python/FunASR-XL.git

			@@ -1,27 +1,35 @@
			[//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>)

			# FunASR: A Fundamental End-to-End Speech Recognition Toolkit
			<p align="left">
			<a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-brightgreen.svg"></a>
			<a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a>
			<a href=""><img src="https://img.shields.io/badge/Pytorch-%3E%3D1.11-blue"></a>
			</p>

			<strong>FunASR</strong> hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model released on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition), researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！

			[News](https://github.com/alibaba-damo-academy/FunASR#whats-new)
			\| [Highlights](#highlights)
			\| [Installation](#installation)
			\| [Docs_CN](https://alibaba-damo-academy.github.io/FunASR/cn/index.html)
			\| [Docs_EN](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
			\| [Docs](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
			\| [Tutorial](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://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
			\| [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md)
			\| [Contact](#contact)
			\|
			[M2MET2.0 Guidence_CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)
			\| [M2MET2.0 Guidence_EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)


			## Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge
			We are pleased to announce that the M2MeT2.0 challenge will be held in the near future. The baseline system is conducted on FunASR and is provided as a receipe of AliMeeting corpus. For more details you can see the guidence of M2MET2.0 ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
			## What's new:

			For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)

			## Highlights
			- Many types of typical models are supported, e.g., [Tranformer](https://arxiv.org/abs/1706.03762), [Conformer](https://arxiv.org/abs/2005.08100), [Paraformer](https://arxiv.org/abs/2206.08317).
			- 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)
			- 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)
			@@ -29,15 +37,37 @@

			## Installation

			``` sh
			pip install "modelscope[audio_asr]" --upgrade -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
			git clone https://github.com/alibaba/FunASR.git && cd FunASR
			pip install --editable ./
			Install from pip
			```shell
			pip install -U funasr
			# For the users in China, you could install with the command:
			# pip install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
			```

			Or install from source code


			``` sh
			git clone https://github.com/alibaba/FunASR.git && cd FunASR
			pip install -e ./
			# For the users in China, you could install with the command:
			# pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple

			```
			If you want to use the pretrained models in ModelScope, you should install the modelscope:

			```shell
			pip install -U modelscope
			# For the users in China, you could install with the command:
			# pip install -U modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -i https://mirror.sjtu.edu.cn/pypi/web/simple
			```

			For more details, please ref to [installation](https://github.com/alibaba-damo-academy/FunASR/wiki)

			## Usage
			For users who are new to FunASR and ModelScope, please refer to FunASR Docs([CN](https://alibaba-damo-academy.github.io/FunASR/cn/index.html) / [EN](https://alibaba-damo-academy.github.io/FunASR/en/index.html))
			[//]: # ()
			[//]: # (## Usage)

			[//]: # (For users who are new to FunASR and ModelScope, please refer to FunASR Docs([CN](https://alibaba-damo-academy.github.io/FunASR/cn/index.html) / [EN](https://alibaba-damo-academy.github.io/FunASR/en/index.html)))

			## Contact