mirror of
https://github.com/modelscope/FunASR
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b28f3c9da9
* funasr1.0 funetine * funasr1.0 pbar * update with main (#1260) * Update websocket_protocol_zh.md * update --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * update with main (#1264) * Funasr1.0 (#1261) * funasr1.0 funetine * funasr1.0 pbar * update with main (#1260) * Update websocket_protocol_zh.md * update --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * bug fix --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * funasr1.0 sanm scama * funasr1.0 infer_after_finetune * funasr1.0 fsmn-vad bug fix * funasr1.0 fsmn-vad bug fix --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>
179 lines
3.9 KiB
YAML
179 lines
3.9 KiB
YAML
# This is an example that demonstrates how to configure a model file.
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# You can modify the configuration according to your own requirements.
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# to print the register_table:
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# from funasr.register import tables
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# tables.print()
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# network architecture
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model: UniASR
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model_conf:
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ctc_weight: 0.0
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lsm_weight: 0.1
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length_normalized_loss: true
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predictor_weight: 1.0
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decoder_attention_chunk_type: chunk
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ctc_weight2: 0.0
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predictor_weight2: 1.0
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decoder_attention_chunk_type2: chunk
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loss_weight_model1: 0.5
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# encoder
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encoder: SANMEncoderChunkOpt
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encoder_conf:
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output_size: 320
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attention_heads: 4
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linear_units: 1280
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num_blocks: 35
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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_shfit: 0
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selfattention_layer_type: sanm
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chunk_size: [20, 60]
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stride: [10, 40]
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pad_left: [5, 10]
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encoder_att_look_back_factor: [0, 0]
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decoder_att_look_back_factor: [0, 0]
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# decoder
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decoder: FsmnDecoderSCAMAOpt
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decoder_conf:
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attention_dim: 256
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attention_heads: 4
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linear_units: 1024
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num_blocks: 12
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.1
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src_attention_dropout_rate: 0.1
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att_layer_num: 6
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kernel_size: 11
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concat_embeds: true
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predictor: CifPredictorV2
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predictor_conf:
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idim: 320
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threshold: 1.0
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l_order: 1
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r_order: 1
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encoder2: SANMEncoderChunkOpt
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encoder2_conf:
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output_size: 320
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attention_heads: 4
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linear_units: 1280
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num_blocks: 20
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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: 21
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sanm_shfit: 0
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selfattention_layer_type: sanm
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chunk_size: [45, 70]
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stride: [35, 50]
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pad_left: [5, 10]
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encoder_att_look_back_factor: [0, 0]
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decoder_att_look_back_factor: [0, 0]
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decoder2: FsmnDecoderSCAMAOpt
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decoder2_conf:
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attention_dim: 320
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attention_heads: 4
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linear_units: 1280
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num_blocks: 12
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.1
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src_attention_dropout_rate: 0.1
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att_layer_num: 6
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kernel_size: 11
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concat_embeds: true
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predictor2: CifPredictorV2
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predictor2_conf:
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idim: 320
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threshold: 1.0
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l_order: 1
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r_order: 1
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stride_conv: stride_conv1d
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stride_conv_conf:
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kernel_size: 2
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stride: 2
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pad: [0, 1]
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# frontend related
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frontend: WavFrontendOnline
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frontend_conf:
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fs: 16000
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window: hamming
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n_mels: 80
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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: 150
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keep_nbest_models: 10
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avg_nbest_model: 5
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log_interval: 50
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optim: adam
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optim_conf:
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lr: 0.0001
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 30000
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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: DynamicBatchLocalShuffleSampler
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batch_type: example # example or length
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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;
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max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
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buffer_size: 500
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shuffle: True
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num_workers: 0
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tokenizer: CharTokenizer
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tokenizer_conf:
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unk_symbol: <unk>
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split_with_space: true
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ctc_conf:
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dropout_rate: 0.0
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ctc_type: 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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