ReDimNet2-B6-CoreML

aufklarer/ReDimNet2-B6-CoreML

ReDimNet2-B6-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 mit.

ReDimNet2-B6 produces local speaker embeddings for comparing clean voice samples. It does not diarize audio or assign names by itself.

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At a glance

Task: Audio Classification
Library: coreml
License: mit
Model type: redimnet2-b6-speaker-coreml
Access: Public
Created: Jul 15, 2026
Updated: Jul 15, 2026
SHA: 1272aa38

Base models

PalabraAI/redimnet2

Model card

From the Hugging Face model README

ReDimNet2-B6 Core ML Speaker Embeddings

ReDimNet2-B6 produces local speaker embeddings for comparing clean voice samples. It does not diarize audio or assign names by itself.

Model

Property	Value
Parameters	12.3 million
Format	Compiled Core ML, Float16 weights
Compiled size	24.7 MiB
Input	96,000 mono Float32 samples
Sample rate	16 kHz
Window	6 seconds
Output	192-dimensional L2-normalized embedding
Minimum deployment	macOS 15 / iOS 18

The checkpoint was trained on VoxBlink2 and VoxCeleb2. The fixed six-second shape avoids the slow Core ML fallback observed with a flexible waveform shape. Applications should repeat clean two-to-six-second speech to fill the input and center-crop longer samples.

Files

File	Size	Description
`ReDimNet2B6.mlmodelc/`	24.7 MiB	Precompiled Core ML model
`config.json`	<2 KiB	Input, output, source revision, checksum, and validation metadata
`README.md`	<4 KiB	This model card
`LICENSE`	1.0 KiB	MIT license from the upstream implementation

Performance

Measured on an Apple M2 Max after two warm-up predictions:

Measurement	Result	Meaning
Warm six-second inference	13.8 ms	One voice-profile embedding
Warm throughput	72.6 embeddings/s	Repeated six-second windows after warm-up
Meeting pilot equal-error rate, 2-second clips	1.50%	Lower is better; WeSpeaker Core ML was 5.17%
Meeting pilot equal-error rate, 3-second clips	0.00%	Lower is better; WeSpeaker Core ML was 1.50%
LibriSpeech test-clean equal-error rate, 40 speakers	0.00%	Two- and three-second controls

The meeting pilot contains five recurring speakers and is not a universal quality claim. Thresholds must be calibrated for the intended microphones, languages, and acoustic conditions. Speaker embeddings are useful for labeling; they are not biometric authentication and do not protect against voice spoofing.

Python usage

import coremltools as ct
import numpy as np

model = ct.models.CompiledMLModel("ReDimNet2B6.mlmodelc")
audio = np.zeros((1, 96_000), dtype=np.float32)
embedding = model.predict({"audio": audio})["embedding"]

speech-swift

speech embed-speaker voice.wav --engine redimnet2 --json

import SpeechVAD

let model = try await ReDimNet2SpeakerModel.fromPretrained()
let embedding = try model.embed(audio: samples, sampleRate: 16_000)

Source

Converted from the official PalabraAI/ReDimNet2 B6 vb2+vox2_v0 large-margin checkpoint. The source revision and checkpoint SHA-256 are recorded in config.json.