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# https://yaml.org/type/float.html
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# network architecture
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model:
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cmvn_file:
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cmvn_file_type: "json"
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# encoder related
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encoder: transformer
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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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# 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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ctc_dropoutrate: 0.0
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ctc_grad_norm_type: null
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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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-clean
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collator:
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vocab_filepath: data/lang_char/train_960_unigram5000_units.txt
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unit_type: spm
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spm_model_prefix: data/lang_char/train_960_unigram5000
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feat_dim: 83
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stride_ms: 10.0
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window_ms: 25.0
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sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
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batch_size: 30
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maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced
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maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced
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minibatches: 0 # for debug
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batch_count: auto
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batch_bins: 0
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batch_frames_in: 0
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batch_frames_out: 0
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batch_frames_inout: 0
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augmentation_config: conf/preprocess.yaml
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num_workers: 0
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subsampling_factor: 1
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num_encs: 1
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training:
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n_epoch: 120
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accum_grad: 2
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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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optim: adam
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optim_conf:
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global_grad_clip: 5.0
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weight_decay: 1.0e-06
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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lr: 0.004
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warmup_steps: 25000
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lr_decay: 1.0
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decoding:
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batch_size: 1
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error_rate_type: wer
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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: False # simulate streaming inference. Defaults to False.
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