# 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: UniASR
model_conf:
    ctc_weight: 0.0
    lsm_weight: 0.1
    length_normalized_loss: true
    predictor_weight: 1.0
    decoder_attention_chunk_type: chunk
    ctc_weight2: 0.0
    predictor_weight2: 1.0
    decoder_attention_chunk_type2: chunk
    loss_weight_model1: 0.5


# encoder
encoder: SANMEncoderChunkOpt
encoder_conf:
    output_size: 320
    attention_heads: 4
    linear_units: 1280
    num_blocks: 35
    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
    chunk_size:
    - 20
    - 60
    stride:
    - 10
    - 40
    pad_left:
    - 5
    - 10
    encoder_att_look_back_factor:
    - 0
    - 0
    decoder_att_look_back_factor:
    - 0
    - 0

# decoder
decoder: FsmnDecoderSCAMAOpt
decoder_conf:
    attention_dim: 256
    attention_heads: 4
    linear_units: 1024
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
    att_layer_num: 6
    kernel_size: 11
    concat_embeds: true

# predictor
predictor: CifPredictorV2
predictor_conf:
    idim: 320
    threshold: 1.0
    l_order: 1
    r_order: 1


# encoder2
encoder2: SANMEncoderChunkOpt
encoder2_conf:
    output_size: 320
    attention_heads: 4
    linear_units: 1280
    num_blocks: 20
    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: 21
    sanm_shfit: 0
    selfattention_layer_type: sanm
    chunk_size:
    - 45
    - 70
    stride:
    - 35
    - 50
    pad_left:
    - 5
    - 10
    encoder_att_look_back_factor:
    - 0
    - 0
    decoder_att_look_back_factor:
    - 0
    - 0

# decoder
decoder2: FsmnDecoderSCAMAOpt
decoder2_conf:
    attention_dim: 320
    attention_heads: 4
    linear_units: 1280
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
    att_layer_num: 6
    kernel_size: 11
    concat_embeds: true

predictor2: CifPredictorV2
predictor2_conf:
    idim: 320
    threshold: 1.0
    l_order: 1
    r_order: 1

stride_conv: stride_conv1d
stride_conv_conf:
    kernel_size: 2
    stride: 2
    pad:
    - 0
    - 1

# 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
    dither: 0.0

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
  keep_nbest_models: 10
  avg_nbest_model: 10
  log_interval: 50

optim: adam
optim_conf:
   lr: 0.0001
scheduler: warmuplr
scheduler_conf:
   warmup_steps: 30000

dataset: AudioDataset
dataset_conf:
    index_ds: IndexDSJsonl
    batch_sampler: BatchSampler
    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