SpeechBrain-ECAPA-VoxLingua107-21M-CoreML

aufklarer/SpeechBrain-ECAPA-VoxLingua107-21M-CoreML

SpeechBrain-ECAPA-VoxLingua107-21M-CoreML is a audio classification model from aufklarer. Use it for the audio classification task on the model card, and read the license before you ship it in a product. It is set up for coreml. The card lists the license as apache-2.0.

This is a pinned, validated compiled Core ML export of speechbrain/lang-id-voxlingua107-ecapa. It identifies one of 107 spoken-language labels on Apple devices.

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At a glance

Task: Audio Classification
Library: coreml
License: apache-2.0
Model type: speechbrain-ecapa-voxlingua107-language-id
Access: Public
Created: Aug 9, 2026
Updated: Aug 9, 2026
SHA: 2aa4d715

Base models

speechbrain/lang-id-voxlingua107-ecapa

Model card

From the Hugging Face model README

SpeechBrain ECAPA VoxLingua107 Core ML

This is a pinned, validated compiled Core ML export of speechbrain/lang-id-voxlingua107-ecapa. It identifies one of 107 spoken-language labels on Apple devices.

Model

Property	Value
Parameters	21.25 million
Format	Compiled Core ML, FLOAT16 compute
Compiled size	40.8 MiB
Input	SpeechBrain log-mel `[1, frames, 60]`
Sample rate	16 kHz
Output	107 log probabilities
Mel frames	10 to 3,001 (about 0.1 to 30 seconds)
Minimum deployment	macOS 15 / iOS 18

Unlike several earlier community conversions, this graph includes the sentence-level mean normalization used by the official SpeechBrain inference pipeline. The release also validates an independent audio frontend against SpeechBrain itself.

Files

File	Description
`SpeechBrainECAPAVoxLingua107.mlmodelc/`	Precompiled Core ML model
`frontend.py`	Reproducible SpeechBrain log-mel frontend
`labels.json`	Stable upstream index, code, and name mapping
`config.json`	Frontend, graph, source revisions, and checksums
`artifact_manifest.json`	SHA-256 and size of every compiled artifact file
`validation.json`	Conversion parity and local latency measurements
`requirements.txt`	Pinned standalone runtime dependencies
`LICENSE`	Apache 2.0 license

Validation

Check	Result
Minimum PyTorch/Core ML output cosine	0.999967
Maximum absolute error	0.217239
Warm local inference	3.7 ms

The upstream card reports 6.7% classification error on the VoxLingua107 development set. That development set contains only 1,609 manually verified clips across 33 of the 107 labels, so it is not a complete 107-language evaluation.

Usage

import coremltools as ct
import numpy as np
import soundfile as sf

from frontend import compute_fbank

model = ct.models.CompiledMLModel("SpeechBrainECAPAVoxLingua107.mlmodelc")
audio, sample_rate = sf.read("recording.wav", dtype="float32")
assert sample_rate == 16000 and audio.ndim == 1
mel = compute_fbank(audio, 60)[None, :, :]
log_probabilities = model.predict({"mel_features": mel})["log_probabilities"]

This is a closed-set classifier: it always ranks a known label. Applications need confidence calibration and an unknown-language rejection policy.

Source

Converted from the official SpeechBrain checkpoint at revision 0253049ae131d6a4be1c4f0d8b0ff483a0f8c8e9. Source and checkpoint hashes are recorded in config.json. Upstream legacy codes such as iw and jw are preserved in labels.json so class indexes never change.