From fcc9c89eaba9a4e36c54958aeedeec7ab3756cd7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 13 二月 2023 17:43:31 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
README.md | 67 +++++++++++++--------------------
1 files changed, 26 insertions(+), 41 deletions(-)
diff --git a/README.md b/README.md
index 6bf1278..c759b79 100644
--- a/README.md
+++ b/README.md
@@ -2,9 +2,19 @@
# 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:
+[**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new)
+| [**Highlights**](#highlights)
+| [**Installation**](#installation)
+| [**Docs**](https://alibaba-damo-academy.github.io/FunASR/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)
+
+## What's new:
### 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.
@@ -16,54 +26,23 @@
- 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
+## 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.
-## 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 +50,21 @@
- 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.
@@ -100,4 +85,4 @@
booktitle={INTERSPEECH},
year={2022}
}
-```
+```
\ No newline at end of file
--
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