update model-revision by model name

雾聪

2023-12-04 ef07c0d48faa0c19d4ac35f23832069c1bfe04bf

 README.md

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[//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>)

([简体中文](./README_zh.md)|English)

# 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！
<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, 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！

[**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new) 
| [**Highlights**](#highlights)
[**Highlights**](#highlights)
| [**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new) 
| [**Installation**](#installation)
| [**Docs**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
| [**Tutorial_CN**](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/model_zoo/modelscope_models.md)
| [**Quick Start**](#quick-start)
| [**Runtime**](./runtime/readme.md)
| [**Model Zoo**](#model-zoo)
| [**Contact**](#contact)
| [**M2MET2.0 Challenge**](https://github.com/alibaba-damo-academy/FunASR#multi-channel-multi-party-meeting-transcription-20-m2met20-challenge)

## What's new: 
### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge
We are pleased to announce that the M2MeT2.0 challenge has been accepted by the ASRU2023 challenge special session. The registration is now open. The baseline system is conducted on FunASR and is provided as a receipe of AliMeeting corpus. For more details you can see the guidence of M2MET2.0 ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
### Release notes
For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)

<a name="highlights"></a>
## 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), ref to [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
- 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.
- FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker Diarization and multi-talker ASR. FunASR provides convenient scripts and tutorials, supporting inference and fine-tuning of pre-trained models.
- We have released a vast collection of academic and industrial pretrained models on the [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition) and [huggingface](https://huggingface.co/FunASR), which can be accessed through our [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md). The representative [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), a non-autoregressive end-to-end speech recognition model, has the advantages of high accuracy, high efficiency, and convenient deployment, supporting the rapid construction of speech recognition services. For more details on service deployment, please refer to the [service deployment document](runtime/readme_cn.md). 


<a name="whats-new"></a>
## What's new: 
- 2023/11/08: The offline file transcription service 3.0 (CPU) of Mandarin has been released, adding punctuation large model, Ngram language model, and wfst hot words. For detailed information, please refer to [docs](runtime#file-transcription-service-mandarin-cpu). 
- 2023/10/17: The offline file transcription service (CPU) of English has been released. For more details, please refer to ([docs](runtime#file-transcription-service-english-cpu)).
- 2023/10/13: [SlideSpeech](https://slidespeech.github.io/): A large scale multi-modal audio-visual corpus with a significant amount of real-time synchronized slides.
- 2023/10/10: The ASR-SpeakersDiarization combined pipeline [Paraformer-VAD-SPK](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr_vad_spk/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/demo.py) is now released. Experience the model to get recognition results with speaker information.
- 2023/10/07: [FunCodec](https://github.com/alibaba-damo-academy/FunCodec): A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec.
- 2023/09/01: The offline file transcription service 2.0 (CPU) of Mandarin has been released, with added support for ffmpeg, timestamp, and hotword models. For more details, please refer to ([docs](runtime#file-transcription-service-mandarin-cpu)).
- 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to ([docs](runtime#the-real-time-transcription-service-mandarin-cpu)).
- 2023/07/17: BAT is released, which is a low-latency and low-memory-consumption RNN-T model. For more details, please refer to ([BAT](egs/aishell/bat)).
- 2023/06/26: ASRU2023 Multi-Channel Multi-Party Meeting Transcription Challenge 2.0 completed the competition and announced the results. For more details, please refer to ([M2MeT2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).


<a name="Installation"></a>
## Installation

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
```
Please ref to [installation docs](https://alibaba-damo-academy.github.io/FunASR/en/installation/installation.html)

Or install from source code
## Model Zoo
FunASR has open-sourced a large number of pre-trained models on industrial data. You are free to use, copy, modify, and share FunASR models under the [Model License Agreement](./MODEL_LICENSE). Below are some representative models, for more models please refer to the [Model Zoo]().

(Note: 🤗 represents the Huggingface model zoo link, ⭐ represents the ModelScope model zoo link)


``` sh
git clone https://github.com/alibaba/FunASR.git && cd FunASR
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
|                                                                             Model Name                                                                             |                                Task Details                                 |          Training Date           | Parameters |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------:|:--------------------------------:|:----------:|
|    paraformer-zh <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)  [🤗]() )    |             speech recognition, with timestamps, non-streaming              |      60000 hours, Mandarin       |    220M    |
|                paraformer-zh-spk <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary)  [🤗]() )                | speech recognition with speaker diarization, with timestamps, non-streaming |      60000 hours, Mandarin       |    220M    |
| <nobr>paraformer-zh-online <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗]() )</nobr> |                        speech recognition, streaming                        |      60000 hours, Mandarin       |    220M    |
|         paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗]() )         |             speech recognition, with timestamps, non-streaming              |       50000 hours, English       |    220M    |
|                                                               paraformer-en-spk <br> ([⭐]()[🤗]()  )                                                               |         speech recognition with speaker diarization, non-streaming          |               Undo               |    Undo    |
|                     conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗]() )                      |                      speech recognition, non-streaming                      |       50000 hours, English       |    220M    |
|                     ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗]() )                      |                           punctuation restoration                           |    100M, Mandarin and English    |    1.1G    | 
|                          fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗]() )                          |                          voice activity detection                           | 5000 hours, Mandarin and English |    0.4M    | 
|                          fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗]() )                           |                            timestamp prediction                             |       5000 hours, Mandarin       |    38M     | 

```
If you want to use the pretrained models in ModelScope, you should install the modelscope:



[//]: # ()
[//]: # (FunASR supports pre-trained or further fine-tuned models for deployment as a service. The CPU version of the Chinese offline file conversion service has been released, details can be found in [docs]&#40;funasr/runtime/docs/SDK_tutorial.md&#41;. More detailed information about service deployment can be found in the [deployment roadmap]&#40;funasr/runtime/readme_cn.md&#41;.)


<a name="quick-start"></a>
## Quick Start
Quick start for new users（[tutorial](https://alibaba-damo-academy.github.io/FunASR/en/funasr/quick_start.html)）

FunASR supports inference and fine-tuning of models trained on industrial data for tens of thousands of hours. For more details, please refer to [modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html). It also supports training and fine-tuning of models on academic standard datasets. For more information, please refer to [egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html).

Below is a quick start tutorial. Test audio files ([Mandarin](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav), [English]()).

### Command-line usage

