From the model card
Fields below are copied from the tags and counters on the HuggingFace repository speechbrain/spkrec-ecapa-voxceleb at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- speechbrain
- Library
- SpeechBrain
- Framework tags
- PyTorch
- License tag
apache-2.0— read the license file in the repo before relying on it- Language tags
- English (en)
- Papers cited
- arXiv:2106.04624
- Datasets declared
- voxceleb
- Downloads (HF counter at last fetch)
- 1,886,337
- Likes (HF counter at last fetch)
- 342
- Model card
- https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb
Use cases
- Speaker verification — confirming a claimed speaker identity
- Speaker identification — matching an utterance to a known speaker
- Building speaker diarization systems using embedding clustering
- Extracting speaker representations for downstream tasks
Pros
- ECAPA-TDNN architecture outperforms older TDNN/x-vector baselines
- Trained on VoxCeleb which covers diverse speaker demographics
- Apache-2.0 licensed
- Integrates directly into SpeechBrain pipeline framework
Cons
- Requires SpeechBrain as a dependency — heavier than standalone PyTorch
- Performance degrades significantly with short utterances under 3 seconds
- Channel and noise conditions from training data may not match deployment
- No direct output for diarization — requires external clustering step
Tags
speechbrainembeddingsSpeakerVerificationIdentificationpytorchECAPATDNNendataset:voxcelebarxiv:2106.04624license:apache-2.0region:us