
## Introduce


Voice activity detection (VAD) plays a important role in speech recognition systems by detecting the beginning and end of effective speech. FunASR provides an efficient VAD model based on the [FSMN structure](https://arxiv.org/abs/1803.05030). To improve model discrimination, we use monophones as modeling units, given the relatively rich speech information. During inference, the VAD system requires post-processing for improved robustness, including operations such as threshold settings and sliding windows.

This repository demonstrates how to leverage FSMN-VAD in conjunction with the funasr_onnx runtime. The underlying model is derived from [FunASR](https://github.com/alibaba-damo-academy/FunASR), which was trained on a massive 5,000-hour dataset.

We have relesed numerous industrial-grade models, including speech recognition, voice activity detection, punctuation restoration, speaker verification, speaker diarization, and timestamp prediction (force alignment). To learn more about these models, kindly refer to the [documentation](https://alibaba-damo-academy.github.io/FunASR/en/index.html) available on FunASR. If you are interested in leveraging advanced AI technology for your speech-related projects, we invite you to explore the possibilities offered by [FunASR](https://github.com/alibaba-damo-academy/FunASR).

## Install funasr_onnx

```shell
pip install -U funasr_onnx
# For the users in China, you could install with the command:
# pip install -U funasr_onnx -i https://mirror.sjtu.edu.cn/pypi/web/simple
```

## Download the model

```shell
git lfs install
git clone https://huggingface.co/funasr/FSMN-VAD
```

## Inference with runtime

### Voice Activity Detection
#### FSMN-VAD
 ```python
 from funasr_onnx import Fsmn_vad

 model_dir = "./FSMN-VAD"
 model = Fsmn_vad(model_dir, quantize=True)

 wav_path = "./FSMN-VAD/asr_example.wav"

 result = model(wav_path)
 print(result)
 ```
- `model_dir`: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`
- `batch_size`: `1` (Default), the batch size duration inference
- `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu)
- `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir`
- `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU

Input: wav formt file, support formats: `str, np.ndarray, List[str]`

Output: `List[str]`: recognition result


## Citations

``` bibtex
@inproceedings{gao2022paraformer,
  title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
  author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie},
  booktitle={INTERSPEECH},
  year={2022}
}
```
{"license": "apache-2.0", "tags": ["FunASR", "FSMN-VAD"], "pipeline_tag": "voice-activity-detection"}
onnx FunASR FSMN-VAD voice-activity-detection arxiv:1803.05030 license:apache-2.0 region:us
TAGS
#onnx #FunASR #FSMN-VAD #voice-activity-detection #arxiv-1803.05030 #license-apache-2.0 #region-us 


## Introduce


Voice activity detection (VAD) plays a important role in speech recognition systems by detecting the beginning and end of effective speech. FunASR provides an efficient VAD model based on the FSMN structure. To improve model discrimination, we use monophones as modeling units, given the relatively rich speech information. During inference, the VAD system requires post-processing for improved robustness, including operations such as threshold settings and sliding windows.

This repository demonstrates how to leverage FSMN-VAD in conjunction with the funasr_onnx runtime. The underlying model is derived from FunASR, which was trained on a massive 5,000-hour dataset.

We have relesed numerous industrial-grade models, including speech recognition, voice activity detection, punctuation restoration, speaker verification, speaker diarization, and timestamp prediction (force alignment). To learn more about these models, kindly refer to the documentation available on FunASR. If you are interested in leveraging advanced AI technology for your speech-related projects, we invite you to explore the possibilities offered by FunASR.

## Install funasr_onnx



## Download the model



## Inference with runtime

### Voice Activity Detection
#### FSMN-VAD
 
- 'model_dir': the model path, which contains 'URL', 'URL', 'URL'
- 'batch_size': '1' (Default), the batch size duration inference
- 'device_id': '-1' (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu)
- 'quantize': 'False' (Default), load the model of 'URL' in 'model_dir'. If set 'True', load the model of 'model_quant.onnx' in 'model_dir'
- 'intra_op_num_threads': '4' (Default), sets the number of threads used for intraop parallelism on CPU

Input: wav formt file, support formats: 'str, np.ndarray, List[str]'

Output: 'List[str]': recognition result


s


## Introduce


Voice activity detection (VAD) plays a important role in speech recognition systems by detecting the beginning and end of effective speech. FunASR provides an efficient VAD model based on the FSMN structure. To improve model discrimination, we use monophones as modeling units, given the relatively rich speech information. During inference, the VAD system requires post-processing for improved robustness, including operations such as threshold settings and sliding windows.

