From 24f73665e2d8ea8e4de2fe4f900bc539d7f7b989 Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期一, 17 四月 2023 15:49:45 +0800
Subject: [PATCH] Merge pull request #367 from alibaba-damo-academy/dev_lhn2
---
README.md | 108 ++++++++++++++++++++++++++++++------------------------
1 files changed, 60 insertions(+), 48 deletions(-)
diff --git a/README.md b/README.md
index 6bf1278..11aa88f 100644
--- a/README.md
+++ b/README.md
@@ -1,69 +1,69 @@
-<div align="left"><img src="docs/images/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
+<p align="left">
+ <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-brightgreen.svg"></a>
+ <a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a>
+ <a href=""><img src="https://img.shields.io/badge/Pytorch-%3E%3D1.11-blue"></a>
+</p>
-<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
-- 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...
+[**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new)
+| [**Highlights**](#highlights)
+| [**Installation**](#installation)
+| [**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://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md)
+| [**Contact**](#contact)
-## 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).
+
+## What's new:
+
+For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)
+
+## Highlights
+- FunASR supports speech recognition(ASR), Multi-talker ASR, Voice Activity Detection(VAD), Punctuation Restoration, Language Models, Speaker Verification and Speaker diarization.
- 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)
+## Installation
-- 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 from pip
+```shell
+pip install -U funasr
+# For the users in China, you could install with the command:
+# pip install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
```
-- 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)
+Or install from source code
-
-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 ./
+pip install -e ./
+# For the users in China, you could install with the command:
+# pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
+
+```
+If you want to use the pretrained models in ModelScope, you should install the modelscope:
+
+```shell
+pip install -U modelscope
+# For the users in China, you could install with the command:
+# pip install -U modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -i https://mirror.sjtu.edu.cn/pypi/web/simple
```
-## Pretrained Model Zoo
+For more details, please ref to [installation](https://github.com/alibaba-damo-academy/FunASR/wiki)
-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([CN](https://alibaba-damo-academy.github.io/FunASR/cn/index.html) / [EN](https://alibaba-damo-academy.github.io/FunASR/en/index.html)))
## Contact
@@ -71,15 +71,21 @@
- 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="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.
@@ -100,4 +106,10 @@
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}
+}
```
--
Gitblit v1.9.1