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docs/modescope_pipeline/vad_pipeline.md
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@ -42,22 +42,23 @@ print(rec_result)
Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/236)
#### API-reference
##### define pipeline
##### Define pipeline
- `task`: `Tasks.auto_speech_recognition`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: 1 (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: 1 (Defalut), sets the number of threads used for intraop parallelism on CPU
- `output_dir`: None (Defalut), the output path of results if set
- `batch_size`: 1 (Defalut), batch size when decoding
##### infer pipeline
- `ngpu`: `1` (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: `1` (Defalut), sets the number of threads used for intraop parallelism on CPU
- `output_dir`: `None` (Defalut), the output path of results if set
- `batch_size`: `1` (Defalut), batch size when decoding
##### Infer pipeline
- `audio_in`: the input to decode, which could be:
- wav_path, `e.g.`: asr_example.wav,
- pcm_path, `e.g.`: asr_example.pcm,
- audio bytes stream, `e.g.`: bytes data from a microphone
- audio sample point，`e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, the dtype is numpy.ndarray or torch.Tensor
- wav.scp, kaldi style wav list (`wav_id \t wav_path``), `e.g.`:
```cat wav.scp
```text
asr_example1 ./audios/asr_example1.wav
asr_example2 ./audios/asr_example2.wav
```
@ -66,42 +67,39 @@ Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/
- `output_dir`: None (Defalut), the output path of results if set
### Inference with multi-thread CPUs or multi GPUs
FunASR also offer recipes [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE//infer.sh) to decode with multi-thread CPUs, or multi GPUs.
FunASR also offer recipes [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
- Setting parameters in `infer.sh`
- <strong>model:</strong> # model name on ModelScope
- <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`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `data_dir`: the dataset dir needs to include `wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
- `output_dir`: output dir of the recognition results
- `batch_size`: `64` (Default), batch size of inference on gpu
- `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference
- `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer
- `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding
- `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models
- `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer
- Decode with multi GPUs:
```shell
bash infer.sh \
--model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
--model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
--batch_size 64 \
--gpu_inference true \
--gpuid_list "0,1"
```
- Decode with multi-thread CPUs:
```shell
bash infer.sh \
--model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
--model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
--gpu_inference false \
--njob 64
```
- Results
The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
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.
## Finetune with pipeline

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egs_modelscope/asr/TEMPLATE/README.md
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@ -62,10 +62,10 @@ Undo
##### Define pipeline
- `task`: `Tasks.auto_speech_recognition`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: 1 (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: 1 (Defalut), sets the number of threads used for intraop parallelism on CPU
- `output_dir`: None (Defalut), the output path of results if set
- `batch_size`: 1 (Defalut), batch size when decoding
- `ngpu`: `1` (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: `1` (Defalut), sets the number of threads used for intraop parallelism on CPU
- `output_dir`: `None` (Defalut), the output path of results if set
- `batch_size`: `1` (Defalut), batch size when decoding
##### Infer pipeline
- `audio_in`: the input to decode, which could be:
- wav_path, `e.g.`: asr_example.wav,