Document Community-1 provenance and license scope

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NOTICE.md

@@ -0,0 +1,42 @@
+# Attribution and license scope
+
+This is a mixed-license-scope repository. Fluid Inference intends the included
+CC-BY-4.0 license text to cover only the following exact Community-1-derived
+artifacts and their uncompiled counterparts listed in `provenance.json`:
+
+- `Segmentation.mlmodelc`
+- `FBank.mlmodelc`
+- `Embedding.mlmodelc`
+- `PLDA.mlmodelc`
+- `PldaRho.mlmodelc`
+- `plda-parameters.json`
+- `xvector-transform.json`
+
+The underlying Community-1 pipeline is published by pyannote under CC-BY-4.0:
+https://huggingface.co/pyannote/speaker-diarization-community-1
+
+The PLDA parameters originate from Brno University of Technology / BUT
+Speech@FIT. The rights holder explicitly licenses `plda.npz` and
+`xvec_transform.npz` under CC-BY-4.0, including commercial use:
+https://huggingface.co/BUT-FIT/diarizen-wavlm-large-s80-md/blob/6285693ddd5b38e8229acb93f864f3d04a82bee1/plda/LICENSE
+
+Fluid Inference converted the PyTorch components to Core ML, introduced fixed
+and enumerated input shapes, applied mixed-precision storage where recorded in
+the model metadata, separated the FBank frontend from the embedding backend,
+and compiled packages for Apple platforms.
+
+Appropriate attribution should identify pyannote, WeSpeaker, BUT Speech@FIT,
+and Fluid Inference, retain the citations in README.md, link CC-BY-4.0, and
+indicate that the files are modified Core ML conversions.
+
+## Legacy exclusions
+
+`pyannote_segmentation.mlmodelc`, `wespeaker.mlmodelc`,
+`wespeaker_v2.mlmodelc`, `wespeaker_int8.mlmodelc`, and their packages predate
+the supported Community-1 artifact set. They are retained for compatibility but
+are not covered by this Community-1 provenance and license-scope confirmation.
+Their original source and licensing must be evaluated separately.
+
+Fluid Inference can grant rights only to the extent it is authorized to do so.
+This notice does not restrict or replace rights granted directly by upstream
+licensors.

PROVENANCE.md

@@ -0,0 +1,85 @@
+# Community-1 artifact provenance
+
+This document covers the supported Community-1 artifacts in repository snapshot
+`1ed7a662fdc7109e36d822db793ee6eebdaf8594`. Exact artifact hashes are recorded
+in `provenance.json`.
+
+## Source mapping
+
+| Converted artifact | Upstream input |
+| --- | --- |
+| `Segmentation` | `segmentation/pytorch_model.bin` |
+| `Embedding` | `embedding/pytorch_model.bin` (WeSpeaker ResNet34) |
+| `FBank` | Feature-extraction graph configured from the embedding checkpoint; no separate learned checkpoint |
+| `PLDA`, `PldaRho` | `plda/plda.npz`, `plda/xvec_transform.npz` |
+| PLDA JSON resources | The same two NPZ files, serialized as Float32 tensors |
+
+The immutable upstream reference snapshot is
+`pyannote/speaker-diarization-community-1@3533c8cf8e369892e6b79ff1bf80f7b0286a54ee`.
+Its relevant file history identifies checkpoint commit
+`5428423440de6baac451412694bb79a0ad644bea` and PLDA import commit
+`7aa6699f402495053a3898e112874cb3e68ee064`.
+
+The PLDA files are byte-identical to the BUT Speech@FIT files with these hashes:
+
+| File | SHA-256 |
+| --- | --- |
+| `plda.npz` | `9b77bcd840692710dd3496f62ecfeed8d8e5f002fd991b785079b244eab7d255` |
+| `xvec_transform.npz` | `325f1ce8e48f7e55e9c8aa47e05d2766b7c48c4b25b8de8dd751e7a4cc5fbe8f` |
+
+All six tensors in the two published JSON resources exactly match these inputs
+after conversion to Float32. The six constants derived by the published PLDA
+transformation also occur byte-for-byte in `PldaRho.mlmodelc` at the locations
+referenced by its MIL graph.
+
+## Recorded conversion environment
+
+The modern artifacts record:
+
+- PyTorch 2.8.0
+- coremltools 9.0b1
+- TorchScript source dialect
+- coremlc 3500.32.1
+- MIL component 3500.14.1
+
+The public conversion reference is Mobius commit
+`33fd6eab634966ae7db4d73da3376a90379642fb`, including conversion and
+compilation scripts plus the locked Python environment. That commit postdates
+the artifact conversion dates, so it is a reference implementation rather than
+an attestation of the exact historical build.
+
+## Reproducible successor pipeline
+
+Mobius commit `ffbc3c8cae2d0ac83912a005a25a2874ced98a3a` adds a release command that:
+
+1. Rejects mutable or abbreviated upstream revisions.
+2. Downloads material inputs from the selected immutable revision.
+3. Embeds source paths, hashes, upstream revision, and converter revision in the
+   generated Core ML packages and JSON resources.
+4. Compiles the packages and emits `provenance.json` containing every input and
+   output SHA-256 plus the build environment and commands.
+5. Refuses release builds from a dirty converter checkout.
+
+Example:
+
+```bash
+uv run python build-release.py \
+  --upstream-revision 3533c8cf8e369892e6b79ff1bf80f7b0286a54ee \
+  --work-dir ./build/community-1-3533c8cf \
+  --release-dir ./build/release-3533c8cf \
+  --selective-fp16
+```
+
+Access to the gated upstream model and acceptance of its user conditions are
+required.
+
+## Historical limitations
+
+- The exact upstream checkout used for the original 2025 segmentation and
+  embedding exports was not recorded.
+- The exact historical converter commit, invocation, Xcode installation, and
+  SDK were not recorded.
+- The existing segmentation and embedding artifacts therefore remain
+  historically reconstructed, not build-attested.
+- The older `pyannote_segmentation` and `wespeaker*` artifacts are outside this
+  document's scope.

