python/FunASR-XL.git

			@@ -1,66 +1,71 @@
			[//]: # (<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 Challenge](https://github.com/alibaba-damo-academy/FunASR#multi-channel-multi-party-meeting-transcription-20-m2met20-challenge)

			## What's new:

			### 2023.2.16, funasr-0.2.0, modelscope-1.3.0
			- We support a new feature, export paraformer models into [onnx and torchscripts](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export) from modelscope. The local finetuned models are also supported.
			- We support a new feature, [onnxruntime](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer), you could deploy the runtime without modelscope or funasr, for the [paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) model, the rtf of onnxruntime is 3x speedup(0.110->0.038) on cpu, [details](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer#speed).
			- We support a new feature, [grpc](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/grpc), you could build the ASR service with grpc, by deploying the modelscope pipeline or onnxruntime.
			- We release a new model [paraformer-large-contextual](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary), which supports the hotword customization based on the incentive enhancement, and improves the recall and precision of hotwords.
			- We optimize the timestamp alignment of [Paraformer-large-long](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), the prediction accuracy of timestamp is much improved, and achieving accumulated average shift (aas) of 74.7ms, [details](https://arxiv.org/abs/2301.12343).
			- We release a new model, [8k VAD model](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 [modelscope](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary).
			- We release a new model, [MFCCA](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary), a multi-channel multi-speaker model which is independent of the number and geometry of microphones and supports Mandarin meeting transcription.
			- We release several new UniASR model:
			[Southern Fujian Dialect model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/summary),
			[French model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/summary),
			[German model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/summary),
			[Vietnamese model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/summary),
			[Persian model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/summary).
			- We release a new model, [paraformer-data2vec model](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/summary), an unsupervised pretraining model on AISHELL-2, which is inited for paraformer model and then finetune on AISHEL-1.
			- Various new types of audio input types are now supported by modelscope inference pipeline, including: mp3、flac、ogg、opus...
			### 2023.1.16, funasr-0.1.6， modelscope-1.2.0
			- 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 model, [16k VAD model](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 [modelscope](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary).
			- We release a new 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 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...
			### 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)).
			### Release notes
			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)
			- 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://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html)
			- 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

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

			## 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))
			```
			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://alibaba-damo-academy.github.io/FunASR/en/installation.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

			@@ -68,21 +73,23 @@

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

			\|Dingding group \| Wechat 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/DeepScience.png" width="250"/> \|
			\|:---:\|
			\| <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> \|
			\|:---------------------------------------------------------------:\|:---------------------------------------------------------------:\|:--------------------------------------------------------------:\|:-------------------------------------------------------:\|:-----------------------------------------------------------:\|

			## 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.
			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.

			## 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.
			@@ -90,18 +97,17 @@
			## Citations

			``` bibtex
			@inproceedings{gao2020universal,
			title={Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model},
			author={Gao, Zhifu and Zhang, Shiliang and Lei, Ming and McLoughlin, Ian},
			booktitle={arXiv preprint arXiv:2010.14099},
			year={2020}
			}

			@inproceedings{gao2022paraformer,
			title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
			author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie},
			booktitle={INTERSPEECH},
			year={2022}
			}
			@inproceedings{gao2020universal,
			title={Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model},
			author={Gao, Zhifu and Zhang, Shiliang and Lei, Ming and McLoughlin, Ian},
			booktitle={arXiv preprint arXiv:2010.14099},
			year={2020}
			}
			@inproceedings{Shi2023AchievingTP,
			title={Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model},
			@@ -109,4 +115,4 @@
			booktitle={arXiv preprint arXiv:2301.12343}
			year={2023}
			}
			```
			```