https://github.com/modelscope/3d-speaker

A Repository for Single- and Multi-modal Speaker Verification, Speaker Recognition and Speaker Diarization
https://github.com/modelscope/3d-speaker
3d-speaker campplus cnceleb eres2net language-identification modelscope rdino speaker-diarization speaker-verification voxceleb
Last synced: 11 months ago
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A Repository for Single- and Multi-modal Speaker Verification, Speaker Recognition and Speaker Diarization
Host: GitHub
URL: https://github.com/modelscope/3d-speaker
Owner: modelscope
License: apache-2.0
Created: 2023-03-06T04:05:27.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-10-29T09:36:22.000Z (over 1 year ago)
Last Synced: 2024-10-29T11:44:02.288Z (over 1 year ago)
Topics: 3d-speaker, campplus, cnceleb, eres2net, language-identification, modelscope, rdino, speaker-diarization, speaker-verification, voxceleb
Language: Python
Homepage:
Size: 3.11 MB
Stars: 1,187
Watchers: 17
Forks: 101
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

          


    


    

    




    



    

![license](https://img.shields.io/github/license/modelscope/modelscope.svg)







    



    

3D-Speaker is an open-source toolkit for single- and multi-modal speaker verification, speaker recognition, and speaker diarization. All pretrained models are accessible on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=speaker-verification&type=audio). Furthermore, we present a large-scale speech corpus also called [3D-Speaker-Dataset](https://3dspeaker.github.io/) to facilitate the research of speech representation disentanglement.

## Benchmark

The EER results on VoxCeleb, CNCeleb and 3D-Speaker datasets for fully-supervised speaker verification.

| Model | Params | VoxCeleb1-O | CNCeleb | 3D-Speaker |

|:-----:|:------:| :------:|:------:|:------:|

| Res2Net | 4.03 M | 1.56% | 7.96% | 8.03% |

| ResNet34 | 6.34 M | 1.05% | 6.92% | 7.29% |

| ECAPA-TDNN | 20.8 M | 0.86% | 8.01% | 8.87% |

| ERes2Net-base | 6.61 M | 0.84% | 6.69% | 7.21% |

| CAM++ | 7.2 M | 0.65% | 6.78% | 7.75% |

| ERes2NetV2 | 17.8M | 0.61% | **6.14%** | 6.52% |

| ERes2Net-large | 22.46 M | **0.52%** | 6.17% | **6.34%** |

The DER results on public and internal multi-speaker datasets for speaker diarization.

| Test | 3D-Speaker | [pyannote.audio](https://github.com/pyannote/pyannote-audio) | [DiariZen_WavLM](https://github.com/BUTSpeechFIT/DiariZen) | 

|:-----:|:------:|:------:|:------:|

|[Aishell-4](https://arxiv.org/abs/2104.03603)|**10.30%**|12.2%|11.7%|

|[Alimeeting](https://www.openslr.org/119/)|19.73%|24.4%|**17.6%**|

|[AMI_SDM](https://groups.inf.ed.ac.uk/ami/corpus/)|21.76%|22.4%|**15.4%**|

|[VoxConverse](https://github.com/joonson/voxconverse)|11.75%|**11.3%**|28.39%|

|Meeting-CN_ZH-1|**18.91%**|22.37%|32.66%|

|Meeting-CN_ZH-2|**12.78%**|17.86%|18%|

## Quickstart

### Install 3D-Speaker

``` sh

git clone https://github.com/modelscope/3D-Speaker.git && cd 3D-Speaker

conda create -n 3D-Speaker python=3.8

conda activate 3D-Speaker

pip install -r requirements.txt

```

### Running experiments

``` sh

# Speaker verification: ERes2NetV2 on 3D-Speaker dataset

cd egs/3dspeaker/sv-eres2netv2/

bash run.sh

# Speaker verification: CAM++ on 3D-Speaker dataset

cd egs/3dspeaker/sv-cam++/

bash run.sh

# Speaker verification: ECAPA-TDNN on 3D-Speaker dataset

cd egs/3dspeaker/sv-ecapa/

bash run.sh

# Self-supervised speaker verification: SDPN on VoxCeleb dataset

cd egs/voxceleb/sv-sdpn/

bash run.sh

# Audio and multimodal Speaker diarization:

cd egs/3dspeaker/speaker-diarization/

bash run_audio.sh

bash run_video.sh

# Language identification

cd egs/3dspeaker/language-idenitfication

bash run.sh

