pyannote / wespeaker-voxceleb-resnet34-LM

huggingface.co
Total runs: 14.8M
24-hour runs: 0
7-day runs: 33.1K
30-day runs: 3.6M
Model's Last Updated: Mai 10 2024

Introduction of wespeaker-voxceleb-resnet34-LM

Model Details of wespeaker-voxceleb-resnet34-LM

Using this open-source model in production?
Consider switching to pyannoteAI for better and faster options.

🎹 Wrapper around wespeaker-voxceleb-resnet34-LM

This model requires pyannote.audio version 3.1 or higher.

This is a wrapper around WeSpeaker wespeaker-voxceleb-resnet34-LM pretrained speaker embedding model, for use in pyannote.audio .

Basic usage
# instantiate pretrained model
from pyannote.audio import Model
model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM")
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
import torch
inference.to(torch.device("cuda"))
embedding = inference("audio.wav")
Extract embedding from an excerpt
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
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 :

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
@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}
}
@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}
}

Runs of pyannote wespeaker-voxceleb-resnet34-LM on huggingface.co

14.8M
Total runs
0
24-hour runs
296.4K
3-day runs
33.1K
7-day runs
3.6M
30-day runs

More Information About wespeaker-voxceleb-resnet34-LM huggingface.co Model

More wespeaker-voxceleb-resnet34-LM license Visit here:

https://choosealicense.com/licenses/cc-by-4.0

wespeaker-voxceleb-resnet34-LM huggingface.co

wespeaker-voxceleb-resnet34-LM huggingface.co is an AI model on huggingface.co that provides wespeaker-voxceleb-resnet34-LM's model effect (), which can be used instantly with this pyannote wespeaker-voxceleb-resnet34-LM model. huggingface.co supports a free trial of the wespeaker-voxceleb-resnet34-LM model, and also provides paid use of the wespeaker-voxceleb-resnet34-LM. Support call wespeaker-voxceleb-resnet34-LM model through api, including Node.js, Python, http.

wespeaker-voxceleb-resnet34-LM huggingface.co Url

https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM

pyannote wespeaker-voxceleb-resnet34-LM online free

wespeaker-voxceleb-resnet34-LM huggingface.co is an online trial and call api platform, which integrates wespeaker-voxceleb-resnet34-LM's modeling effects, including api services, and provides a free online trial of wespeaker-voxceleb-resnet34-LM, you can try wespeaker-voxceleb-resnet34-LM online for free by clicking the link below.

pyannote wespeaker-voxceleb-resnet34-LM online free url in huggingface.co:

https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM

wespeaker-voxceleb-resnet34-LM install

wespeaker-voxceleb-resnet34-LM is an open source model from GitHub that offers a free installation service, and any user can find wespeaker-voxceleb-resnet34-LM on GitHub to install. At the same time, huggingface.co provides the effect of wespeaker-voxceleb-resnet34-LM install, users can directly use wespeaker-voxceleb-resnet34-LM installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

wespeaker-voxceleb-resnet34-LM install url in huggingface.co:

https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM

Url of wespeaker-voxceleb-resnet34-LM

wespeaker-voxceleb-resnet34-LM huggingface.co Url

Provider of wespeaker-voxceleb-resnet34-LM huggingface.co

pyannote
ORGANIZATIONS

Other API from pyannote

huggingface.co

Total runs: 318.5K
Run Growth: -55.3K
Growth Rate: -17.63%
Updated: Mai 10 2024
huggingface.co

Total runs: 100.8K
Run Growth: 86.7K
Growth Rate: 85.75%
Updated: Novembre 15 2022