---
library_name: coreml
license: mit
tags:
- coreml
- speaker-verification
- speaker-embedding
- diarization
- redimnet
- audio
pipeline_tag: audio-classification
---

# ReDimNet2-B6 Core ML Speaker Embeddings

This directory contains a Core ML conversion of the ReDimNet2-B6 speaker embedding model from [`PalabraAI/redimnet2`](https://github.com/PalabraAI/redimnet2).

The model is used by software to assign deterministic speaker labels inside each audio file and prefix transcriptions with markers such as:

```text
{SPEAKER_1} Добрий день.
{SPEAKER_2} Вітаю.
```

## Files

```text
ReDimNet2-B6.mlpackage/
```

## Model Details

- Source model: ReDimNet2-B6
- Upstream repository: `PalabraAI/redimnet2`
- Checkpoint: `b6-vb2+vox2_v0-lm.pt`
- Task: speaker embedding extraction
- Input: mono 16 kHz waveform
- Output: L2-normalized speaker embedding
- Core ML input name: `audio`
- Core ML output name: `embedding`

The converted package expects a fixed waveform input of `160320` samples, about `10.02s` at 16 kHz. The software pads shorter chunks and center-crops longer chunks before inference.

## Convert

From the repository root:

```bash
uv run --with torch --with torchaudio --with scipy --with coremltools \
  scripts/convert_redimnet2_coreml.py \
  --output Models/speaker/ReDimNet2-B6.mlpackage
```

## Notes

The model produces embeddings, not speaker IDs. The software performs per-file online cosine clustering over chunk embeddings. Speaker labels are deterministic within a source audio file, but `SPEAKER_1` in one file is not the same person as `SPEAKER_1` in another file.