funasr 0.4.1

FunASR: A Fundamental End-to-End Speech Recognition Toolkit

FunASR: A Fundamental End-to-End Speech Recognition Toolkit

FunASR 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, 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 | Highlights | Installation | Docs_EN | Tutorial | Papers | Runtime | Model Zoo | Contact

What's new:

For the release notes, please ref to news

Highlights

• Many types of typical models are supported, e.g., Tranformer, Conformer, Paraformer.
• We have released large number of academic and industrial pretrained models on ModelScope
• The pretrained model Paraformer-large obtains the best performance on many tasks in SpeechIO leaderboard
• FunASR supplies a easy-to-use pipeline to finetune pretrained models from ModelScope
• Compared to Espnet framework, the training speed of large-scale datasets in FunASR is much faster owning to the optimized dataloader.

Installation

Install from pip

pip install -U funasr -i https://pypi.Python.org/simple

Or install from source code

git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip install -e ./

If you want to use the pretrained models in ModelScope, you should install the modelscope:

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

Usage

For users who are new to FunASR and ModelScope, please refer to FunASR Docs(CN / EN)

Contact

If you have any questions about FunASR, please contact us by

• email: [e-mail]

Dingding group	Wechat group

Contributors

Acknowledge

1. We borrowed a lot of code from Kaldi for data preparation.
2. We borrowed a lot of code from ESPnet. FunASR follows up the training and finetuning pipelines of ESPnet.
3. We referred Wenet for building dataloader for large scale data training.
4. We acknowledge DeepScience for contributing the grpc service.

License

This project is licensed under the The MIT License. FunASR also contains various third-party components and some code modified from other repos under other open source licenses.

Citations

@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{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}
}