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| | | <div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div> |
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| | | [//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>) |
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| | | |
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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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| | | ### 2023.1.16, funasr-0.1.6 |
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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_CN**](https://alibaba-damo-academy.github.io/FunASR/cn/index.html) |
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| | | | [**Docs_EN**](https://alibaba-damo-academy.github.io/FunASR/en/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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| | | ### 2023.2.17, funasr-0.2.0, modelscope-1.3.0 |
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| | | - We support a new feature, export paraformer models into [onnx and torchscripts](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export) from modelscope. The local finetuned models are also supported. |
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| | | - We support a new feature, [onnxruntime](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python), you could deploy the runtime without modelscope or funasr, for the [paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) model, the rtf of onnxruntime is 3x speedup(0.110->0.038) on cpu, [details](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer#speed). |
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| | | - We support a new feature, [grpc](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/grpc), you could build the ASR service with grpc, by deploying the modelscope pipeline or onnxruntime. |
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| | | - We release a new model [paraformer-large-contextual](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary), which supports the hotword customization based on the incentive enhancement, and improves the recall and precision of hotwords. |
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| | | - We optimize the timestamp alignment of [Paraformer-large-long](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), the prediction accuracy of timestamp is much improved, and achieving accumulated average shift (aas) of 74.7ms, [details](https://arxiv.org/abs/2301.12343). |
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| | | - We release a new model, [8k VAD model](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 [modelscope](https://github.com/alibaba-damo-academy/FunASR/discussions/134). |
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| | | - We release a new model, [MFCCA](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary), a multi-channel multi-speaker model which is independent of the number and geometry of microphones and supports Mandarin meeting transcription. |
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| | | - We release several new UniASR model: |
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| | | [Southern Fujian Dialect model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/summary), |
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| | | [French model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/summary), |
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| | | [German model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/summary), |
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| | | [Vietnamese model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/summary), |
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| | | [Persian model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/summary). |
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| | | - We release a new model, [paraformer-data2vec model](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/summary), an unsupervised pretraining model on AISHELL-2, which is inited for paraformer model and then finetune on AISHEL-1. |
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| | | - We release a new feature, the `VAD`, `ASR` and `PUNC` models could be integrated freely, which could be models from [modelscope](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), or the local finetine models. The [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/134). |
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| | | - We optimized the [punctuation common model](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary), enhance the recall and precision, fix the badcases of missing punctuation marks. |
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| | | - Various new types of audio input types are now supported by modelscope inference pipeline, including: mp3、flac、ogg、opus... |
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| | | ### 2023.1.16, funasr-0.1.6， modelscope-1.2.0 |
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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 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). |
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| | | - 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). |
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| | | - We release a new model, [16k VAD model](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 [modelscope](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary). |
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| | | - We release a new 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). |
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| | | - 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. |
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| | | - 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. |
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| | | - 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. |
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| | | - We release a new 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. |
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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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| | | If you are in the area of China, you could set the source to speed 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 ModelScope: |
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| | | ``` sh |
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| | | pip install "modelscope[audio]" -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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| | | |
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| | | ``` sh |
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| | | pip install "modelscope[audio_asr]" --upgrade -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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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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| | | |
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| | | ## Usage |
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| | | For users who are new to FunASR and ModelScope, please refer to FunASR Docs([CN](https://alibaba-damo-academy.github.io/FunASR/cn/index.html) / [EN](https://alibaba-damo-academy.github.io/FunASR/en/index.html)) |
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| | | |
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| | | ## Contact |
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| | | - email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com) |
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| | | |
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| | | - Dingding group: |
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| | | <div align="left"><img src="docs/images/dingding.jpg" width="250"/> <img src="docs/images/wechat.png" width="222"/></div> |
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| | | |Dingding group | Wechat group | |
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| | | |:---:|:-----------------------------------------------------:| |
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| | | |<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="232"/></div> | |
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| | | |
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| | | ## Contributors |
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| | | |
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| | | | <div align="left"><img src="docs/images/damo.png" width="180"/> | <img src="docs/images/DeepScience.png" width="200"/> </div> | |
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| | | |:---------------------------------------------------------------:|:-----------------------------------------------------------:| |
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| | | |
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| | | ## Acknowledge |
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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://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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| | | @inproceedings{Shi2023AchievingTP, |
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| | | title={Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model}, |
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| | | author={Xian Shi and Yanni Chen and Shiliang Zhang and Zhijie Yan}, |
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| | | booktitle={arXiv preprint arXiv:2301.12343} |
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| | | year={2023} |
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| | | } |
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| | | ``` |
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