readme docs

docs/index.rst

@@ -20,6 +20,12 @@ Overview
    ./installation/installation.md
    ./installation/docker.md
 
+.. toctree::
+   :maxdepth: 1
+   :caption: Quick Start
+
+   ./qick_start.md
+
 .. toctree::
    :maxdepth: 1
    :caption: Academic Egs

docs/qick_start.md

@@ -0,0 +1,80 @@
+
+# Quick Start
+
+You can use FunASR in the following ways:
+
+- Service Deployment SDK
+- Industrial model egs
+- Academic model egs
+
+## Service Deployment SDK
+
+### Python version Example
+Supports real-time streaming speech recognition, uses non-streaming models for error correction, and outputs text with punctuation. Currently, only single client is supported. For multi-concurrency, please refer to the C++ version service deployment SDK below.
+
+#### Server Deployment
+
+```shell
+cd funasr/runtime/python/websocket
+python funasr_wss_server.py --port 10095
+```
+
+#### Client Testing
+
+```shell
+python funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode 2pass --chunk_size "5,10,5"
+```
+
+For more examples, please refer to [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/websocket_python.html#id2).
+
+### C++ version Example
+
+Currently, offline file transcription service (CPU) is supported, and concurrent requests of hundreds of channels are supported.
+
+#### Server Deployment
+
+You can use the following command to complete the deployment with one click:
+
+```shell
+curl -O https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/shell/funasr-runtime-deploy-offline-cpu-zh.sh
+sudo bash funasr-runtime-deploy-offline-cpu-zh.sh install --workspace ./funasr-runtime-resources
+```
+
+#### Client Testing
+
+```shell
+python3 funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode offline --audio_in "../audio/asr_example.wav"
+```
+
+For more examples, please refer to [docs](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/docs/SDK_tutorial_zh.md)
+
+
+## Industrial Model Egs
+
+If you want to use the pre-trained industrial models in ModelScope for inference or fine-tuning training, you can refer to the following command:
+
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+)
+
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
+print(rec_result)
+# {'text': '欢迎大家来体验达摩院推出的语音识别模型'}
+```
+
+More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)
+
+## Academic model egs
+
+If you want to train from scratch, usually for academic models, you can start training and inference with the following command:
+
+```shell
+cd egs/aishell/paraformer
+. ./run.sh --CUDA_VISIBLE_DEVICES="0,1" --gpu_num=2
+```
+More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)