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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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