python/FunASR-XL.git

			@@ -1,69 +1,47 @@
			<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

			<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！[Model Zoo](docs/modelscope_models.md)
			<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！

			## Release Notes:
			### 2023.1.16, funasr-0.1.6
			- We release a new version model [Paraformer-large-long](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), which integrate the [VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) model, [ASR](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary),
			[Punctuation](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) model and timestamp together. The model could take in several hours long inputs.
			- We release a new type model, [VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary), which could predict the duration of none-silence speech. It could be freely integrated with any ASR models in [Model Zoo](docs/modelscope_models.md).
			- We release a new type model, [Punctuation](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary), which could predict the punctuation of ASR models's results. It could be freely integrated with any ASR models in [Model Zoo](docs/modelscope_models.md).
			- We release a new model, [Data2vec](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch/summary), an unsupervised pretraining model which could be finetuned on ASR and other downstream tasks.
			- We release a new model, [Paraformer-Tiny](https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary), a lightweight Paraformer model which supports Mandarin command words recognition.
			- We release a new type model, [SV](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary), which could extract speaker embeddings and further perform speaker verification on paired utterances. It will be supported for speaker diarization in the future version.
			- We improve the pipeline of modelscope to speedup the inference, by integrating the process of build model into build pipeline.
			- Various new types of audio input types are now supported by modelscope inference pipeline, including wav.scp, wav format, audio bytes, wave samples...
			[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)
			\| [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)
			\| [Contact](#contact)

			## Key Features
			[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).
			- 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)

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

			- Install Pytorch (version >= 1.7.0):
			``` sh
			pip3 install torch torchvision torchaudio
			```
			For more versions, please see [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally)


			If you are in the area of China, you could set the source to speed the downloading.
			## Installation

			``` sh
			pip config set global.index-url https://mirror.sjtu.edu.cn/pypi/web/simple
			```

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

			## Pretrained Model Zoo

			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

			@@ -71,15 +49,21 @@

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

			- Dingding group:
			<div align="left"><img src="docs/images/dingding.jpg" width="250"/>!<img src="docs/images/wechat.png" width="222"/></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.
			@@ -100,4 +84,10 @@
			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}
			}
			```