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@ -93,7 +93,7 @@ training:
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decoding:
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decoding:
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batch_size: 1
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batch_size: 128
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error_rate_type: cer
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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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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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lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
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@ -104,11 +104,11 @@ decoding:
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cutoff_top_n: 0
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cutoff_top_n: 0
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num_proc_bsearch: 8
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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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ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
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decoding_chunk_size: 16 # decoding chunk size. Defaults to -1.
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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 full chunk.
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# >0: for decoding, use fixed chunk size as set.
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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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# 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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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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simulate_streaming: true # simulate streaming inference. Defaults to False.
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