# Model Card for johbac/voice-embedder-base
## Model Description
Hey there! This is `voice-embedder-base`, a model that generates speaker embeddings—compact vectors that capture unique vocal characteristics for tasks like speaker verification, clustering, or voice similarity retrieval. It’s built by fine-tuning the `openai/whisper-base` encoder with a contrastive learning approach, using a mix of triplet loss and NT-Xent loss to make embeddings robust and speaker-discriminative. Trained on English speech from Common Voice 17 and VoxCeleb2 datasets, it shines in clean studio settings but holds its own in noisier environments too.
- **Developed by**: John Backsund
- **Model Type**: Speaker Embedding
- **Base Model**: `openai/whisper-base` encoder
- **Embedding Size**: 256
- **Training Data**: Common Voice 17 (en), VoxCeleb2 (en)
- **License**: MIT
## Intended Use
This model is great for:
- **Speaker Clustering**: Grouping audio samples by speaker (e.g., for diarization).
- **Speaker V…