```

### Inference using pretrained models from Modelscope

All pretrained models are released on [Modelscope](https://www.modelscope.cn/models?page=1&tasks=speaker-verification&type=audio).

``` sh

# Install modelscope

pip install modelscope

# ERes2Net trained on 200k labeled speakers

model_id=iic/speech_eres2net_sv_zh-cn_16k-common

# ERes2NetV2 trained on 200k labeled speakers

model_id=iic/speech_eres2netv2_sv_zh-cn_16k-common

# CAM++ trained on 200k labeled speakers

model_id=iic/speech_campplus_sv_zh-cn_16k-common

# Run CAM++ or ERes2Net inference

python speakerlab/bin/infer_sv.py --model_id $model_id

# Run batch inference

python speakerlab/bin/infer_sv_batch.py --model_id $model_id --wavs $wav_list

# SDPN trained on VoxCeleb

model_id=iic/speech_sdpn_ecapa_tdnn_sv_en_voxceleb_16k

# Run SDPN inference

python speakerlab/bin/infer_sv_ssl.py --model_id $model_id

# Run diarization inference

python speakerlab/bin/infer_diarization.py --wav [wav_list OR wav_path] --out_dir $out_dir

# Enable overlap detection

python speakerlab/bin/infer_diarization.py --wav [wav_list OR wav_path] --out_dir $out_dir --include_overlap --hf_access_token $hf_access_token

```

## Overview of Content

- **Supervised Speaker Verification**

  - [CAM++](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-cam%2B%2B), [ERes2Net](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-eres2net), [ERes2NetV2](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-eres2netv2), [ECAPA-TDNN](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-ecapa), [ResNet](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-resnet) and [Res2Net](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-res2net) training recipes on [3D-Speaker](https://3dspeaker.github.io/).

  - [CAM++](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-cam%2B%2B), [ERes2Net](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-eres2net), [ERes2NetV2](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-eres2netv2), [ECAPA-TDNN](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-ecapa), [ResNet](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-resnet) and [Res2Net](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-res2net) training recipes on [VoxCeleb](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/). 

  - [CAM++](https://github.com/modelscope/3D-Speaker/tree/main/egs/cnceleb/sv-cam%2B%2B), [ERes2Net](https://github.com/modelscope/3D-Speaker/tree/main/egs/cnceleb/sv-eres2net), [ERes2NetV2](https://github.com/modelscope/3D-Speaker/tree/main/egs/cnceleb/sv-eres2netv2), [ECAPA-TDNN](https://github.com/modelscope/3D-Speaker/tree/main/egs/cnceleb/sv-ecapa), [ResNet](https://github.com/modelscope/3D-Speaker/tree/main/egs/cnceleb/sv-resnet) and [Res2Net](https://github.com/modelscope/3D-Speaker/tree/main/egs/cnceleb/sv-res2net) training recipes on [CN-Celeb](http://cnceleb.org/).

- **Self-supervised Speaker Verification**

  - [RDINO](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-rdino) and [SDPN](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-sdpn) training recipes on [VoxCeleb](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/)

    

  - [RDINO](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-rdino) training recipes on [3D-Speaker](https://3dspeaker.github.io/).

  - [RDINO](https://github.com/modelscope/3D-Speaker/tree/main/egs/cnceleb/sv-rdino) training recipes on [CN-Celeb](http://cnceleb.org/).

- **Speaker Diarization**

  - [Speaker diarization](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/speaker-diarization) inference recipes which comprise multiple modules, including overlap detection[optional], voice activity detection, speech segmentation, speaker embedding extraction, and speaker clustering. 

- **Language Identification**

  - [Language identification](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/language-identification) training recipes on [3D-Speaker](https://3dspeaker.github.io/).

- **3D-Speaker Dataset**

  - Dataset introduction and download address: [3D-Speaker](https://3dspeaker.github.io/) 


  - Related paper address: [3D-Speaker](https://arxiv.org/pdf/2306.15354.pdf)

## What‘s new :fire:

- [2024.12] Update [diarization](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/speaker-diarization) recipes and add results on multiple diarization benchmarks.

- [2024.8] Releasing [ERes2NetV2](https://modelscope.cn/models/iic/speech_eres2netv2_sv_zh-cn_16k-common) and [ERes2NetV2_w24s4ep4](https://modelscope.cn/models/iic/speech_eres2netv2w24s4ep4_sv_zh-cn_16k-common) pretrained models trained on 200k-speaker datasets.

- [2024.5] Releasing [SDPN](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-sdpn) model and [X-vector](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-xvector) model training and inference recipes for VoxCeleb.

- [2024.5] Releasing [visual module](https://github.com/modelscope/3D-Speaker/tree/main/egs/ava-asd/talknet) and [semantic module](https://github.com/modelscope/3D-Speaker/tree/main/egs/semantic_speaker/bert) training recipes.

- [2024.4] Releasing [ONNX Runtime](https://github.com/modelscope/3D-Speaker/tree/main/runtime/onnxruntime) and the relevant scripts for inference.

- [2024.4] Releasing [ERes2NetV2](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-eres2netv2) model with lower parameters and faster inference speed on VoxCeleb datasets.

- [2024.2] Releasing [language identification](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/language-identification) integrating phonetic information recipes for more higher recognition accuracy.

- [2024.2] Releasing [multimodal diarization](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/speaker-diarization) recipes which fuses audio and video image input to produce more accurate results.

