From d29f201e3201bde6a984e436888a2aae877e449f Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 19 三月 2024 12:04:50 +0800
Subject: [PATCH] vad conf

---
 README.md |   40 +++++++++++++++++++++++++++++++++++-----
 1 files changed, 35 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index d34249d..a4511b9 100644
--- a/README.md
+++ b/README.md
@@ -112,7 +112,7 @@
 from funasr import AutoModel
 # paraformer-zh is a multi-functional asr model
 # use vad, punc, spk or not as you need
-model = AutoModel(model="paraformer-zh",  vad_model="fsmn-vad",  punc_model="ct-punc-c", 
+model = AutoModel(model="paraformer-zh",  vad_model="fsmn-vad",  punc_model="ct-punc", 
                   # spk_model="cam++", 
                   )
 res = model.generate(input=f"{model.model_path}/example/asr_example.wav", 
@@ -210,7 +210,37 @@
 
 More examples ref to [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)
 
-[//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to &#40;[modelscope_egs]&#40;https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html&#41;&#41;. It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to&#40;[egs]&#40;https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html&#41;&#41;. The models include speech recognition &#40;ASR&#41;, speech activity detection &#40;VAD&#41;, punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo]&#40;https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md&#41;:)
+
+## Export ONNX
+
+### Command-line usage
+```shell
+funasr-export ++model=paraformer ++quantize=false ++device=cpu
+```
+
+### Python
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="paraformer", device="cpu")
+
+res = model.export(quantize=False)
+```
+
+### Test ONNX
+```python
+# pip3 install -U funasr-onnx
+from funasr_onnx import Paraformer
+model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+model = Paraformer(model_dir, batch_size=1, quantize=True)
+
+wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
+
+result = model(wav_path)
+print(result)
+```
+
+More examples ref to [demo](runtime/python/onnxruntime)
 
 ## Deployment Service
 FunASR supports deploying pre-trained or further fine-tuned models for service. Currently, it supports the following types of service deployment:
@@ -229,9 +259,9 @@
 
 You can also scan the following DingTalk group or WeChat group QR code to join the community group for communication and discussion.
 
-|DingTalk group |                     WeChat group                      |
-|:---:|:-----------------------------------------------------:|
-|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="215"/></div> |
+|                           DingTalk group                            |                     WeChat group                      |
+|:-------------------------------------------------------------------:|:-----------------------------------------------------:|
+| <div align="left"><img src="docs/images/dingding.png" width="250"/> | <img src="docs/images/wechat.png" width="215"/></div> |
 
 ## Contributors
 

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