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| | | These two files both have two columns, while the first column is wav ids and the second column is the corresponding wav paths/label tokens. |
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| | | ### Stage 1: Feature and CMVN Generation |
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| | | This stage computes CMVN based on `train` dataset, which is used in the following stages. Users can set `nj` to control the number of jobs for computing CMVN. The generated CMVN file is saved as `$feats_dir/data/train/cmvn/cmvn.mvn`. |
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| | | This stage computes CMVN based on `train` dataset, which is used in the following stages. Users can set `nj` to control the number of jobs for computing CMVN. The generated CMVN file is saved as `$feats_dir/data/train/cmvn/am.mvn`. |
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| | | ### Stage 2: Dictionary Preparation |
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| | | This stage processes the dictionary, which is used as a mapping between label characters and integer indices during ASR training. The processed dictionary file is saved as `$feats_dir/data/$lang_toekn_list/$token_type/tokens.txt`. An example of `tokens.txt` is as follows: |
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| | | * Decoding by CPU or GPU |
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| | | We support CPU and GPU decoding. For CPU decoding, |
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| | | We support CPU and GPU decoding. For CPU decoding, set `gpu_inference=false` and `njob` to specific the total number of CPU jobs. For GPU decoding, first set `gpu_inference=true`. Then set `gpuid_list` to specific which GPUs for decoding and `njob` to specific the number of decoding jobs on each GPU. |
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