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# https://yaml.org/type/float.html
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data:
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train_manifest: data/manifest.train
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dev_manifest: data/manifest.dev
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test_manifest: data/manifest.test
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min_input_len: 0.5
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max_input_len: 20.0 # second
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min_output_len: 0.0
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max_output_len: 400.0
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min_output_input_ratio: 0.05
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max_output_input_ratio: 10.0
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collator:
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vocab_filepath: data/vocab.txt
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unit_type: 'char'
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spm_model_prefix: ''
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augmentation_config: conf/augmentation.json
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batch_size: 32
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raw_wav: True # use raw_wav or kaldi feature
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spectrum_type: fbank #linear, mfcc, fbank
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feat_dim: 80
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delta_delta: False
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dither: 1.0
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target_sample_rate: 8000
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max_freq: None
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n_fft: None
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stride_ms: 10.0
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window_ms: 25.0
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use_dB_normalization: True
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target_dB: -20
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random_seed: 0
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keep_transcription_text: False
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sortagrad: True
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shuffle_method: batch_shuffle
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num_workers: 2
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# network architecture
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model:
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cmvn_file: "data/mean_std.json"
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cmvn_file_type: "json"
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# encoder related
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encoder: conformer
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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: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # 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: 0.0
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input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before: True
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use_cnn_module: True
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cnn_module_kernel: 15
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activation_type: 'swish'
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pos_enc_layer_type: 'rel_pos'
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selfattention_layer_type: 'rel_selfattn'
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causal: true
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use_dynamic_chunk: true
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cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
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use_dynamic_left_chunk: false
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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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: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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training:
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n_epoch: 240
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accum_grad: 4
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global_grad_clip: 5.0
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optim: adam
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optim_conf:
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lr: 0.001
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weight_decay: 1e-6
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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lr_decay: 1.0
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log_interval: 100
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checkpoint:
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kbest_n: 50
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latest_n: 5
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decoding:
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batch_size: 128
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error_rate_type: cer
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decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
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lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
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alpha: 2.5
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beta: 0.3
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beam_size: 10
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cutoff_prob: 1.0
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cutoff_top_n: 0
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num_proc_bsearch: 8
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ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
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decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
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# <0: for decoding, use full chunk.
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# >0: for decoding, use fixed chunk size as set.
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# 0: used for training, it's prohibited here.
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num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
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simulate_streaming: true # simulate streaming inference. Defaults to False.
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