# https://yaml.org/type/float.html data: train_manifest: data/manifest.tiny dev_manifest: data/manifest.tiny test_manifest: data/manifest.tiny min_input_len: 0.5 # second max_input_len: 30.0 # second min_output_len: 0.0 # tokens max_output_len: 400.0 # tokens min_output_input_ratio: 0.05 max_output_input_ratio: 10.0 collator: mean_std_filepath: "" vocab_filepath: data/vocab.txt unit_type: 'spm' spm_model_prefix: 'data/bpe_unigram_200' augmentation_config: conf/augmentation.json batch_size: 4 raw_wav: True # use raw_wav or kaldi feature specgram_type: fbank #linear, mfcc, fbank feat_dim: 80 delta_delta: False dither: 1.0 target_sample_rate: 16000 max_freq: None n_fft: None stride_ms: 10.0 window_ms: 25.0 use_dB_normalization: True target_dB: -20 random_seed: 0 keep_transcription_text: False sortagrad: True shuffle_method: batch_shuffle num_workers: 2 # network architecture model: cmvn_file: "data/mean_std.json" cmvn_file_type: "json" # encoder related encoder: conformer encoder_conf: output_size: 256 # dimension of attention attention_heads: 4 linear_units: 2048 # the number of units of position-wise feed forward num_blocks: 12 # the number of encoder blocks dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8 normalize_before: True use_cnn_module: True cnn_module_kernel: 15 activation_type: 'swish' pos_enc_layer_type: 'rel_pos' selfattention_layer_type: 'rel_selfattn' causal: True use_dynamic_chunk: True cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster use_dynamic_left_chunk: false # decoder related decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 # hybrid CTC/attention model_conf: ctc_weight: 0.3 lsm_weight: 0.1 # label smoothing option length_normalized_loss: false training: n_epoch: 20 accum_grad: 1 global_grad_clip: 5.0 optim: adam optim_conf: lr: 0.001 weight_decay: 1e-06 scheduler: warmuplr # pytorch v1.1.0+ required scheduler_conf: warmup_steps: 25000 lr_decay: 1.0 log_interval: 1 checkpoint: kbest_n: 10 latest_n: 1 decoding: batch_size: 64 error_rate_type: wer decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring' lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm alpha: 2.5 beta: 0.3 beam_size: 10 cutoff_prob: 1.0 cutoff_top_n: 0 num_proc_bsearch: 8 ctc_weight: 0.5 # ctc weight for attention rescoring decode mode. decoding_chunk_size: -1 # decoding chunk size. Defaults to -1. # <0: for decoding, use full chunk. # >0: for decoding, use fixed chunk size as set. # 0: used for training, it's prohibited here. num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1. simulate_streaming: False # simulate streaming inference. Defaults to False.