python/FunASR-XL.git

			@@ -12,7 +12,7 @@
			[News](https://github.com/alibaba-damo-academy/FunASR#whats-new)
			\| [Highlights](#highlights)
			\| [Installation](#installation)
			\| [Docs](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
			\| [Usage](#usage)
			\| [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/model_zoo/modelscope_models.md)
			@@ -20,9 +20,18 @@
			\| [M2MET2.0 Challenge](https://github.com/alibaba-damo-academy/FunASR#multi-channel-multi-party-meeting-transcription-20-m2met20-challenge)

			## What's new:

			### FunASR runtime-SDK

			- 2023.07.03:
			We have release the FunASR runtime-SDK-0.1.0, file transcription service (Mandarin) is now supported ([ZH](funasr/runtime/readme_cn.md)/[EN](funasr/runtime/readme.md))

			### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MeT2.0) Challenge

			We are pleased to announce that the M2MeT2.0 challenge has been accepted by the ASRU 2023 challenge special session. The registration is now open. 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
			@@ -34,9 +43,9 @@

			Install from pip
			```shell
			pip install -U funasr
			pip3 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
			# pip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
			```

			Or install from source code
			@@ -44,22 +53,71 @@

			``` sh
			git clone https://github.com/alibaba/FunASR.git && cd FunASR
			pip install -e ./
			pip3 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
			# pip3 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
			pip3 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
			# pip3 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/installation.html)

			## Usage

			You could use FunASR by:

			- egs
			- egs_modelscope
			- runtime

			### egs
			If you want to train the model from scratch, you could use funasr directly by recipe, as the following:
			```shell
			cd egs/aishell/paraformer
			. ./run.sh --CUDA_VISIBLE_DEVICES="0,1" --gpu_num=2
			```
			More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)

			### egs_modelscope
			If you want to infer or finetune pretraining models from modelscope, you could use funasr by modelscope pipeline, as the following:

			```python
			from modelscope.pipelines import pipeline
			from modelscope.utils.constant import Tasks

			inference_pipeline = pipeline(
			task=Tasks.auto_speech_recognition,
			model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
			)

			rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
			print(rec_result)
			# {'text': '欢迎大家来体验达摩院推出的语音识别模型'}
			```
			More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)

			### runtime

			An example with websocket:

			For the server:
			```shell
			cd funasr/runtime/python/websocket
			python wss_srv_asr.py --port 10095
			```

			For the client:
			```shell
			python wss_client_asr.py --host "127.0.0.1" --port 10095 --mode 2pass --chunk_size "5,10,5"
			#python wss_client_asr.py --host "127.0.0.1" --port 10095 --mode 2pass --chunk_size "8,8,4" --audio_in "./data/wav.scp" --output_dir "./results"
			```
			More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/websocket_python.html#id2)
			## Contact

			If you have any questions about FunASR, please contact us by
			@@ -72,8 +130,8 @@

			## 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/China_Telecom.png" width="200"/> </div> \| <img src="docs/images/RapidAI.png" width="200"/> </div> \| <img src="docs/images/DeepScience.png" width="200"/> </div> \| <img src="docs/images/aihealthx.png" width="200"/> </div> \|
			\|:---------------------------------------------------------------:\|:---------------------------------------------------------------:\|:--------------------------------------------------------------:\|:-------------------------------------------------------:\|:-----------------------------------------------------------:\|:-----------------------------------------------------------:\|
			\| <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/aihealthx.png" width="200"/> </div> \|
			\|:---------------------------------------------------------------:\|:---------------------------------------------------------------:\|:--------------------------------------------------------------:\|:-------------------------------------------------------:\|:-----------------------------------------------------------:\|

			## Acknowledge

			@@ -82,13 +140,17 @@
			3. We referred [Wenet](https://github.com/wenet-e2e/wenet) for building dataloader for large scale data training.
			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.
			6. We acknowledge [AiHealthx](http://www.aihealthx.com/) for contributing the websocket service and html5.

			## 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.
			The use of pretraining model is subject to [model licencs](./MODEL_LICENSE)


			## Stargazers over time

			[![Stargazers over time](https://starchart.cc/alibaba-damo-academy/FunASR.svg)](https://starchart.cc/alibaba-damo-academy/FunASR)

			## Citations

			``` bibtex