From 6e5f075b1d9f189dd4e5400a0a228c670aa4696e Mon Sep 17 00:00:00 2001 From: hnluo <haoneng.lhn@alibaba-inc.com> Date: 星期四, 09 二月 2023 14:15:18 +0800 Subject: [PATCH] Merge pull request #80 from alibaba-damo-academy/dev --- README.md | 51 +++++++++++++-------------------------------------- 1 files changed, 13 insertions(+), 38 deletions(-) diff --git a/README.md b/README.md index 6bf1278..7b16f58 100644 --- a/README.md +++ b/README.md @@ -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锛乕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锛� ## Release Notes: ### 2023.1.16, funasr-0.1.6 @@ -23,47 +23,16 @@ - 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: +## Installation ``` sh git clone https://github.com/alibaba/FunASR.git && cd FunASR pip install --editable ./ ``` +For more details, please ref to [installation](https://github.com/alibaba-damo-academy/FunASR/wiki) -## Pretrained Model Zoo - -We have trained many academic and industrial models, [model hub](docs/modelscope_models.md) +## 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 @@ -71,15 +40,21 @@ - email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com) -- Dingding group: -<div align="left"><img src="docs/images/dingding.jpg" width="250"/>!<img src="docs/images/wechat.png" width="222"/></div> +|Dingding group | Wechat group| +|:---:|:---:| +|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> |<img src="docs/images/wechat.png" width="222"/></div>| +## Contributors + +| <div align="left"><img src="docs/images/DeepScience.png" width="250"/> | +|:---:| ## 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. +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. -- Gitblit v1.9.1