WeSpeaker ResNet34 LM MLX EU AI Act Compliance Profile

WeSpeaker ResNet34-LM — MLX

MLX-compatible weights for WeSpeaker ResNet34-LM, converted from the pyannote speaker embedding model with BatchNorm fused into Conv2d.

Model

WeSpeaker ResNet34-LM is a speaker embedding model (~6.6M params) that produces 256-dimensional L2-normalized speaker embeddings from audio. Trained on VoxCeleb for speaker verification and diarization.

Architecture:

BatchNorm is fused into Conv2d at conversion time — no BN layers in the MLX model.

Usage (Swift / MLX)

import SpeechVAD

// Speaker embedding
let model = try await WeSpeakerModel.fromPretrained()
let embedding = model.embed(audio: samples, sampleRate: 16000)
// embedding: [Float] of length 256, L2-normalized

// Compare speakers
let similarity = WeSpeakerModel.cosineSimilarity(embeddingA, embeddingB)

// Full speaker diarization pipeline
let pipeline = try await DiarizationPipeline.fromPretrained()
let result = pipeline.diarize(audio: samples, sampleRate: 16000)
for seg in result.segments {
    print("Speaker \(seg.speakerId): \(seg.startTime)s - \(seg.endTime)s")
}

Part of speech-swift.

Conversion

python3 scripts/convert_wespeaker.py --upload

Converts the original pyannote/wespeaker-voxceleb-resnet34-LM checkpoint using a custom unpickler (no pyannote.audio dependency required). Key transformations:

• Fuse BatchNorm into Conv2d: w_fused = w × γ/√(σ²+ε), b_fused = β − μ×γ/√(σ²+ε)
• Transpose Conv2d weights: `[O, I, H, W

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Tags

Library

Base Model