readme docs

README.md

@@ -20,163 +20,35 @@
 | [**Contact**](#contact)
 
 
-<a name="whats-new"></a>
-## What's new: 
-
-### FunASR runtime
-
-- 2023.07.03: 
-We have release the FunASR runtime-SDK-0.1.0, file transcription service (Mandarin) is now supported ([ZH](funasr/runtime/readme_cn.md)/[EN](funasr/runtime/readme.md))
-
-### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MeT2.0) Challenge
-
-Challenge details ref to ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html))
-
-### Speech Recognition
- 
-- Academic Models
-  - Encoder-Decoder Models (AED): [Transformer](egs/aishell/transformer), [Conformer](egs/aishell/conformer), [Branchformer](egs/aishell/branchformer)
-  - Transducer Models (RNNT): [RNNT streaming](egs/aishell/rnnt), [BAT streaming/non-streaming](egs/aishell/bat)
-  - Non-autoregressive Model (NAR): [Paraformer](egs/aishell/paraformer)
-  - Multi-speaker recognition model: [MFCCA](egs_modelscope/asr/mfcca)
-
-
-- Industrial-level Models
-  - Paraformer Models (Mandarin): [Paraformer-large](egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch), [Paraformer-large-long](egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch), [Paraformer-large streaming](egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online), [Paraformer-large-contextual](egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404)
-  - Conformer Models (English): [Conformer]()
-  - UniASR streaming offline unifying models: [16k UniASR Burmese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-my-16k-common-vocab696-pytorch/summary), [16k UniASR Hebrew](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-he-16k-common-vocab1085-pytorch/summary), [16k UniASR Urdu](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ur-16k-common-vocab877-pytorch/summary), [8k UniASR Mandarin financial domain](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-finance-vocab3445-online/summary), [16k UniASR Mandarin audio-visual domain](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-audio_and_video-vocab3445-online/summary),
-  [Southern Fujian Dialect model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/summary), [French model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/summary),  [German model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/summary),  [Vietnamese model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/summary),  [Persian model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/summary)
-
-- Speaker Recognition
-  - Speaker Verification Model: [xvector](egs_modelscope/speaker_verification)
-  - Speaker Diarization Model: [SOND](egs/callhome/diarization/sond)
+<a name="highlights"></a>
+## Highlights
+- FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker Diarization and multi-talker ASR. FunASR provides convenient scripts and tutorials, supporting inference and fine-tuning of pre-trained models.
+- We have released a vast collection of academic and industrial pretrained models on the [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition), which can be accessed through our [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md). The representative [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), a non-autoregressive end-to-end speech recognition model, has the advantages of high accuracy, high efficiency, and convenient deployment, supporting the rapid construction of speech recognition services. For more details on service deployment, please refer to the [service deployment document](funasr/runtime/readme_cn.md). 
 
-- Punctuation Restoration
-  - Chinese Punctuation Model: [CT-Transformer](egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch), [CT-Transformer streaming](egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vadrealtime-vocab272727)
 
-- Endpoint Detection
-  - [FSMN-VAD](egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common)
+<a name="whats-new"></a>
+## What's new: 
 
-- Timestamp Prediction
-  - Character-level FA Model: [TP-Aligner](egs_modelscope/tp/speech_timestamp_prediction-v1-16k-offline)
+- 2023/07/17: BAT released a low-latency and low-memory-consumption RNN-T model. For more details, please refer to ([BAT](egs/aishell/bat)).
+- 2023/07/03: The CPU version of the Chinese offline file transcription service has been released with one-click deployment. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial.md)).
+- 2023/06/26: ASRU2023 Multi-Channel Multi-Party Meeting Transcription Challenge 2.0 completed the competition and announced the results. For more details, please refer to ([M2MeT2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
 
 
-<a name="highlights"></a>
-## Highlights
-- FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker diarization and multi-talker ASR.
-- We have released a vast collection of academic and industrial pretrained models on the [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition), which can be accessed through our [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md). The representative [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) model has achieved SOTA performance in many speech recognition tasks. 
-- FunASR offers a user-friendly pipeline for fine-tuning pretrained models from the [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition). Additionally, the optimized dataloader in FunASR enables faster training speeds for large-scale datasets. This feature enhances the efficiency of the speech recognition process for researchers and practitioners.
-
 <a name="Installation"></a>
 ## Installation
 
-Install from pip
-```shell
-pip3 install -U funasr
-# For the users in China, you could install with the command:
-# pip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
-```
-
-Or install from source code
 
+Please ref to [installation docs](https://alibaba-damo-academy.github.io/FunASR/en/installation/installation.html)
 
-``` sh
-git clone https://github.com/alibaba/FunASR.git && cd FunASR
-pip3 install -e ./
-# For the users in China, you could install with the command:
-# pip3 install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
+## Deployment Service
 
-```
-If you want to use the pretrained models in ModelScope, you should install the modelscope:
-
-```shell
-pip3 install -U modelscope
-# For the users in China, you could install with the command:
-# pip3 install -U modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -i https://mirror.sjtu.edu.cn/pypi/web/simple
-```
+FunASR supports pre-trained or further fine-tuned models for deployment as a service. The CPU version of the Chinese offline file conversion service has been released, details can be found in [docs](funasr/runtime/docs/SDK_tutorial.md). More detailed information about service deployment can be found in the [deployment roadmap](funasr/runtime/readme_cn.md).
 
-For more details, please ref to [installation](https://alibaba-damo-academy.github.io/FunASR/en/installation/installation.html)
 
 <a name="quick-start"></a>
 ## 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)
+FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), 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](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):
 
 <a name="contact"></a>
 ## Contact
@@ -194,24 +66,12 @@ If you have any questions about FunASR, please contact us by
 | <div align="left"><img src="docs/images/damo.png" width="180"/> | <div align="left"><img src="docs/images/nwpu.png" width="260"/> | <img src="docs/images/China_Telecom.png" width="200"/> </div>  | <img src="docs/images/RapidAI.png" width="200"/> </div> | <img src="docs/images/aihealthx.png" width="200"/> </div> |
 |:---------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|
 
-## Acknowledge
-
-1. We borrowed a lot of code from [Kaldi](http://kaldi-asr.org/) for data preparation.
-2. We borrowed a lot of code from [ESPnet](https://github.com/espnet/espnet). FunASR follows up the training and finetuning pipelines of ESPnet.
-3. We referred [Wenet](https://github.com/wenet-e2e/wenet) for building dataloader for large scale data training.
-4. We acknowledge [ChinaTelecom](https://github.com/zhuzizyf/damo-fsmn-vad-infer-httpserver) for contributing the VAD runtime. 
-5. We acknowledge [RapidAI](https://github.com/RapidAI) for contributing the Paraformer and CT_Transformer-punc runtime.
-6. We acknowledge [AiHealthx](http://www.aihealthx.com/) for contributing the websocket service and html5.
-
+he contributor list can be found in [contributors]((./Acknowledge))
 ## License
 This project is licensed under the [The MIT License](https://opensource.org/licenses/MIT). FunASR also contains various third-party components and some code modified from other repos under other open source licenses.
 The use of pretraining model is subject to [model licencs](./MODEL_LICENSE)
 
 
-## Stargazers over time
-
-[![Stargazers over time](https://starchart.cc/alibaba-damo-academy/FunASR.svg)](https://starchart.cc/alibaba-damo-academy/FunASR)
-
 ## Citations
 
 ``` bibtex