Roadmap | Awesome Papers | Runtime (x86_gpu) | Pretrained Models | Huggingface Demo
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.
- 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
- VoxCeleb: Speaker Verification recipe on the VoxCeleb dataset
- 🔥 UPDATE 2022.7.19: We apply the same setups as the CNCeleb recipe, and obtain SOTA performance considering the open-source systems
- 🔥 EER/minDCF on vox1-O-clean test set are 0.723%/0.069 (ResNet34) and 0.728%/0.099 (ECAPA_TDNN_GLOB_c1024), after LM fine-tuning and AS-Norm
- CNCeleb: Speaker Verification recipe on the CnCeleb dataset
- VoxConverse: 🔥 UPDATE 2022.7.2: Diarization recipe on the VoxConverse dataset
- Model (SOTA Models)
- Pooling Functions
- TAP(mean) / TSDP(std) / TSTP(mean+std)
- Comparison of mean/std pooling can be found in shuai_iscslp, anna_arxiv
- Attentive Statistics Pooling (ASTP)
- mainly for ECAPA_TDNN
- TAP(mean) / TSDP(std) / TSTP(mean+std)
- Criteria
- Scoring
- Cosine
- PLDA
- Score Normalization (AS-Norm)
- Metric
- EER
- minDCF
- Online Augmentation
- Noise && RIR
- Speed Perturb
- SpecAug
- Literature
For Chinese users, you can scan the QR code on the left to follow our offical account of WeNet Community
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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.
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If you are interested to contribute, feel free to contact @wsstriving or @robin1001