```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
funasr --model paraformer-zh asr_example_zh.wav
```

For more details, please ref to [installation](https://alibaba-damo-academy.github.io/FunASR/en/installation/installation.html)
Notes: Support recognition of single audio file, as well as file list in Kaldi-style wav.scp format: `wav_id wav_pat`

### Speech Recognition (Non-streaming)
```python
from funasr import infer

p = infer(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc", model_hub="ms")

res = p("asr_example_zh.wav", batch_size_token=5000)
print(res)
```
Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.

### Speech Recognition (Streaming)
```python
from funasr import infer

p = infer(model="paraformer-zh-streaming", model_hub="ms")

chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size, "encoder_chunk_look_back": 4, "decoder_chunk_look_back": 1}

import torchaudio
speech = torchaudio.load("asr_example_zh.wav")[0][0]
speech_length = speech.shape[0]

stride_size = chunk_size[1] * 960
sample_offset = 0
for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
    param_dict["is_final"] = True if sample_offset + stride_size >= speech_length - 1 else False
    input = speech[sample_offset: sample_offset + stride_size]
    rec_result = p(input=input, param_dict=param_dict)
    print(rec_result)
```
Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.

Quick start for new users can be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/funasr/quick_start_zh.html)


## Contact
[//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to &#40;[modelscope_egs]&#40;https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html&#41;&#41;. It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to&#40;[egs]&#40;https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html&#41;&#41;. The models include speech recognition &#40;ASR&#41;, speech activity detection &#40;VAD&#41;, punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo]&#40;https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md&#41;:)

If you have any questions about FunASR, please contact us by
## Deployment Service
FunASR supports deploying pre-trained or further fine-tuned models for service. Currently, it supports the following types of service deployment:
- File transcription service, Mandarin, CPU version, done
- The real-time transcription service, Mandarin (CPU), done
- File transcription service, English, CPU version, done
- File transcription service, Mandarin, GPU version, in progress
- and more.

- email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com)
For more detailed information, please refer to the [service deployment documentation](runtime/readme.md).

|Dingding group |                     Wechat group                      |

<a name="contact"></a>
## Community Communication
If you encounter problems in use, you can directly raise Issues on the github page.

You can also scan the following DingTalk group or WeChat group QR code to join the community group for communication and discussion.

|DingTalk group |                     WeChat group                      |
|:---:|:-----------------------------------------------------:|
|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="232"/></div> |
|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="215"/></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/China_Telecom.png" width="200"/> </div>  | <img src="docs/images/RapidAI.png" width="200"/> </div> | <img src="docs/images/DeepScience.png" width="200"/> </div> |
|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|
| <div align="left"><img src="docs/images/alibaba.png" width="260"/> | <div align="left"><img src="docs/images/nwpu.png" width="260"/> | <img src="docs/images/China_Telecom.png" width="200"/> </div>  | <img src="docs/images/RapidAI.png" width="200"/> </div> | <img src="docs/images/aihealthx.png" width="200"/> </div> | <img src="docs/images/XVERSE.png" width="250"/> </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 [ChinaTelecom](https://github.com/zhuzizyf/damo-fsmn-vad-infer-httpserver) for contributing the VAD runtime. 
5. We acknowledge [RapidAI](https://github.com/RapidAI) for contributing the Paraformer and CT_Transformer-punc runtime.
6. We acknowledge [DeepScience](https://www.deepscience.cn) for contributing the grpc service.
The contributors can be found in [contributors list](./Acknowledge.md)

## 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.
The use of pretraining model is subject to [model license](./MODEL_LICENSE)


## Citations

``` bibtex
@inproceedings{gao2022paraformer,
  title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
  author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie},
@inproceedings{gao2023funasr,
  author={Zhifu Gao and Zerui Li and Jiaming Wang and Haoneng Luo and Xian Shi and Mengzhe Chen and Yabin Li and Lingyun Zuo and Zhihao Du and Zhangyu Xiao and Shiliang Zhang},
  title={FunASR: A Fundamental End-to-End Speech Recognition Toolkit},
  year={2023},
  booktitle={INTERSPEECH},
  year={2022}
}
@inproceedings{gao2020universal,
  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={2020}
@inproceedings{An2023bat,
  author={Keyu An and Xian Shi and Shiliang Zhang},
  title={BAT: Boundary aware transducer for memory-efficient and low-latency ASR},
  year={2023},
  booktitle={INTERSPEECH},
}
@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}
@inproceedings{gao22b_interspeech,
  author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
  title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={2063--2067},
  doi={10.21437/Interspeech.2022-9996}
}
```