docs

游雁

2023-10-19 b9bcf1f093c3053fdc4e2cf4a1d38e27bbf429fb

 README.md

@@ -28,9 +28,13 @@

<a name="whats-new"></a>
## What's new: 
- 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial_online_zh.md)).
- 2023/10/17: The offline file transcription service (CPU) of English has been released. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial_en.md)).
- 2023/10/13: [SlideSpeech](https://slidespeech.github.io/): A large scale multi-modal audio-visual corpus with a significant amount of real-time synchronized slides.
- 2023/10/10: The ASR-SpeakersDiarization combined pipeline [Paraformer-VAD-SPK](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr_vad_spk/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/demo.py) is now released. Experience the model to get recognition results with speaker information.
- 2023/10/07: [FunCodec](https://github.com/alibaba-damo-academy/FunCodec): A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec.
- 2023/09/01: The offline file transcription service 2.0 (CPU) of Mandarin has been released, with added support for ffmpeg, timestamp, and hotword models. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial.md)).
- 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial_online.md)).
- 2023/07/17: BAT is released, which is a low-latency and low-memory-consumption RNN-T model. For more details, please refer to ([BAT](egs/aishell/bat)).
- 2023/07/03: The offline file transcription service (CPU) of Mandarin has been released. 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)).


@@ -46,7 +50,7 @@

<a name="quick-start"></a>
## Quick Start
Quick start for new users（[tutorial](https://alibaba-damo-academy.github.io/FunASR/en/funasr/quick_start_zh.html)）
Quick start for new users（[tutorial](https://alibaba-damo-academy.github.io/FunASR/en/funasr/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):
@@ -59,18 +63,18 @@

|DingTalk group |                     WeChat group                      |
|:---:|:-----------------------------------------------------:|
|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="232"/></div> |
|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="215"/></div> |

## Contributors

| <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> |
|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|
| <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> | <img src="docs/images/XVERSE.png" width="250"/> </div> |
|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------:|

The contributors can be found in [contributors list]((./Acknowledge))
The contributors can be found in [contributors list](./Acknowledge.md)

## 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)
The use of pretraining model is subject to [model license](./MODEL_LICENSE)


## Citations
@@ -87,6 +91,12 @@
  year={2023},
  booktitle={INTERSPEECH},
}
@inproceedings{wang2023told,
  author={Jiaming Wang and Zhihao Du and Shiliang Zhang},
  title={{TOLD:} {A} Novel Two-Stage Overlap-Aware Framework for Speaker Diarization},
  year={2023},
  booktitle={ICASSP},
}
@inproceedings{gao22b_interspeech,
  author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
  title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}},