python/FunASR-XL.git

			@@ -1,56 +1,69 @@
			<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>
			[//]: # (<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_EN](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://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md)
			\| [Contact](#contact)


			## What's new:

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

			## Highlights
			- FunASR supports many types of models, such as, Tranformer, Conformer, [Paraformer](https://arxiv.org/abs/2206.08317).
			- A large number of ASR models trained on academic datasets or industrial datasets are open sourced 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 first place on many task in [SpeechIO leaderboard](https://github.com/SpeechColab/Leaderboard)
			- FunASR supports large-scale dataset dataloader and multi-GPU training.
			- 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)
			- 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.

			## Installation(Training and Developing)
			## Installation

			- Clone the repo:
			``` sh
			git clone https://github.com/alibaba/FunASR.git
			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
			```

			- Install Conda:
			``` sh
			wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
			sh Miniconda3-latest-Linux-x86_64.sh
			conda create -n funasr python=3.7
			conda activate funasr
			```
			Or install from source code

			- Install Pytorch (version >= 1.7.0):

			\| cuda \| \|
			\|:-----:\| --- \|
			\| 9.2 \| conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=9.2 -c pytorch \|
			\| 10.2 \| conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch \|
			\| 11.1 \| conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch \|

			For more versions, please see [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally)

			- Install ModelScope:
			``` sh
			pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
			```

			For more details about modelscope, please see [modelscope installation](https://modelscope.cn/docs/%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85)

			- Install FunASR and other packages:

			``` sh
			pip install --editable ./
			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
			```

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

			We have trained many academic and industrial models, [model hub](docs/modelscope_models.md)
			[//]: # ()
			[//]: # (## 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

			@@ -58,15 +71,21 @@

			- email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com)

			- Dingding group:
			<div align="left"><img src="docs/images/dingding.jpg" width="400"/></div>
			\|Dingding group \| Wechat group \|
			\|:---:\|:-----------------------------------------------------:\|
			\|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> \| <img src="docs/images/wechat.png" width="232"/></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/DeepScience.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 [DeepScience](https://www.deepscience.cn) for contributing the grpc service.

			## 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.
			@@ -87,4 +106,10 @@
			booktitle={INTERSPEECH},
			year={2022}
			}
			@inproceedings{Shi2023AchievingTP,
			title={Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model},
			author={Xian Shi and Yanni Chen and Shiliang Zhang and Zhijie Yan},
			booktitle={arXiv preprint arXiv:2301.12343}
			year={2023}
			}
			```