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https://github.com/wenet-e2e/wespeaker
Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit
https://github.com/wenet-e2e/wespeaker
asv campplus cnceleb dino ecapa-tdnn eres2net nist-sre plda production-ready pytorch repvgg resnet self-supervised-learning speaker-diarization speaker-recognition speaker-verification ssl tdnn voxceleb xvector
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Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit
- Host: GitHub
- URL: https://github.com/wenet-e2e/wespeaker
- Owner: wenet-e2e
- License: apache-2.0
- Created: 2021-09-28T15:25:44.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2024-04-10T13:44:33.000Z (5 months ago)
- Last Synced: 2024-04-10T15:33:21.107Z (5 months ago)
- Topics: asv, campplus, cnceleb, dino, ecapa-tdnn, eres2net, nist-sre, plda, production-ready, pytorch, repvgg, resnet, self-supervised-learning, speaker-diarization, speaker-recognition, speaker-verification, ssl, tdnn, voxceleb, xvector
- Language: Python
- Homepage:
- Size: 3.4 MB
- Stars: 514
- Watchers: 17
- Forks: 88
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Roadmap: ROADMAP.md
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README
# WeSpeaker
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/wenet-e2e/wespeaker)
[**Roadmap**](ROADMAP.md)
| [**Docs**](http://wenet.org.cn/wespeaker)
| [**Paper**](https://arxiv.org/abs/2210.17016)
| [**Runtime**](https://github.com/wenet-e2e/wespeaker/tree/master/runtime)
| [**Pretrained Models**](docs/pretrained.md)
| [**Huggingface Demo**](https://huggingface.co/spaces/wenet/wespeaker_demo)
| [**Modelscope Demo**](https://www.modelscope.cn/studios/wenet/Speaker_Verification_in_WeSpeaker/summary)
WeSpeaker mainly focuses on [**speaker embedding learning**](https://wsstriving.github.io/talk/ncmmsc_slides_shuai.pdf), with application to the speaker verification task. We support
online feature extraction or loading pre-extracted features in kaldi-format.
## Installation
### Install python package
``` sh
pip install git+https://github.com/wenet-e2e/wespeaker.git
```
**Command-line usage** (use `-h` for parameters):
``` sh
$ wespeaker --task embedding --audio_file audio.wav --output_file embedding.txt
$ wespeaker --task embedding_kaldi --wav_scp wav.scp --output_file /path/to/embedding
$ wespeaker --task similarity --audio_file audio.wav --audio_file2 audio2.wav
$ wespeaker --task diarization --audio_file audio.wav
```
**Python programming usage**:
``` python
import wespeaker
model = wespeaker.load_model('chinese')
embedding = model.extract_embedding('audio.wav')
utt_names, embeddings = model.extract_embedding_list('wav.scp')
similarity = model.compute_similarity('audio1.wav', 'audio2.wav')
diar_result = model.diarize('audio.wav')
```
Please refer to [python usage](docs/python_package.md) for more command line and python programming usage.
### Install for development & deployment
* Clone this repo
``` sh
git clone https://github.com/wenet-e2e/wespeaker.git
```
* Create conda env: pytorch version >= 1.12.1 is recommended !!!
``` sh
conda create -n wespeaker python=3.9
conda activate wespeaker
conda install pytorch=1.12.1 torchaudio=0.12.1 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt
pre-commit install # for clean and tidy code
```
## 🔥 News
* 2024.05.15: Add support for [quality-aware score calibration](https://arxiv.org/pdf/2211.00815), see [#320](https://github.com/wenet-e2e/wespeaker/pull/320).
* 2024.04.25: Add support for the gemini-dfresnet model, see [#291](https://github.com/wenet-e2e/wespeaker/pull/291).
* 2024.04.23: Support MNN inference engine in runtime, see [#310](https://github.com/wenet-e2e/wespeaker/pull/310).
* 2024.04.02: Release [Wespeaker document](http://wenet.org.cn/wespeaker) with detailed model-training tutorials, introduction of various runtime platforms, etc.
* 2024.03.04: Support the [eres2net-cn-common-200k](https://www.modelscope.cn/models/iic/speech_eres2net_sv_zh-cn_16k-common/summary) and [campplus-cn-common-200k](https://www.modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) of damo [#281](https://github.com/wenet-e2e/wespeaker/pull/281), check [python usage](https://github.com/wenet-e2e/wespeaker/blob/master/docs/python_package.md) for details.
