python/FunASR-XL.git

			@@ -1,51 +1,31 @@
			[//]: # (<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)
			\| [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)


			## 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...
			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)
			@@ -53,14 +33,37 @@

			## 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 ./
			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

			@@ -68,14 +71,14 @@

			- 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/DeepScience.png" width="200"/> </div> \|
			\|:---------------------------------------------------------------:\|:---------------------------------------------------------------:\|:-----------------------------------------------------------:\|

			## Acknowledge

			@@ -109,4 +112,4 @@
			booktitle={arXiv preprint arXiv:2301.12343}
			year={2023}
			}
			```
			```