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嘉渊 2023-05-24 11:44:05 +08:00
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docs/academic_recipe/asr_recipe.md
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@ -83,7 +83,6 @@ This stage computes CMVN based on `train` dataset, which is used in the followin
### Stage 2: Dictionary Preparation
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/$token_type/tokens.txt`. An example of `tokens.txt` is as follows:
* `tokens.txt`
```
<blank>
<s>
@ -95,10 +94,10 @@ This stage processes the dictionary, which is used as a mapping between label ch
龟
<unk>
```
* `<blank>`: indicates the blank token for CTC
* `<s>`: indicates the start-of-sentence token
* `</s>`: indicates the end-of-sentence token
* `<unk>`: indicates the out-of-vocabulary token
* `<blank>`: indicates the blank token for CTC, must be in the first line
* `<s>`: indicates the start-of-sentence token, must be in the second line
* `</s>`: indicates the end-of-sentence token, must be in the third line
* `<unk>`: indicates the out-of-vocabulary token, must be in the last line
### Stage 3: LM Training
@ -146,7 +145,6 @@ We support CPU and GPU decoding in FunASR. For CPU decoding, you should set `gpu
* Performance
We adopt `CER` to verify the performance. The results are in `$exp_dir/exp/$model_dir/$decoding_yaml_name/$average_model_name/$dset`, namely `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text while `text.cer.txt` saves the final `CER` results. The following is an example of `text.cer`:
* `text.cer`
```
...
BAC009S0764W0213(nwords=11,cor=11,ins=0,del=0,sub=0) corr=100.00%,cer=0.00%