* 2024.02.05: Support the ERes2Net [#272](https://github.com/wenet-e2e/wespeaker/pull/272) and Res2Net [#273](https://github.com/wenet-e2e/wespeaker/pull/273) models.
* 2023.11.13: Support CLI usage of wespeaker, check [python usage](https://github.com/wenet-e2e/wespeaker/blob/master/docs/python_package.md) for details.
* 2023.07.18: Support the kaldi-compatible PLDA and unsupervised adaptation, see [#186](https://github.com/wenet-e2e/wespeaker/pull/186).
* 2023.07.14: Support the [NIST SRE16 recipe](https://www.nist.gov/itl/iad/mig/speaker-recognition-evaluation-2016), see [#177](https://github.com/wenet-e2e/wespeaker/pull/177).
## Recipes
* [VoxCeleb](https://github.com/wenet-e2e/wespeaker/tree/master/examples/voxceleb): Speaker Verification recipe on the [VoxCeleb dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/)
* 🔥 UPDATE 2024.05.15: We support score calibration for Voxceleb and achieve better performance!
* 🔥 UPDATE 2023.07.10: We support self-supervised learning recipe on Voxceleb! Achieving **2.627%** (ECAPA_TDNN_GLOB_c1024) EER on vox1-O-clean test set without any labels.
* 🔥 UPDATE 2022.10.31: We support deep r-vector up to the 293-layer version! Achieving **0.447%/0.043** EER/mindcf on vox1-O-clean test set
* 🔥 UPDATE 2022.07.19: We apply the same setups as the CNCeleb recipe, and obtain SOTA performance considering the open-source systems
- EER/minDCF on vox1-O-clean test set are **0.723%/0.069** (ResNet34) and **0.728%/0.099** (ECAPA_TDNN_GLOB_c1024), after LM fine-tuning and AS-Norm
* [CNCeleb](https://github.com/wenet-e2e/wespeaker/tree/master/examples/cnceleb/v2): Speaker Verification recipe on the [CnCeleb dataset](http://cnceleb.org/)
* 🔥 UPDATE 2024.05.16: We support score calibration for Cnceleb and achieve better EER.
* 🔥 UPDATE 2022.10.31: 221-layer ResNet achieves **5.655%/0.330** EER/minDCF
* 🔥 UPDATE 2022.07.12: We migrate the winner system of CNSRC 2022 [report](https://aishell-cnsrc.oss-cn-hangzhou.aliyuncs.com/T082.pdf) [slides](https://aishell-cnsrc.oss-cn-hangzhou.aliyuncs.com/T082-ZhengyangChen.pdf)
- EER/minDCF reduction from 8.426%/0.487 to **6.492%/0.354** after large margin fine-tuning and AS-Norm
* [NIST SRE16](https://github.com/wenet-e2e/wespeaker/tree/master/examples/sre/v2): Speaker Verification recipe for the [2016 NIST Speaker Recognition Evaluation Plan](https://www.nist.gov/itl/iad/mig/speaker-recognition-evaluation-2016). Similar recipe can be found in [Kaldi](https://github.com/kaldi-asr/kaldi/tree/master/egs/sre16).
* 🔥 UPDATE 2023.07.14: We support NIST SRE16 recipe. After PLDA adaptation, we achieved 6.608%, 10.01%, and 2.974% EER on trial Pooled, Tagalog, and Cantonese, respectively.
* [VoxConverse](https://github.com/wenet-e2e/wespeaker/tree/master/examples/voxconverse): Diarization recipe on the [VoxConverse dataset](https://www.robots.ox.ac.uk/~vgg/data/voxconverse/)
## Discussion
For Chinese users, you can scan the QR code on the left to follow our offical account of `WeNet Community`.
We also created a WeChat group for better discussion and quicker response. Please scan the QR code on the right to join the chat group.
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## Citations
If you find wespeaker useful, please cite it as
```bibtex
@inproceedings{wang2023wespeaker,
title={Wespeaker: A research and production oriented speaker embedding learning toolkit},
author={Wang, Hongji and Liang, Chengdong and Wang, Shuai and Chen, Zhengyang and Zhang, Binbin and Xiang, Xu and Deng, Yanlei and Qian, Yanmin},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2023},
organization={IEEE}
}
```
## Looking for contributors
If you are interested to contribute, feel free to contact @wsstriving or @robin1001