ModelListedExternal

SpeechBrain Coreml

Pre-converted speechbrain/lang-id-voxlingua107-ecapa model for spoken language identification. Supports 107 languages from audio.

Sourcehuggingface.co/drakulavich/SpeechBrain-coreml/tree/41e60dea31b80ea5d4f9d9d9e818501ea184e568

KindOtherBase modelspeechbrain/lang-id-voxlingua107-ecapaVersionLang id ecapaUnratedgrade

Model source

Kind: Other
File size: 742 KB
Base model: speechbrain/lang-id-voxlingua107-ecapa
Version: vLang id ecapa
Updated: Nov 5, 2026
Source: Hugging Face

Source description

SpeechBrain ECAPA-TDNN — ONNX + CoreML

Pre-converted speechbrain/lang-id-voxlingua107-ecapa model for spoken language identification. Supports 107 languages from audio.

Converted for use with Kesha Voice Kit — open-source voice toolkit.

Files

File	Format	Size	Description
`lang-id-ecapa.onnx`	ONNX	~760KB	Model graph
`lang-id-ecapa.onnx.data`	ONNX	~85MB	Model weights (external data)
`lang-id-ecapa.mlpackage.tar.gz`	CoreML	~40MB	CoreML model archive (macOS)
`labels.json`	JSON	<1KB	107 ISO 639-1 language codes

Usage with Kesha Voice Kit

bun install -g @drakulavich/kesha-voice-kit
kesha install          # downloads this model automatically
kesha --json audio.ogg # transcribe + detect language

Usage with ONNX Runtime (Python)

import onnxruntime as ort
import numpy as np
import json

session = ort.InferenceSession("lang-id-ecapa.onnx")
with open("labels.json") as f:
    labels = json.load(f)

# Input: 16kHz mono float32 waveform
audio = np.random.randn(1, 160000).astype(np.float32)  # 10 seconds
result = session.run(None, {"waveform": audio})
probs = result[0][0]

top_idx = np.argmax(probs)
print(f"Language: {labels[top_idx]} (confidence: {probs[top_idx]:.4f})")

Usage with ONNX Runtime (Rust)

use ort::session::Session;

let session = Session::builder()?.commit_from_file("lang-id-ecapa.onnx")?;
// Input: "waveform" [1, samples] float32
// Output: "language_probs" [1, 107] float32

Model Details

Architecture: ECAPA-TDNN (originally for speaker recognition, adapted for language ID)
Training data: VoxLingua107 — 6628 hours of speech across 107 languages
Input: Raw waveform at 16kHz mono ([1, samples] float32)
Output: Language probabilities ([1, 107] float32, softmax applied)
Error rate: 6.7% on VoxLingua107 dev set

Supported Languages

ab, af, am, ar, as, az, ba, be, bg, bn, bo, br, ca, ceb, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fo, fr, gl, gn, gu, ha, haw, he, hi, hr, ht, hu, hy, ia, id, is, it, ja, jw, ka, kk, km, kn, ko, la, lb, ln, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, nn, no, oc, pa, pl, ps, pt, ro, ru, sa, sd, si, sk, sl, sn, so, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, uk, ur, uz, vi, war, yi, yo, zh

Conversion

Converted from PyTorch using torch.onnx.export (ONNX) and torch.export + coremltools (CoreML).

Conversion script: scripts/convert-lang-id-model.py

License

Apache 2.0 (same as the original SpeechBrain model)

Sources

1 source

Hugging FacePrimaryVersion 41e60dea31b80ea5d4f9d9d9e818501ea184e568

huggingface.co/drakulavich/SpeechBrain-coreml/tree/41e60dea31b80ea5d4f9d9d9e818501ea184e568

Verified Nov 5

Model artifacts

1 artifact

lang-id-ecapa.onnx

onnx · 742 KB · SHA-256 4af3b6a5b416…a288 · Hugging Face

Download file

Source excerpts

2 excerpts

ReadmeAug 29huggingface.co/drakulavich/SpeechBrain-coreml

--- language: - multilingual tags: - speech - language-identification - ecapa-tdnn - onnx - coreml - kesha-voice-kit license: apache-2.0 library_name: onnx pipeline_tag: audio-classification base_model: speechbrain/lang-id-voxlingua107-ecapa --- # SpeechBrain ECAPA-TDNN — ONNX + CoreML Pre-converted [speechbrain/lang-id-voxlingua107-ecapa](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa) model for spoken language identification. Supports **107 languages** from audio. Converted for use with [Kesha Voice Kit](https://github.com/drakulavich/kesha-voice-kit) — open-source voice toolkit. ## Files | File | Format | Size | Description | |---|---|---|---| | `lang-id-ecapa.onnx` | ONNX | ~760KB | Model graph | | `lang-id-ecapa.onnx.data` | ONNX | ~85MB | Model weights (external data) | | `lang-id-ecapa.mlpackage.tar.gz` | CoreML | ~40MB | CoreML model archive (macOS) | | `labels.json` | JSON | <1KB | 107 ISO 639-1 language codes | ## Usage with Kesha Voice Kit ```bash bun install -g @drakulavich/kesha-voice-kit kesha install # downloads this model automatically kesha --json audio.ogg # transcribe + detect language ``` ## Usage with ONNX Runtime (Python) ```python import onnxruntime as ort import numpy as np import json session = ort.InferenceSession("lang-id-ecapa.onnx") with open("labels.json") as f: labels = json.load(f) # Input: 16kHz mono float32 waveform audio = np.random.randn(1, 160000).astype(np.float32) # 10 seconds result = session.run(None, {"waveform": audio}) probs = result[0][0] top_idx = np.argmax(probs) print(f"Language: {labels[top_idx]} (confidence: {probs[top_idx]:.4f})") ``` ## Usage with ONNX Runtime (Rust) ```rust use ort::session::Session; let session = Session::builder()?.commit_from_file("lang-id-ecapa.onnx")?; // Input: "waveform" [1, samples] float32 // Output: "language_probs" [1, 107] float32 ``` ## Model Details - **Architecture:** ECAPA-TDNN (originally for speaker recognition, adapted for language ID) - **Training data:** [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/) — 6628 hours of speech across 107 languages - **Input:** Raw waveform at 16kHz mono (`[1, samples]` float32) - **Output:** Language probabilities (`[1, 107]` float32, softmax applied) - **Error rate:** 6.7% on VoxLingua107 dev set ## Supported Languages ab, af, am, ar, as, az, ba, be, bg, bn, bo, br, ca, ceb, cs, cy, da,...

NoteAug 29huggingface.co/drakulavich/SpeechBrain-coreml

Source context: 0 downloads · 0 likes · Pipeline audio-classification · Library onnx · Repo drakulavich/SpeechBrain-coreml