README.md

@@ -1,5 +1,7 @@
 ---
-license: cc-by-4.0
+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
@@ -10,54 +12,78 @@ tags:
 - speaker-segmentation
 base_model:
 - pyannote/speaker-diarization-community-1
-base_model_relation: finetune
 pipeline_tag: voice-activity-detection
 ---
 
+# Speaker Diarization Core ML
 
-# **<span style="color:#5DAF8D">🧃 Speaker Diarization CoreML </span>**
-[![Discord](https://img.shields.io/badge/Discord-Join%20Chat-7289da.svg)](https://discord.gg/WNsvaCtmDe)
-[![GitHub Repo stars](https://img.shields.io/github/stars/FluidInference/FluidAudio?style=flat&logo=github)](https://github.com/FluidInference/FluidAudio)
+Core ML conversions used by
+[FluidAudio](https://github.com/FluidInference/FluidAudio) for on-device speaker
+diarization on Apple platforms.
 
-Speaker diarization based on [pyannote](https://github.com/pyannote) models optimized for Apple Neural Engine.
+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.
 
-Models are trained on acoustic signatures so it supports any lanugage.
+## Supported Community-1 artifacts
 
-## Usage
-
-See the SDK for more details [https://github.com/FluidInference/FluidAudio](https://github.com/FluidInference/FluidAudio)
-
-Please note that the SDK itself is Apache 2.0, but the parent model from Pyannote is `cc-by-4.0`
-
-### Technical Specifications
-- **Input**: 16kHz mono audio
-- **Output**: Speaker segments with timestamps and IDs
-- **Framework**: CoreML (converted from PyTorch)
-- **Optimization**: Apple Neural Engine (ANE) optimized operations
-- **Precision**: FP32 on CPU/GPU, FP16 on ANE
+| 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.
 
-## Performance
+## Provenance status
 
-See the [origianl model](https://huggingface.co/pyannote/speaker-diarization-community-1) for detailed DER benchmark, for the purpose of our conversion, we tried to match the original model as much as possible: 
+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 models on CoreML exhibit a ~10x Speedup on CPU and ~20x speed up on GPU. 
+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).
 
-![plots/pipeline_timing.png](plots/pipeline_timing.png)
+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).
 
-Due to different precisions, there are minor differences in the values generated but the differences are mostly negilible, though it does account for some errors that needs to be adjusted during clustering:
+## Legacy compatibility artifacts
 
-![plots/metrics_timeseries.png](plots/metrics_timeseries.png)
+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,
+record a different toolchain, and are not included in the Community-1
+provenance or license-scope confirmation in `NOTICE.md`.
 
+## Technical specifications
 
-We see this when running the end to end pipeline with the Pytorch model versus the Core ML model (patched the Pyannote pipeline to run the Core ML model instead). The DER and JER is ~1% compared to the Pytorch model as we're dropping the precision to fp32
-![plots/pipeline_overview.png](plots/pipeline_overview.png)
+- 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 (from original model)
+## Citations
 
-1. Speaker segmentation model
+### Speaker segmentation
 
 ```bibtex
 @inproceedings{Plaquet23,
@@ -68,7 +94,7 @@ We see this when running the end to end pipeline with the Pytorch model versus t
 }
 ```
 
-2. Speaker embedding model
+### Speaker embedding
 
 ```bibtex
 @inproceedings{Wang2023,
@@ -81,13 +107,13 @@ We see this when running the end to end pipeline with the Pytorch model versus t
 }
 ```
 
-
-3. Speaker clustering
+### 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},
-}
+  journal={Computer Speech & Language},
+}
+```

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+}