- [2024.1] Releasing [ResNet34](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-resnet) and [Res2Net](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-res2net) model training and inference recipes for 3D-Speaker, VoxCeleb and CN-Celeb datasets.

- [2024.1] Releasing [large-margin finetune recipes](https://github.com/modelscope/3D-Speaker/blob/main/egs/voxceleb/sv-eres2net/run.sh) in speaker verification and adding [diarization recipes](https://github.com/modelscope/3D-Speaker/blob/main/egs/3dspeaker/speaker-diarization/run.sh). 

- [2023.11] [ERes2Net-base](https://modelscope.cn/models/damo/speech_eres2net_base_200k_sv_zh-cn_16k-common/summary) pretrained model released, trained on a Mandarin dataset of 200k labeled speakers.

- [2023.10] Releasing [ECAPA model](https://github.com/modelscope/3D-Speaker/tree/main/egs/voxceleb/sv-ecapa) training and inference recipes for three datasets.

- [2023.9] Releasing [RDINO](https://github.com/modelscope/3D-Speaker/tree/main/egs/cnceleb/sv-rdino) model training and inference recipes for [CN-Celeb](http://cnceleb.org/).

- [2023.8] Releasing [CAM++](https://modelscope.cn/models/damo/speech_campplus_sv_cn_cnceleb_16k/summary), [ERes2Net-Base](https://modelscope.cn/models/damo/speech_eres2net_base_sv_zh-cn_cnceleb_16k/summary) and [ERes2Net-Large](https://modelscope.cn/models/damo/speech_eres2net_large_sv_zh-cn_cnceleb_16k/summary) benchmarks in [CN-Celeb](http://cnceleb.org/).

- [2023.8] Releasing [ERes2Net](https://modelscope.cn/models/damo/speech_eres2net_base_lre_en-cn_16k/summary) annd [CAM++](https://modelscope.cn/models/damo/speech_campplus_lre_en-cn_16k/summary) in language identification for Mandarin and English. 

- [2023.7] Releasing [CAM++](https://modelscope.cn/models/damo/speech_campplus_sv_zh-cn_3dspeaker_16k/summary), [ERes2Net-Base](https://modelscope.cn/models/damo/speech_eres2net_base_sv_zh-cn_3dspeaker_16k/summary), [ERes2Net-Large](https://modelscope.cn/models/damo/speech_eres2net_large_sv_zh-cn_3dspeaker_16k/summary) pretrained models trained on [3D-Speaker](https://3dspeaker.github.io/).

- [2023.7] Releasing [Dialogue Detection](https://modelscope.cn/models/damo/speech_bert_dialogue-detetction_speaker-diarization_chinese/summary) and [Semantic Speaker Change Detection](https://modelscope.cn/models/damo/speech_bert_semantic-spk-turn-detection-punc_speaker-diarization_chinese/summary) in speaker diarization.

- [2023.7] Releasing [CAM++](https://modelscope.cn/models/damo/speech_campplus_lre_en-cn_16k/summary) in language identification for Mandarin and English.

- [2023.6] Releasing [3D-Speaker](https://3dspeaker.github.io/) dataset and its corresponding benchmarks including [ERes2Net](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-eres2net), [CAM++](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-cam%2B%2B) and [RDINO](https://github.com/modelscope/3D-Speaker/tree/main/egs/3dspeaker/sv-rdino).

- [2023.5] [ERes2Net](https://modelscope.cn/models/damo/speech_eres2net_sv_zh-cn_16k-common/summary) and [CAM++](https://www.modelscope.cn/models/damo/speech_campplus_sv_zh-cn_16k-common/summary) pretrained model released, trained on a Mandarin dataset of 200k labeled speakers.

## Contact

If you have any comment or question about 3D-Speaker, please contact us by

- email: {yfchen97, wanghuii}@mail.ustc.edu.cn, {dengchong.d, zsq174630, shuli.cly}@alibaba-inc.com

## License

3D-Speaker is released under the [Apache License 2.0](LICENSE).

## Acknowledge

3D-Speaker contains third-party components and code modified from some open-source repos, including: 


[Speechbrain](https://github.com/speechbrain/speechbrain), [Wespeaker](https://github.com/wenet-e2e/wespeaker), [D-TDNN](https://github.com/yuyq96/D-TDNN), [DINO](https://github.com/facebookresearch/dino), [Vicreg](https://github.com/facebookresearch/vicreg), [TalkNet-ASD

](https://github.com/TaoRuijie/TalkNet-ASD), [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB), [pyannote.audio](https://github.com/pyannote/pyannote-audio)

## Citations

If you find this repository useful, please consider giving a star :star: and citation :t-rex::

```BibTeX

@article{chen20243d,

  title={3D-Speaker-Toolkit: An Open Source Toolkit for Multi-modal Speaker Verification and Diarization},

  author={Chen, Yafeng and Zheng, Siqi and Wang, Hui and Cheng, Luyao and others},

  booktitle={ICASSP},

  year={2025}

}

```
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