Create README.md

README.md

@@ -0,0 +1,100 @@
+---
+license: cc-by-4.0
+language:
+- en
+pipeline_tag: summarization
+tags:
+- speaker embedding
+- wespeaker
+- speaker modelling
+---
+
+
+Official model provided by Wespeaker project, ResNet34 based r-vector (After large margin finetune)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63b7e3814705f0ed5d7a0b00/wXgoQOaS8TLACe85mDqcj.png)
+
+## Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/wenet-e2e/wespeaker
+- **Paper:** https://arxiv.org/pdf/2210.17016.pdf
+- **Demo:** https://huggingface.co/spaces/wenet/wespeaker_demo
+
+## Uses
+
+## Results on VoxCeleb
+| Model | Params | Flops | LM | AS-Norm | vox1-O-clean | vox1-E-clean | vox1-H-clean |
+|:------|:------:|:------|:--:|:-------:|:------------:|:------------:|:------------:|
+| ResNet34-TSTP-emb256 | 6.63M | 4.55G | × | × | 0.867 | 1.049 | 1.959 |
+|                      |       |       | × | √ | 0.787 | 0.964 | 1.726 |
+|                      |       |       | √ | × | 0.797 | 0.937 | 1.695 |
+|                      |       |       | √ | √ | 0.723 | 0.867 | 1.532 |
+
+## Install Wespeaker
+
+``` sh
+pip install git+https://github.com/wenet-e2e/wespeaker.git
+```
+
+for development install:
+
+``` sh
+git clone https://github.com/wenet-e2e/wespeaker.git
+cd wespeaker
+pip install -e .
+```
+
+
+### Command line Usage
+
+``` sh
+$ wespeaker -p ResNet34_download_dir --task embedding --audio_file audio.wav --output_file embedding.txt
+$ wespeaker -p ResNet34_download_dir --task embedding_kaldi --wav_scp wav.scp --output_file /path/to/embedding
+$ wespeaker -p ResNet34_download_dir --task similarity --audio_file audio.wav --audio_file2 audio2.wav
+$ wespeaker -p ResNet34_download_dir --task diarization --audio_file audio.wav
+```
+
+### Python Programming Usage
+
+``` python
+import wespeaker
+
+model = wespeaker.load_model_local(ResNet34_download_dir)
+# set_gpu to enable the cuda inference, number < 0 means using CPU
+model.set_gpu(0)
+
+# embedding/embedding_kaldi/similarity/diarization
+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')
+
+# register and recognize
+model.register('spk1', 'spk1_audio1.wav')
+model.register('spk2', 'spk2_audio1.wav')
+model.register('spk3', 'spk3_audio1.wav')
+result = model.recognize('spk1_audio2.wav')
+```
+
+## Citation
+
+
+```bibtex
+@article{zeinali2019rvector,
+  title={But system description to voxceleb speaker recognition challenge 2019},
+  author={Zeinali, Hossein and Wang, Shuai and Silnova, Anna and Mat{\v{e}}jka, Pavel and Plchot, Old{\v{r}}ich},
+  journal={arXiv preprint arXiv:1910.12592},
+  year={2019}
+}
+
+@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}
+}
+```