Add README with model card and license attribution

README.md

@@ -0,0 +1,68 @@
+---
+license: other
+license_name: mixed
+license_link: LICENSE.md
+tags:
+  - speaker-diarization
+  - coreml
+  - apple
+  - macos
+  - ios
+  - sortformer
+  - wespeaker
+  - speaker-embedding
+language:
+  - en
+pipeline_tag: audio-classification
+---
+
+# Speaker Diarization CoreML Models
+
+CoreML conversions of speaker diarization and speaker embedding models for on-device inference on Apple platforms.
+
+## Models
+
+| Model | Original | Size | Description |
+|-------|----------|------|-------------|
+| `sortformer_4spk_v21.mlpackage` | [nvidia/diar_streaming_sortformer_4spk-v2.1](https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2.1) | 441 MB | Sortformer diarization model — end-to-end neural speaker diarization supporting up to 4 speakers, streaming capable |
+| `wespeaker_resnet34.mlpackage` | [WeSpeaker ResNet34](https://github.com/wenet-e2e/wespeaker) | 25 MB | ResNet34 speaker embedding model — extracts 256-dim speaker embeddings for speaker verification and identification |
+
+## Format
+
+Both models are in Apple `.mlpackage` format (FP32). On first load, CoreML compiles them to `.mlmodelc` and caches the compiled version for subsequent fast loading.
+
+- **Sortformer**: Input `mel_features (B, 128, T)` → Output `speaker_probs (B, T/8, 4)` sigmoid probabilities per speaker per frame
+- **ResNet34**: Input `fbank_features (1, 80, T)` → Output `embedding (1, 256)` speaker embedding vector
+
+## Usage
+
+These models are designed for use with the [AxiiDiarization](https://github.com/AugustDev/AxiiDiarization) Swift library:
+
+```swift
+import AxiiDiarization
+
+let pipeline = try DiarizationPipeline(
+    sortformerModelPath: "path/to/sortformer_4spk_v21.mlpackage",
+    embModelPath: "path/to/wespeaker_resnet34.mlpackage"
+)
+
+let result = try pipeline.run(samples: audioSamples)
+for segment in result.segments {
+    print("\(segment.speaker.label): \(segment.start)s - \(segment.end)s")
+}
+```
+
+## Licenses
+
+The models in this repository have separate licenses from their original authors:
+
+- **Sortformer v2.1**: Licensed by NVIDIA Corporation under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Commercial use permitted. See original model card: [nvidia/diar_streaming_sortformer_4spk-v2.1](https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2.1)
+
+- **WeSpeaker ResNet34**: Licensed under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). See original project: [wenet-e2e/wespeaker](https://github.com/wenet-e2e/wespeaker)
+
+The CoreML conversion code and this repository are MIT licensed.
+
+## Acknowledgments
+
+- NVIDIA NeMo team for the Sortformer diarization model
+- WeSpeaker / WeNet team for the ResNet34 speaker embedding model