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WeSpeaker-ResNet34-LM-CoreML
aufklarer/WeSpeaker-ResNet34-LM-CoreML
WeSpeaker-ResNet34-LM-CoreML is a audio classification model from aufklarer. Use it for the audio classification task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
CoreML conversion of WeSpeaker ResNet34-LM for Apple Neural Engine.
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At a glance
- Task
- Audio Classification
- License
- mit
- Model type
- wespeaker-resnet34-lm-coreml
- Access
- Public
- Created
- Feb 28, 2026
- Updated
- Apr 12, 2026
- SHA
- b358f33f
Base models
Model card
From the Hugging Face model README
WeSpeaker-ResNet34-LM — CoreML
CoreML conversion of WeSpeaker ResNet34-LM for Apple Neural Engine.
Produces 256-dimensional L2-normalized speaker embeddings from audio.
Model Details
| Detail | Value |
|---|---|
| Architecture | ResNet34 with statistics pooling |
| Parameters | ~6.6M |
| Input | 80-bin log-mel spectrogram (16kHz) |
| Output | 256-dim L2-normalized speaker embedding |
| BatchNorm | Fused into Conv2d at conversion time |
Usage
let model = try await WeSpeakerModel.fromPretrained(backend: .coreML)
let embedding = model.embed(audio: samples, sampleRate: 16000)
let similarity = WeSpeakerModel.cosineSimilarity(embeddingA, embeddingB)
Variants
| Variant | Backend | Model ID |
|---|---|---|
| MLX | GPU | aufklarer/WeSpeaker-ResNet34-LM-MLX |
| CoreML | Neural Engine | aufklarer/WeSpeaker-ResNet34-LM-CoreML |
Links
- Swift library: soniqo/speech-swift
- Original model: pyannote/wespeaker-voxceleb-resnet34-LM
- Guide: soniqo.audio/guides/embed-speaker
- Docs: soniqo.audio
- GitHub: soniqo/speech-swift
- Task
- Audio Classification
- Type
- wespeaker-resnet34-lm-coreml
- License
- mit
- Created
- Feb 28, 2026
- Updated
- Apr 12, 2026