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Axii Diarization Coreml

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Sourcehuggingface.co/bwarzecha/axii-diarization-coreml/tree/d2cc809367dc123c336f09440a8e965dddf763b2
KindOtherVersionRevision d2cc809

Model source

Kind
Other
File size
25.3–440 MB
Version
vRevision d2cc809
Updated
Sep 19, 2026
Downloads
6
Source
Hugging Face

Source description

Speaker Diarization CoreML Models

CoreML conversions of speaker diarization and speaker embedding models for on-device inference on Apple platforms.

Models

ModelOriginalSizeDescription
sortformer_4spk_v21.mlpackagenvidia/diar_streaming_sortformer_4spk-v2.1441 MBSortformer diarization model — end-to-end neural speaker diarization supporting up to 4 speakers, streaming capable
wespeaker_resnet34.mlpackageWeSpeaker ResNet3425 MBResNet34 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 Swift library:

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:

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

Sources

1 source
Hugging FacePrimaryVersion d2cc809367dc123c336f09440a8e965dddf763b2
huggingface.co/bwarzecha/axii-diarization-coreml/tree/d2cc809367dc123c336f09440a8e965dddf763b2

Verified Sep 19

Model artifacts

2 artifacts

sortformer_4spk_v21.mlpackage/Data/com.apple.CoreML/weights/weight.bin

bin · 440 MB · SHA-256 119e7c45fc0a…ac81 · Hugging Face

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wespeaker_resnet34.mlpackage/Data/com.apple.CoreML/weights/weight.bin

bin · 25.3 MB · SHA-256 ecd83443be45…48ac · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 19huggingface.co/bwarzecha/axii-diarization-coreml

--- 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:/...

NoteSep 19huggingface.co/bwarzecha/axii-diarization-coreml

Source context: 6 downloads · 0 likes · Pipeline audio-classification · Repo bwarzecha/axii-diarization-coreml