python/FunASR-XL.git

			@@ -13,7 +13,6 @@
			\| [Highlights](#highlights)
			\| [Installation](#installation)
			\| [Docs](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
			\| [Tutorial_CN](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C)
			\| [Papers](https://github.com/alibaba-damo-academy/FunASR#citations)
			\| [Runtime](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
			\| [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
			@@ -27,11 +26,9 @@
			For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)

			## Highlights
			- FunASR supports speech recognition(ASR), Multi-talker ASR, Voice Activity Detection(VAD), Punctuation Restoration, Language Models, Speaker Verification and Speaker diarization.
			- We have released large number of academic and industrial pretrained models on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition), ref to [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
			- The pretrained model [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) obtains the best performance on many tasks in [SpeechIO leaderboard](https://github.com/SpeechColab/Leaderboard)
			- FunASR supplies a easy-to-use pipeline to finetune pretrained models from [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)
			- Compared to [Espnet](https://github.com/espnet/espnet) framework, the training speed of large-scale datasets in FunASR is much faster owning to the optimized dataloader.
			- 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.

			## Installation

			@@ -75,8 +72,8 @@

			## 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/DeepScience.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> \|
			\|:---------------------------------------------------------------:\|:---------------------------------------------------------------:\|:--------------------------------------------------------------:\|:-------------------------------------------------------:\|:-----------------------------------------------------------:\|

			## Acknowledge

			@@ -85,11 +82,11 @@
			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 [DeepScience](https://www.deepscience.cn) for contributing the grpc service.
			6. We acknowledge [AiHealthx](http://www.aihealthx.com/) for contributing the websocket service and html5.

			## 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)

			## Citations