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README.md

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+---
+tags:
+  - pyannote
+  - pyannote-audio
+  - pyannote-audio-model
+  - wespeaker
+  - audio
+  - voice
+  - speech
+  - speaker
+  - speaker-recognition
+  - speaker-verification
+  - speaker-identification
+  - speaker-embedding
+datasets:
+  - voxceleb
+license: cc-by-4.0
+inference: false
+---
+
+Using this open-source model in production?  
+Make the most of it thanks to our [consulting services](https://herve.niderb.fr/consulting.html).
+
+# 🎹 `pyannote` wrapper around `wespeaker-voxceleb-resnet34-LM`
+
+This model requires `pyannote.audio` version 3.1 or higher (currently in development).
+
+This is a wrapper around [WeSpeaker](https://github.com/wenet-e2e/wespeaker) `wespeaker-voxceleb-resnet34-LM` pretrained speaker embedding model, for use in `pyannote.audio`.
+
+## Basic usage
+
+```python
+# instantiate pretrained model
+from pyannote.audio import Model
+model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM")
+```
+
+```python
+from pyannote.audio import Inference
+inference = Inference(model, window="whole")
+embedding1 = inference("speaker1.wav")
+embedding2 = inference("speaker2.wav")
+# `embeddingX` is (1 x D) numpy array extracted from the file as a whole.
+
+from scipy.spatial.distance import cdist
+distance = cdist(embedding1, embedding2, metric="cosine")[0,0]
+# `distance` is a `float` describing how dissimilar speakers 1 and 2 are.
+```
+
+## Advanced usage
+
+### Running on GPU
+
+```python
+import torch
+inference.to(torch.device("cuda"))
+embedding = inference("audio.wav")
+```
+
+### Extract embedding from an excerpt
+
+```python
+from pyannote.audio import Inference
+from pyannote.core import Segment
+inference = Inference(model, window="whole")
+excerpt = Segment(13.37, 19.81)
+embedding = inference.crop("audio.wav", excerpt)
+# `embedding` is (1 x D) numpy array extracted from the file excerpt.
+```
+
+### Extract embeddings using a sliding window
+
+```python
+from pyannote.audio import Inference
+inference = Inference(model, window="sliding",
+                      duration=3.0, step=1.0)
+embeddings = inference("audio.wav")
+# `embeddings` is a (N x D) pyannote.core.SlidingWindowFeature
+# `embeddings[i]` is the embedding of the ith position of the
+# sliding window, i.e. from [i * step, i * step + duration].
+```
+
+## License
+
+According to [this page](https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md):
+
+> The pretrained model in WeNet follows the license of it's corresponding dataset. For example, the pretrained model on VoxCeleb follows Creative Commons Attribution 4.0 International License., since it is used as license of the VoxCeleb dataset, see https://mm.kaist.ac.kr/datasets/voxceleb/.
+
+## Citation
+
+```bibtex
+@inproceedings{Wang2023,
+  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={ICASSP 2023, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={1--5},
+  year={2023},
+  organization={IEEE}
+}
+```
+
+```bibtex
+@inproceedings{Bredin23,
+  author={Hervé Bredin},
+  title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}},
+  year=2023,
+  booktitle={Proc. INTERSPEECH 2023},
+  pages={1983--1987},
+  doi={10.21437/Interspeech.2023-105}
+}
+```

pytorch_model.bin

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wespeaker.pt

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