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| # This is an example that demonstrates how to configure a model file. | |
| # You can modify the configuration according to your own requirements. | |
| # to print the register_table: | |
| # from funasr.register import tables | |
| # tables.print() | |
| # network architecture | |
| #model: funasr.models.paraformer.model:Paraformer | |
| model: BiCifParaformer | |
| model_conf: | |
| ctc_weight: 0.0 | |
| lsm_weight: 0.1 | |
| length_normalized_loss: true | |
| predictor_weight: 1.0 | |
| predictor_bias: 1 | |
| sampling_ratio: 0.75 | |
| # encoder | |
| encoder: SANMEncoder | |
| encoder_conf: | |
| output_size: 512 | |
| attention_heads: 4 | |
| linear_units: 2048 | |
| num_blocks: 50 | |
| dropout_rate: 0.1 | |
| positional_dropout_rate: 0.1 | |
| attention_dropout_rate: 0.1 | |
| input_layer: pe | |
| pos_enc_class: SinusoidalPositionEncoder | |
| normalize_before: true | |
| kernel_size: 11 | |
| sanm_shfit: 0 | |
| selfattention_layer_type: sanm | |
| # decoder | |
| decoder: ParaformerSANMDecoder | |
| decoder_conf: | |
| attention_heads: 4 | |
| linear_units: 2048 | |
| num_blocks: 16 | |
| dropout_rate: 0.1 | |
| positional_dropout_rate: 0.1 | |
| self_attention_dropout_rate: 0.1 | |
| src_attention_dropout_rate: 0.1 | |
| att_layer_num: 16 | |
| kernel_size: 11 | |
| sanm_shfit: 0 | |
| predictor: CifPredictorV3 | |
| predictor_conf: | |
| idim: 512 | |
| threshold: 1.0 | |
| l_order: 1 | |
| r_order: 1 | |
| tail_threshold: 0.45 | |
| smooth_factor2: 0.25 | |
| noise_threshold2: 0.01 | |
| upsample_times: 3 | |
| use_cif1_cnn: false | |
| upsample_type: cnn_blstm | |
| # frontend related | |
| frontend: WavFrontend | |
| frontend_conf: | |
| fs: 16000 | |
| window: hamming | |
| n_mels: 80 | |
| frame_length: 25 | |
| frame_shift: 10 | |
| lfr_m: 7 | |
| lfr_n: 6 | |
| specaug: SpecAugLFR | |
| specaug_conf: | |
| apply_time_warp: false | |
| time_warp_window: 5 | |
| time_warp_mode: bicubic | |
| apply_freq_mask: true | |
| freq_mask_width_range: | |
| - 0 | |
| - 30 | |
| lfr_rate: 6 | |
| num_freq_mask: 1 | |
| apply_time_mask: true | |
| time_mask_width_range: | |
| - 0 | |
| - 12 | |
| num_time_mask: 1 | |
| train_conf: | |
| accum_grad: 1 | |
| grad_clip: 5 | |
| max_epoch: 150 | |
| val_scheduler_criterion: | |
| - valid | |
| - acc | |
| best_model_criterion: | |
| - - valid | |
| - acc | |
| - max | |
| keep_nbest_models: 10 | |
| log_interval: 50 | |
| optim: adam | |
| optim_conf: | |
| lr: 0.0005 | |
| scheduler: warmuplr | |
| scheduler_conf: | |
| warmup_steps: 30000 | |
| dataset: AudioDataset | |
| dataset_conf: | |
| index_ds: IndexDSJsonl | |
| batch_sampler: DynamicBatchLocalShuffleSampler | |
| batch_type: example # example or length | |
| batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; | |
| max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, | |
| buffer_size: 500 | |
| shuffle: True | |
| num_workers: 0 | |
| tokenizer: CharTokenizer | |
| tokenizer_conf: | |
| unk_symbol: <unk> | |
| split_with_space: true | |
| ctc_conf: | |
| dropout_rate: 0.0 | |
| ctc_type: builtin | |
| reduce: true | |
| ignore_nan_grad: true | |
| normalize: null | |