| --- |
| license: other |
| license_name: scoped-cc-by-4.0 |
| license_link: https://huggingface.co/FluidInference/speaker-diarization-coreml/blob/main/NOTICE.md |
| tags: |
| - speech |
| - audio |
| - voice |
| - speaker-diarization |
| - speaker-change-detection |
| - coreml |
| - speaker-segmentation |
| base_model: |
| - pyannote/speaker-diarization-community-1 |
| pipeline_tag: voice-activity-detection |
| --- |
| |
| > Mirror of FluidInference/speaker-diarization-coreml at revision 7eab74f3463c736418d0eb10296261ae581146c9, kept by the PrivateWispr team as a backup host. Licence: scoped CC-BY-4.0 (mixed-licence repo; see NOTICE.md and LICENSE, kept unchanged). Original authors: pyannote (Community-1 pipeline, segmentation-3.0), WeSpeaker, BUT Speech@FIT (PLDA parameters) and FluidInference (Core ML conversions, modified from the originals). All model files are byte-identical to the upstream revision; only this header was added to the README. |
|
|
| # Speaker Diarization Core ML |
|
|
| Core ML conversions used by |
| [FluidAudio](https://github.com/FluidInference/FluidAudio) for on-device speaker |
| diarization on Apple platforms. |
|
|
| The supported Community-1 artifact set is distributed under CC-BY-4.0. See |
| [NOTICE.md](NOTICE.md) for attribution and the precise license scope, and |
| [PROVENANCE.md](PROVENANCE.md) plus [provenance.json](provenance.json) for |
| source, conversion, environment, and file-integrity records. |
|
|
| ## Supported Community-1 artifacts |
|
|
| | Artifact | Source | |
| | --- | --- | |
| | `Segmentation.mlmodelc` | `pyannote/speaker-diarization-community-1/segmentation/pytorch_model.bin` | |
| | `FBank.mlmodelc` | Deterministic feature-extraction graph configured from the embedding checkpoint | |
| | `Embedding.mlmodelc` | `pyannote/speaker-diarization-community-1/embedding/pytorch_model.bin` | |
| | `PLDA.mlmodelc`, `PldaRho.mlmodelc` | `plda/plda.npz` and `plda/xvec_transform.npz` | |
| | `plda-parameters.json`, `xvector-transform.json` | Serialized tensors from the same two PLDA files | |
|
|
| The snapshot includes uncompiled packages for `Segmentation`, `FBank`, |
| `Embedding`, and `PldaRho` under `mlpackages/`; no uncompiled `PLDA` package was |
| published. These are Core ML conversions, not fine-tuned models. |
|
|
| ## Provenance status |
|
|
| The artifacts are pinned by SHA-256 in `provenance.json`. Their historical |
| source lineage has been reconstructed from the public Community-1 repository, |
| artifact metadata, and the published conversion source. The exact local |
| upstream checkout and Mobius commit used for the original 2025 conversion were |
| not recorded, so the existing binaries are not described as a fully attested |
| reproducible build. |
|
|
| The immutable upstream reference used for reconstruction is |
| [`pyannote/speaker-diarization-community-1@3533c8cf`](https://huggingface.co/pyannote/speaker-diarization-community-1/tree/3533c8cf8e369892e6b79ff1bf80f7b0286a54ee). |
| The public historical conversion reference is |
| [`FluidInference/mobius@33fd6eab`](https://github.com/FluidInference/mobius/tree/33fd6eab634966ae7db4d73da3376a90379642fb/models/speaker-diarization/pyannote-community-1/coreml). |
|
|
| The current conversion pipeline requires a full upstream commit SHA, records |
| input and output hashes, embeds source metadata in each model, and captures the |
| build environment. It is available at |
| [`FluidInference/mobius@ffbc3c8`](https://github.com/FluidInference/mobius/tree/ffbc3c8cae2d0ac83912a005a25a2874ced98a3a/models/speaker-diarization/pyannote-community-1/coreml). |
|
|
| ## Legacy compatibility artifacts |
|
|
| The repository also retains `pyannote_segmentation.mlmodelc`, |
| `wespeaker.mlmodelc`, `wespeaker_v2.mlmodelc`, and `wespeaker_int8.mlmodelc` |
| for FluidAudio's legacy online diarizer. They predate the Community-1 export |
| and are not covered by its license scope. They are Core ML conversions of |
| [`pyannote/segmentation-3.0`](https://huggingface.co/pyannote/segmentation-3.0) |
| (MIT) and [`pyannote/wespeaker-voxceleb-resnet34-LM`](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM) |
| (CC-BY-4.0); see `NOTICE.md` for revisions, parity evidence, and attribution. |
|
|
| ## Technical specifications |
|
|
| - Input: 16 kHz mono audio |
| - Output: speaker segments with timestamps and speaker identifiers |
| - Framework: Core ML converted from PyTorch |
| - Deployment target: iOS 17 / macOS 14 or later |
| - Community-1 conversion metadata: PyTorch 2.8.0, coremltools 9.0b1, |
| TorchScript source dialect |
| - Compiled MIL metadata: coremlc 3500.32.1, MIL 3500.14.1 |
|
|
| ## Usage |
|
|
| See the [FluidAudio diarization documentation](https://github.com/FluidInference/FluidAudio/tree/main/Documentation/Diarization). |
|
|
| ## Citations |
|
|
| ### Speaker segmentation |
|
|
| ```bibtex |
| @inproceedings{Plaquet23, |
| author={Alexis Plaquet and Hervé Bredin}, |
| title={{Powerset multi-class cross entropy loss for neural speaker diarization}}, |
| year=2023, |
| booktitle={Proc. INTERSPEECH 2023}, |
| } |
| ``` |
|
|
| ### Speaker embedding |
|
|
| ```bibtex |
| @inproceedings{Wang2023, |
| title={Wespeaker: A research and production oriented speaker embedding learning toolkit}, |
| author={Wang, Hongji and Liang, Chengdong and Wang, Shuai and Chen, Zhengyang and Zhang, Binbin and Xiang, Xu and Deng, Yanlei and Qian, Yanmin}, |
| booktitle={ICASSP 2023, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| pages={1--5}, |
| year={2023}, |
| organization={IEEE} |
| } |
| ``` |
|
|
| ### Speaker clustering |
|
|
| ```bibtex |
| @article{Landini2022, |
| author={Landini, Federico and Profant, J{\'a}n and Diez, Mireia and Burget, Luk{\'a}{\v{s}}}, |
| title={{Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks}}, |
| year={2022}, |
| journal={Computer Speech & Language}, |
| } |
| ``` |
|
|
