
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avg_checkpoint_interval: 1000
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log_interval: 50
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optim: adam
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optim_conf:
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lr: 0.001
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 10000
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dataset: AudioDataset
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dataset_conf:
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index_ds: IndexDSJsonl
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batch_sampler: EspnetStyleBatchSampler
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batch_type: length # example or length
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batch_size: 96000 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
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max_token_length: 1600 # filter samples if source_token_len+target_token_len > max_token_length,
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buffer_size: 2048
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shuffle: true
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num_workers: 8
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preprocessor_speech: SpeechPreprocessSpeedPerturb
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preprocessor_speech_conf:
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speed_perturb: [0.9, 1.0, 1.1]
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tokenizer: CharTokenizer
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tokenizer_conf:
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unk_symbol: <unk>
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ctc_conf:
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dropout_rate: 0.0
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ctc_type: builtin # ctc_type: focalctc, builtin
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reduce: true
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ignore_nan_grad: true
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normalize: null
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