# 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](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) ## Release Notes: ### 2023.1.16, funasr-0.1.6 - 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 type model, [VAD](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 [Model Zoo](docs/modelscope_models.md). - We release a new type 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 type 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... ## Key Features - 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). - 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) - Compared to [Espnet](https://github.com/espnet/espnet) framework, the training speed of large-scale datasets in FunASR is much faster owning to the optimized dataloader. ## Installation(Training and Developing) - Install Conda: ``` sh wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh sh Miniconda3-latest-Linux-x86_64.sh source ~/.bashrc conda create -n funasr python=3.7 conda activate funasr ``` - Install Pytorch (version >= 1.7.0): ``` sh pip3 install torch torchvision torchaudio ``` For more versions, please see [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally) If you are in the area of China, you could set the source to speed the downloading. ``` sh pip config set global.index-url https://mirror.sjtu.edu.cn/pypi/web/simple ``` - Install ModelScope: ``` sh pip install "modelscope[audio]" -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) - Install FunASR and other packages: ``` sh git clone https://github.com/alibaba/FunASR.git && cd FunASR pip install --editable ./ ``` ## Pretrained Model Zoo We have trained many academic and industrial models, [model hub](docs/modelscope_models.md) ## Contact If you have any questions about FunASR, please contact us by - email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com) - Dingding group:
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## Acknowledge 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. ## 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. ## Citations ``` bibtex @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} } ```