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README.md

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+---
+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.

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ReDimNet2-B6.mlpackage/Manifest.json

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+    "itemInfoEntries": {
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+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
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+}