This repository demonstrates how to leverage FSMN-VAD in conjunction with the funasr_onnx runtime. The underlying model is derived from FunASR, which was trained on a massive 5,000-hour dataset.

We have relesed numerous industrial-grade models, including speech recognition, voice activity detection, punctuation restoration, speaker verification, speaker diarization, and timestamp prediction (force alignment). To learn more about these models, kindly refer to the documentation available on FunASR. If you are interested in leveraging advanced AI technology for your speech-related projects, we invite you to explore the possibilities offered by FunASR. ## Install funasr_onnx ## Download the model ## Inference with runtime ### Voice Activity Detection #### FSMN-VAD
 
- 'model_dir': the model path, which contains 'URL', 'URL', 'URL'
- 'batch_size': '1' (Default), the batch size duration inference
- 'device_id': '-1' (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu)
- 'quantize': 'False' (Default), load the model of 'URL' in 'model_dir'. If set 'True', load the model of 'model_quant.onnx' in 'model_dir'
- 'intra_op_num_threads': '4' (Default), sets the number of threads used for intraop parallelism on CPU

Input: wav formt file, support formats: 'str, np.ndarray, List[str]'

Output: 'List[str]': recognition result


s
TAGS
#onnx #FunASR #FSMN-VAD #voice-activity-detection #arxiv-1803.05030 #license-apache-2.0 #region-us 
 ## Introduce


Voice activity detection (VAD) plays a important role in speech recognition systems by detecting the beginning and end of effective speech. FunASR provides an efficient VAD model based on the FSMN structure. To improve model discrimination, we use monophones as modeling units, given the relatively rich speech information. During inference, the VAD system requires post-processing for improved robustness, including operations such as threshold settings and sliding windows.

This repository demonstrates how to leverage FSMN-VAD in conjunction with the funasr_onnx runtime. The underlying model is derived from FunASR, which was trained on a massive 5,000-hour dataset.

We have relesed numerous industrial-grade models, including speech recognition, voice activity detection, punctuation restoration, speaker verification, speaker diarization, and timestamp prediction (force alignment). To learn more about these models, kindly refer to the documentation available on FunASR. If you are interested in leveraging advanced AI technology for your speech-related projects, we invite you to explore the possibilities offered by FunASR. ## Install funasr_onnx ## Download the model ## Inference with runtime ### Voice Activity Detection #### FSMN-VAD
 
- 'model_dir': the model path, which contains 'URL', 'URL', 'URL'
- 'batch_size': '1' (Default), the batch size duration inference
- 'device_id': '-1' (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu)
- 'quantize': 'False' (Default), load the model of 'URL' in 'model_dir'. If set 'True', load the model of 'model_quant.onnx' in 'model_dir'
- 'intra_op_num_threads': '4' (Default), sets the number of threads used for intraop parallelism on CPU

Input: wav formt file, support formats: 'str, np.ndarray, List[str]'

Output: 'List[str]': recognition result


s
TAGS
#onnx #FunASR #FSMN-VAD #voice-activity-detection #arxiv-1803.05030 #license-apache-2.0 #region-us 
## Introduce


Voice activity detection (VAD) plays a important role in speech recognition systems by detecting the beginning and end of effective speech. FunASR provides an efficient VAD model based on the FSMN structure. To improve model discrimination, we use monophones as modeling units, given the relatively rich speech information. During inference, the VAD system requires post-processing for improved robustness, including operations such as threshold settings and sliding windows.

This repository demonstrates how to leverage FSMN-VAD in conjunction with the funasr_onnx runtime. The underlying model is derived from FunASR, which was trained on a massive 5,000-hour dataset.

We have relesed numerous industrial-grade models, including speech recognition, voice activity detection, punctuation restoration, speaker verification, speaker diarization, and timestamp prediction (force alignment). To learn more about these models, kindly refer to the documentation available on FunASR. If you are interested in leveraging advanced AI technology for your speech-related projects, we invite you to explore the possibilities offered by FunASR.## Install funasr_onnx## Download the model## Inference with runtime### Voice Activity Detection#### FSMN-VAD
 
- 'model_dir': the model path, which contains 'URL', 'URL', 'URL'
- 'batch_size': '1' (Default), the batch size duration inference
- 'device_id': '-1' (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu)
- 'quantize': 'False' (Default), load the model of 'URL' in 'model_dir'. If set 'True', load the model of 'model_quant.onnx' in 'model_dir'
- 'intra_op_num_threads': '4' (Default), sets the number of threads used for intraop parallelism on CPU

Input: wav formt file, support formats: 'str, np.ndarray, List[str]'

Output: 'List[str]': recognition result


s