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| | | conda activate funasr |
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| | | . ./run.sh |
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| | | ``` |
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| | | The training log files are saved in `exp/*_train_*/log/train.log.*` and the inference results are saved in `exp/*_train_*/decode_asr_*`. |
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| | | The training log files are saved in `exp/*_train_*/log/train.log.*`， which can be viewed using the following command: |
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| | | ```sh |
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| | | vim exp/*_train_*/log/train.log.0 |
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| | | ``` |
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| | | It should be looked like as follows: |
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| | | |
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| | | |
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| | | and the inference results are saved in `exp/*_train_*/decode_asr_*`. |
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| | | |
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| | | ## Introduction |
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| | | We provide a recipe `egs/aishell/paraformer/run.sh` for training a paraformer model on AISHELL-1 dataset. This recipe consists of five stages, supporting training on multiple GPUs and decoding by CPU or GPU. Before introducing each stage in detail, we first explain several parameters which should be set by users. |
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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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| | | |
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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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| | | |
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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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| | | |
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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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