aufklarer/WeSpeaker-ResNet34-LM-CoreML

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

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• Guide: soniqo.audio/guides/embed-speaker
• Docs: soniqo.audio
• GitHub: soniqo/speech-swift