From 4e37a5fda20f0878b593b8ba2b9ea46db63743b5 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期四, 11 五月 2023 14:16:28 +0800
Subject: [PATCH] update repo
---
README.md | 73 +++++++++++++++++++++---------------
1 files changed, 42 insertions(+), 31 deletions(-)
diff --git a/README.md b/README.md
index 795a130..e5a940c 100644
--- a/README.md
+++ b/README.md
@@ -1,44 +1,43 @@
-<div align="left"><img src="image/funasr_logo.jpg" width="400"/></div>
+[//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>)
# 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�
-## Installation(Training and Developing)
+[**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new)
+| [**Highlights**](#highlights)
+| [**Installation**](#installation)
+| [**Docs_CN**](https://alibaba-damo-academy.github.io/FunASR/cn/index.html)
+| [**Docs_EN**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
+| [**Tutorial**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C)
+| [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations)
+| [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
+| [**Model Zoo**](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
+| [**Contact**](#contact)
-- Clone the repo:
-``` sh
-git clone https://github.com/alibaba/FunASR.git
-```
-- Install Conda:
-``` sh
-wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
-sh Miniconda3-latest-Linux-x86_64.sh
-conda create -n funasr python=3.7
-conda activate funasr
-```
+## What's new:
-- Install Pytorch (version >= 1.7.0):
+For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)
-| cuda | |
-|:-----:| --- |
-| 9.2 | conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=9.2 -c pytorch |
-| 10.2 | conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch |
-| 11.1 | conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch |
+## Highlights
+- 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.
-For more versions, please see https://pytorch.org/get-started/locally/
-
-- Install ModelScope:
-``` sh
-pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
-```
-
-- Install other packages:
+## Installation
``` sh
-pip install --editable ./
+pip install "modelscope[audio_asr]" --upgrade -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+git clone https://github.com/alibaba/FunASR.git && cd FunASR
+pip install -e ./
```
+For more details, please ref to [installation](https://github.com/alibaba-damo-academy/FunASR/wiki)
+
+## Usage
+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))
## Contact
@@ -46,15 +45,21 @@
- email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com)
-- Dingding group:
-<div align="left"><img src="image/dingding.jpg" width="400"/></div>
+|Dingding group | Wechat group |
+|:---:|:-----------------------------------------------------:|
+|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="232"/></div> |
+## Contributors
+
+| <div align="left"><img src="docs/images/damo.png" width="180"/> | <div align="left"><img src="docs/images/nwpu.png" width="260"/> | <img src="docs/images/DeepScience.png" width="200"/> </div> |
+|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:-----------------------------------------------------------:|
## 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.
@@ -66,7 +71,7 @@
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={2010}
+ year={2020}
}
@inproceedings{gao2022paraformer,
@@ -75,4 +80,10 @@
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}
+}
```
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