aufklarer/WeSpeaker-ResNet34-LM-MLX

Audio Classification·aufklarer· 51.0K· 2

mlx mit Audio Classification 6.6M params base_model:pyannote/wespeaker-voxceleb-resnet34-LMbase_model:finetune:pyannote/wespeaker-voxceleb-resnet34-LM

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

Open in MLForge Sign up free Desktop app Source ↗

# pull & run locally
pip install mlforge-sdk && mlforge pull aufklarer/WeSpeaker-ResNet34-LM-MLX

Model details

Task

Audio Classification

Provider

aufklarer

Framework

mlx

Parameters

6.6M

Size

25 MB

License

mit

Downloads

51.0K

Likes

Updated

2026-04-12

About aufklarer/WeSpeaker-ResNet34-LM-MLX

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

Related Audio Classification

C laion/clap-htsat-fused Audio Classification ·153.6M params 14.7M 107 🤗 HF W alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech Audio Classification ·315.7M params 587.0K 48 🤗 HF W audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim Audio Classification ·165.3M params 584.0K 171 🤗 HF A MIT/ast-finetuned-audioset-10-10-0.4593 Audio Classification ·86.6M params 529.0K 360 🤗 HF M m-a-p/MERT-v1-330M Audio Classification 466.6K 90 🤗 HF W audeering/wav2vec2-large-robust-24-ft-age-gender Audio Classification ·317.5M params 444.1K 56 🤗 HF

Browse all Audio Classification models →