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wespeaker-resnet34
Speaker recognition with WeSpeaker's ResNet34 trained on VoxCeleb, exported to ONNX. 256-d embeddings, CPU-friendly — avoids the PyTorch runtime entirely (onnxruntime only). APACHE 2.0. Pair with the `speaker-recognition` backend's OnnxDirectEngine. Use when ECAPA-TDNN's torch dependency is undesirable (small images, edge deployments).

Repository: localaiLicense: cc-by-4.0

parakeet-cpp-nemotron-3-diarization-speakers
Nemotron-3-Diarization (Sortformer) with WeSpeaker ResNet34 speaker identification, for the parakeet-cpp backend. Speakers you register with /v1/voice/register (using the voice-detect-wespeaker-resnet34 model) come back by name in /v1/audio/diarization, next to the SPEAKER_NN label. Speakers that are not registered keep only their SPEAKER_NN label. The diarization model is OpenMDW-1.1, the speaker model is CC-BY-4.0. Naming was measured on one two-voice fixture only; check the threshold on your own audio.

Repository: localaiLicense: openmdw-1.1