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| | | # FunASR: A Fundamental End-to-End Speech Recognition Toolkit |
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| | | <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) |
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| | | <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！ |
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| | | |
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| | | ## Release Notes: |
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| | | [**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new) |
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| | | | [**Highlights**](#highlights) |
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| | | | [**Installation**](#installation) |
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| | | | [**Docs**](https://alibaba-damo-academy.github.io/FunASR/index.html) |
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| | | | [**Tutorial**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C) |
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| | | | [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations) |
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| | | | [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime) |
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| | | | [**Model Zoo**](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) |
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| | | | [**Contact**](#contact) |
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| | | |
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| | | ## What's new: |
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| | | ### 2023.1.16, funasr-0.1.6 |
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| | | - 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), |
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| | | [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. |
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| | | - We improve the pipeline of modelscope to speedup the inference, by integrating the process of build model into build pipeline. |
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| | | - Various new types of audio input types are now supported by modelscope inference pipeline, including wav.scp, wav format, audio bytes, wave samples... |
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| | | |
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| | | ## Key Features |
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| | | ## Highlights |
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| | | - 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). |
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| | | - We have released large number of academic and industrial pretrained models on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition) |
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| | | - 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) |
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| | | |
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| | | ## Installation |
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| | | |
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| | | - Install Conda: |
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| | | ``` sh |
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| | | wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh |
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| | | sh Miniconda3-latest-Linux-x86_64.sh |
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| | | source ~/.bashrc |
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| | | conda create -n funasr python=3.7 |
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| | | conda activate funasr |
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| | | ``` |
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| | | - Install Pytorch (version >= 1.7.0): |
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| | | ``` sh |
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| | | pip3 install torch torchvision torchaudio |
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| | | ``` |
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| | | For more versions, please see [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally) |
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| | | - Install ModelScope: |
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| | | If you are in the area of China, you could set the source to speedup the downloading. |
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| | | |
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| | | ``` sh |
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| | | pip config set global.index-url https://mirror.sjtu.edu.cn/pypi/web/simple |
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| | | ``` |
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| | | Install or upgrade modelscope. |
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| | | ``` sh |
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| | | pip install "modelscope[audio]" --upgrade -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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| | | ``` |
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| | | 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) |
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| | | - Install FunASR and other packages: |
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| | | ``` sh |
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| | | git clone https://github.com/alibaba/FunASR.git && cd FunASR |
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| | | pip install --editable ./ |
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| | | ``` |
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| | | For more details, please ref to [installation](https://github.com/alibaba-damo-academy/FunASR/wiki) |
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| | | ## Usage |
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| | | For users who are new to FunASR and ModelScope, please refer to [FunASR Docs](https://alibaba-damo-academy.github.io/FunASR/index.html). |
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| | | ## Contact |
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| | | 1. We borrowed a lot of code from [Kaldi](http://kaldi-asr.org/) for data preparation. |
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| | | 2. We borrowed a lot of code from [ESPnet](https://github.com/espnet/espnet). FunASR follows up the training and finetuning pipelines of ESPnet. |
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| | | 3. We referred [Wenet](https://github.com/wenet-e2e/wenet) for building dataloader for large scale data training. |
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| | | 4. We acknowledge [DeepScience](https://github.com/dyyzhmm/FunASR) for contributing the grpc service. |
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| | | 4. We acknowledge [DeepScience](https://www.deepscience.cn) for contributing the grpc service. |
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| | | ## License |
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| | | 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. |
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| | | booktitle={INTERSPEECH}, |
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| | | year={2022} |
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| | | } |
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| | | ``` |
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| | | ``` |
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