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LICENSE

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+MIT License
+
+Copyright (c) 2026 Palabra.ai
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+language:
+  - multilingual
+tags:
+  - coreml
+  - speaker-verification
+  - speaker-embedding
+  - voice-identity
+base_model: PalabraAI/redimnet2
+library_name: coreml
+pipeline_tag: audio-classification
+---
+
+# 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
+
+```python
+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
+
+```bash
+speech embed-speaker voice.wav --engine redimnet2 --json
+```
+
+```swift
+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](https://github.com/PalabraAI/redimnet2) B6
+`vb2+vox2_v0` large-margin checkpoint. The source revision and checkpoint
+SHA-256 are recorded in `config.json`.
+
+## Links
+
+- [speech-swift](https://github.com/soniqo/speech-swift) — Apple SDK
+- [Docs](https://soniqo.audio/getting-started) — install and CLI docs
+- [soniqo.audio](https://soniqo.audio)
+- [blog](https://soniqo.audio/blog)

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config.json

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+{
+  "model_type": "redimnet2-b6-speaker-coreml",
+  "sample_rate": 16000,
+  "input_samples": 96000,
+  "input_duration_seconds": 6.0,
+  "minimum_recommended_duration_seconds": 2.0,
+  "embedding_dimension": 192,
+  "input_name": "audio",
+  "output_name": "embedding",
+  "output_normalized": true,
+  "checkpoint": "b6-vb2+vox2_v0-lm.pt",
+  "checkpoint_sha256": "e0a7d340a92f798720d1208949aa6a6bd0cddcb0ba7d4cec33596a17a484e6a2",
+  "source_repository": "https://github.com/PalabraAI/redimnet2.git",
+  "source_revision": "2a8d15f65b1dfb5d73fede2f11ee42bcccca3035",
+  "compute_precision": "float16",
+  "compiled_model": "ReDimNet2B6.mlmodelc",
+  "compiled_size_bytes": 25931707,
+  "validation": {
+    "pytorch_coreml_cosine": 0.9999898663545027,
+    "max_absolute_error": 0.0011521875858306885,
+    "output_l2_norm": 0.9999155402183533,
+    "warm_latency_median_ms": 13.766916505119298,
+    "warm_latency_min_ms": 13.575500008300878
+  }
+}