| | |
|---|
| | | |
|---|
| | | - Setting parameters in `infer.sh` |
|---|
| | | - <strong>model:</strong> # model name on ModelScope |
|---|
| | | - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed |
|---|
| | | - <strong>data_dir:</strong> # the dataset dir needs to include `${data_dir}/wav.scp`. If `${data_dir}/text` is also exists, CER will be computed |
|---|
| | | - <strong>output_dir:</strong> # result dir |
|---|
| | | - <strong>batch_size:</strong> # batchsize of inference |
|---|
| | | - <strong>gpu_inference:</strong> # whether to perform gpu decoding, set false for cpu decoding |
|---|
| | | - <strong>gpuid_list:</strong> # set gpus, e.g., gpuid_list="0,1" |
|---|
| | | - <strong>njob:</strong> # the number of jobs for CPU decoding, if `gpu_inference`=false, use CPU decoding, please set `njob` |
|---|
| | | |
|---|
| | | - Then you can run the pipeline to infer with: |
|---|
| | | ```python |
|---|
| | | sh infer.sh |
|---|
| | | - Decode with multi GPUs: |
|---|
| | | ```shell |
|---|
| | | bash infer.sh \ |
|---|
| | | --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ |
|---|
| | | --data_dir "./data/test" \ |
|---|
| | | --output_dir "./results" \ |
|---|
| | | --batch_size 64 \ |
|---|
| | | --gpu_inference true \ |
|---|
| | | --gpuid_list "0,1" |
|---|
| | | ``` |
|---|
| | | |
|---|
| | | - Decode with multi-thread CPUs: |
|---|
| | | ```shell |
|---|
| | | bash infer.sh \ |
|---|
| | | --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ |
|---|
| | | --data_dir "./data/test" \ |
|---|
| | | --output_dir "./results" \ |
|---|
| | | --gpu_inference false \ |
|---|
| | | --njob 64 |
|---|
| | | ``` |
|---|
| | | |
|---|
| | | - Results |
|---|
| | | |
|---|
| | | The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. |
|---|
| | | The decoding results can be found in `${output_dir}/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. |
|---|
| | | |
|---|
| | | If you decode the SpeechIO test sets, you can use textnorm with `stage`=3, and `DETAILS.txt`, `RESULTS.txt` record the results and CER after text normalization. |
|---|
| | | |
|---|
