Georgehappy1/wespeaker

WeSpeaker

WeSpeaker mainly focuses on speaker embedding learning, with application to the speaker verification task. We support online feature extraction or loading pre-extracted features in kaldi-format.

Installation

• Clone this repo

git clone https://github.com/wenet-e2e/wespeaker.git

• Create conda env: pytorch version >= 1.10.0 is required !!!

conda create -n wespeaker python=3.9
conda activate wespeaker
conda install pytorch=1.10.1 torchaudio=0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt

Recipes

• VoxCeleb
• CNCeleb

Support List:

• Model (SOTA Models)
  • Standard X-vector
  • ResNet
  • ECAPA_TDNN
• Pooling Functions
  • TAP(mean) / TSDP(std) / TSTP(mean+std)
  • Attentive Statistics Pooling (ASTP)
  • Learnable Dictionary Encoding (LDE)
• Criteria
  • Softmax
  • Sphere (A-Softmax)
  • Add_Margin (AM-Softmax)
  • Arc_Margin (AAM-Softmax)
• Scoring
  • Cosine
  • PLDA
  • Score Normalization (AS-Norm)
• Metric
  • EER
  • minDCF
• Online Augmentation
  • Noise && RIR
  • Speed Perturb
  • Specaug
• Literature
  • Awesome Speaker Papers

Discussion

For Chinese users, you can scan the QR code on the left to follow our offical account of WeNet Community. We also created a WeChat group for better discussion and quicker response. Please scan the QR code on the right to join the chat group.

Looking for contributors

If you are interested to contribute, feel free to contact @wsstriving or @robin1001