Update README.md

README.md

@@ -4,4 +4,64 @@ pipeline_tag: voice-activity-detection
 tags:
 - FunASR
 - FSMN-VAD
----
+---
+
+## 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 60,000-hour Mandarin dataset. Notably, Paraformer's performance secured the top spot on the [SpeechIO leaderboard](https://github.com/SpeechColab/Leaderboard), highlighting its exceptional capabilities in speech recognition.
+
+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 clone https://huggingface.co/funasr/paraformer-large
+```
+
+## 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}
+}
+```