python/FunASR-XL.git

			@@ -2,7 +2,7 @@

			# 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
			@@ -50,7 +50,7 @@

			Install or upgrade modelscope.
			``` sh
			pip install "modelscope[audio]" --upgrade -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
			pip install "modelscope[audio_asr]" --upgrade -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)
			@@ -61,6 +61,9 @@
			git clone https://github.com/alibaba/FunASR.git && cd FunASR
			pip install --editable ./
			```

			## Usage
			For users who are new to FunASR and ModelScope, please refer to [FunASR Docs](https://alibaba-damo-academy.github.io/FunASR/index.html).

			## Contact

			@@ -82,7 +85,8 @@
			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://github.com/dyyzhmm/FunASR) for contributing the grpc service.
			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.