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README.md
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---
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license: cc-by-4.0
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language:
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- en
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pipeline_tag: summarization
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tags:
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- speaker embedding
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- wespeaker
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- speaker modelling
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---
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Official model provided by [Wespeaker](https://github.com/wenet-e2e/wespeaker) project, ECAPA-TDNN based x-vector (After large margin finetune)
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The model is trained on VoxCeleb2 Dev dataset, containing 5994 speakers.
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## Model Sources
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/wenet-e2e/wespeaker
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- **Paper:** https://arxiv.org/pdf/2210.17016.pdf
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- **Demo:** https://huggingface.co/spaces/wenet/wespeaker_demo
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## Results on VoxCeleb
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| Model | Params | Flops | LM | AS-Norm | vox1-O-clean | vox1-E-clean | vox1-H-clean |
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|:------|:------:|:------|:--:|:-------:|:------------:|:------------:|:------------:|
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| ECAPA_TDNN_GLOB_c512-ASTP-emb192 | 6.19M | 1.04G | × | × | 1.069 | 1.209 | 2.310 |
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| | | | × | √ | 0.957 | 1.128 | 2.105 |
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| | | | √ | × | 0.878 | 1.072 | 2.007 |
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| | | | √ | √ | 0.782 | 1.005 | 1.824 |
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## Install Wespeaker
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``` sh
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pip install git+https://github.com/wenet-e2e/wespeaker.git
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```
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for development install:
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``` sh
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git clone https://github.com/wenet-e2e/wespeaker.git
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cd wespeaker
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pip install -e .
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```
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### Command line Usage
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``` sh
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$ wespeaker -p ecapa_tdnn512_download_dir --task embedding --audio_file audio.wav --output_file embedding.txt
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$ wespeaker -p ecapa_tdnn512_download_dir --task embedding_kaldi --wav_scp wav.scp --output_file /path/to/embedding
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$ wespeaker -p ecapa_tdnn512_download_dir --task similarity --audio_file audio.wav --audio_file2 audio2.wav
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$ wespeaker -p ecapa_tdnn512_download_dir --task diarization --audio_file audio.wav
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```
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### Python Programming Usage
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``` python
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import wespeaker
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model = wespeaker.load_model_local(ecapa_tdnn512_download_dir)
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# set_gpu to enable the cuda inference, number < 0 means using CPU
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model.set_gpu(0)
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# embedding/embedding_kaldi/similarity/diarization
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embedding = model.extract_embedding('audio.wav')
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utt_names, embeddings = model.extract_embedding_list('wav.scp')
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similarity = model.compute_similarity('audio1.wav', 'audio2.wav')
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diar_result = model.diarize('audio.wav')
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# register and recognize
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model.register('spk1', 'spk1_audio1.wav')
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model.register('spk2', 'spk2_audio1.wav')
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model.register('spk3', 'spk3_audio1.wav')
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result = model.recognize('spk1_audio2.wav')
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```
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## Citation
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```bibtex
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@article{desplanques2020ecapa,
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title={Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification},
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author={Desplanques, Brecht and Thienpondt, Jenthe and Demuynck, Kris},
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journal={arXiv preprint arXiv:2005.07143},
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year={2020}
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}
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@inproceedings{wang2023wespeaker,
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title={Wespeaker: A research and production oriented speaker embedding learning toolkit},
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author={Wang, Hongji and Liang, Chengdong and Wang, Shuai and Chen, Zhengyang and Zhang, Binbin and Xiang, Xu and Deng, Yanlei and Qian, Yanmin},
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booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
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pages={1--5},
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year={2023},
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organization={IEEE}
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}
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```
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