lang-id-voxlingua107-ecapa-mlx – AI Model by beshkenadze | AlphaNeural AI

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beshkenadze/
lang-id-voxlingua107-ecapa-mlx

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audio-classification

language-identification

speechbrain/lang-id-voxlingua107-ecapa

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ECAPA-TDNN VoxLingua107 — MLX

ECAPA-TDNN language identification model converted to MLX safetensors format for inference on Apple Silicon (Metal GPU).

Identifies 107 languages from audio. Original model: speechbrain/lang-id-voxlingua107-ecapa.

Performance (M1, 10s audio)

Framework	Latency	Russian	English
Python MLX (with `mx.compile()`)	15.7ms	99.6%	99.9%
Swift MLX (with `compile()` + GPU mel)	14.8ms	99.5%	99.9%
CoreML GPU	17ms	99.7%	98.6%

Usage

python

# Clone the benchmark repo
git clone https://github.com/beshkenadze/lid-bench
cd lid-bench/mlx

# Setup
uv venv && uv pip install mlx numpy soundfile safetensors

# Run
python ecapa_tdnn_lid.py path/to/audio.wav --benchmark

Full implementation: github.com/beshkenadze/lid-bench

Model Details

Architecture: ECAPA-TDNN (channels=1024, embed_dim=256)
Input: Log-mel spectrogram (60 mels, periodic Hamming window, SpeechBrain-compatible)
Output: 107 language probabilities
Parameters: 20M
Weight format: safetensors (81.2 MB)
Conversion: SpeechBrain .ckpt → Conv1d axis swap → safetensors

Weight Conversion

Weights were converted from the original SpeechBrain checkpoint:

Conv1d axes swapped: [out, in, kernel] → [out, kernel, in] (MLX convention)
BatchNorm running stats preserved
No quantization applied

Convert script: mlx/convert_ecapa_weights.py in the lid-bench repo.