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:

Input: [B, T, 80, 1] log-mel spectrogram (80 fbank, 16kHz)
  │
  ├─ Conv2d(1→32, k=3, p=1) + ReLU
  ├─ Layer1: 3× BasicBlock(32→32)
  ├─ Layer2: 4× BasicBlock(32→64, stride=2)
  ├─ Layer3: 6× BasicBlock(64→128, stride=2)
  ├─ Layer4: 3× BasicBlock(128→256, stride=2)
  │
  ├─ Statistics Pooling: mean + std → [B, 5120]
  ├─ Linear(5120→256) → L2 normalize
  │
  Output: [B, 256] speaker embedding

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] → [O, H, W, I] for MLX channels-last
Rename: strip resnet. prefix, seg_1 → embedding
Drop num_batches_tracked keys

Weight Mapping

PyTorch Key	MLX Key	Shape
`resnet.conv1.weight` + `resnet.bn1.*`	`conv1.weight`	[32, 3, 3, 1]
`resnet.layer{L}.{B}.conv{1,2}.weight` + `bn{1,2}.*`	`layer{L}.{B}.conv{1,2}.weight`	[O, 3, 3, I]
`resnet.layer{L}.0.shortcut.0.weight` + `shortcut.1.*`	`layer{L}.0.shortcut.weight`	[O, 1, 1, I]
`resnet.seg_1.weight`	`embedding.weight`	[256, 5120]
`resnet.seg_1.bias`	`embedding.bias`	[256]