docs

docs/modescope_pipeline/vad_pipeline.md

@@ -1,14 +1,107 @@
 # Voice Activity Detection
 
-## Inference with pipeline
+> **Note**: 
+> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take model of FSMN-VAD as example to demonstrate the usage.
+
+## Inference
 
 ### Quick start
+#### [FSMN-VAD model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary)
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.voice_activity_detection,
+    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+)
+
+segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav')
+print(segments_result)
+```
+#### [FSMN-VAD-online model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary)
+```python
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    )
+import soundfile
+speech, sample_rate = soundfile.read("example/asr_example.wav")
+
+param_dict = {"in_cache": dict(), "is_final": False}
+chunk_stride = 1600# 100ms
+# first chunk, 100ms
+speech_chunk = speech[0:chunk_stride] 
+rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
+print(rec_result)
+# next chunk, 480ms
+speech_chunk = speech[chunk_stride:chunk_stride+chunk_stride]
+rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
+print(rec_result)
+```
+Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/236)
+
+
+#### API-reference
+##### 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
+- `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
+  asr_example1  ./audios/asr_example1.wav
+  asr_example2  ./audios/asr_example2.wav
+  ```
+  In this case of `wav.scp` input, `output_dir` must be set to save the output results
+- `audio_fs`: audio sampling rate, only set when audio_in is pcm audio
+- `output_dir`: None (Defalut), the output path of results if set
+
+### Inference with multi-thread CPUs or multi GPUs
+FunASR also offer recipes [run.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/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`
+
+- Decode with multi GPUs:
+```shell
+    bash infer.sh \
+    --model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
+    --data_dir "./data/test" \
+    --output_dir "./results" \
+    --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" \
+    --data_dir "./data/test" \
+    --output_dir "./results" \
+    --gpu_inference false \
+    --njob 64
+```
+
+- Results
 
-### Inference with you data
+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.
 
-### Inference with multi-threads on CPU
+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.
 
-### Inference with multi GPU
 
 ## Finetune with pipeline
 

funasr/bin/asr_inference_paraformer.py

@@ -631,7 +631,9 @@ def inference_modelscope(
         export_mode = param_dict.get("export_mode", False)
     else:
         hotword_list_or_file = None
-
+    
+    if kwargs.get("device", None) == "cpu":
+        ngpu = 0
     if ngpu >= 1 and torch.cuda.is_available():
         device = "cuda"
     else: