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aufklarer/SpeechBrain-ECAPA-VoxCeleb-20M-MLX

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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SpeechBrain ECAPA VoxCeleb MLX

This is a reproducible MLX weight export of `speechbrain/spkrec-ecapa-voxceleb` for native Apple Silicon inference. It produces a 192-dimensional, L2-normalized voice embedding for cosine-similarity speaker comparison.

Model

Property	Value
Parameters	20.77 million
Weights	Safetensors, FLOAT16
Weight size	39.7 MiB
Input	SpeechBrain log-mel `[1, frames, 80]`
Sample rate	16 kHz
Output	192-dimensional L2-normalized embedding

Files

File	Description
`model.safetensors`	MLX-layout ECAPA weights
`mlx_model.py`	Native MLX ECAPA graph
`frontend.py`	Reproducible SpeechBrain log-mel frontend
`config.json`	Architecture, frontend, source revision, and checksums
`artifact_manifest.json`	SHA-256 and size of every artifact file
`validation.json`	PyTorch/MLX parity and local latency measurements
`requirements.txt`	Pinned standalone runtime dependencies
`LICENSE`	Apache 2.0 license

Validation

Check	Result
Minimum PyTorch/MLX output cosine	0.999999
Maximum absolute error	0.000372
Warm local inference	5.2 ms

The source model reports 0.80% equal-error rate on cleaned VoxCeleb1. Run a fresh VoxCeleb1-O evaluation before treating that upstream score as conversion parity or changing a production threshold.

Usage

python

import mlx.core as mx
import soundfile as sf

from frontend import compute_fbank
from mlx_model import SpeakerModel

audio, sample_rate = sf.read("voice.wav", dtype="float32")
assert sample_rate == 16000 and audio.ndim == 1
features = compute_fbank(audio, 80)[None, :, :]
model = SpeakerModel()
model.load_weights(list(mx.load("model.safetensors").items()), strict=True)
embedding = model(mx.array(features))
mx.eval(embedding)
print(embedding.shape, float(mx.linalg.norm(embedding).item()))

The exact frontend contract is in config.json. Speaker embeddings are not secure authentication and should not be used alone for access control.

Source