initial bundle: segmentation-3.0 + wespeaker_resnet34_lm (3 forms) + PLDA weights, with attribution

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+wespeaker_resnet34_lm.onnx.data filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,22 @@
+This bundle redistributes model artifacts under three different upstream
+licenses. The full text of each license is included in the
+`LICENSE.MIT`, `LICENSE.APACHE-2.0`, and `LICENSE.CC-BY-4.0` files
+alongside this one. Each artifact retains its upstream license; using
+the bundle obligates the user to comply with all three.
+
+  segmentation-3.0.onnx                   — MIT (Copyright © 2023 CNRS,
+                                            Hervé Bredin / pyannote.audio)
+  wespeaker_resnet34_lm.onnx (+ .data)    — Apache-2.0 (WeSpeaker / wenet-e2e)
+  wespeaker_resnet34_lm_packed.onnx       — Apache-2.0 (derivative of above:
+                                            same weights repacked into a
+                                            single file)
+  wespeaker_resnet34_lm.pt                — Apache-2.0 (TorchScript export)
+  plda/*                                  — CC-BY-4.0 (BUT Speech@FIT,
+                                            redistributed via pyannote/
+                                            speaker-diarization-community-1)
+
+CC-BY-4.0 attribution for plda/* (required by upstream):
+  PLDA model trained by BUT Speech@FIT (https://speech.fit.vut.cz/).
+  Integration of VBx in pyannote.audio by Jiangyu Han and Petr Pálka.
+
+See README.md for upstream sources and snapshot revisions.

LICENSE.APACHE-2.0

@@ -0,0 +1,201 @@
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LICENSE.CC-BY-4.0

@@ -0,0 +1,52 @@
+Creative Commons Attribution 4.0 International (CC-BY-4.0)
+
+This CC-BY-4.0 license applies to:
+  plda/eigenvectors_desc.bin
+  plda/lda.bin
+  plda/mean1.bin
+  plda/mean2.bin
+  plda/mu.bin
+  plda/phi_desc.bin
+  plda/psi.bin
+  plda/tr.bin
+  plda/plda.npz
+  plda/xvec_transform.npz
+
+Required attribution (per upstream `plda/README.md` in the
+`pyannote/speaker-diarization-community-1` HuggingFace snapshot at
+revision 3533c8cf8e369892e6b79ff1bf80f7b0286a54ee):
+
+  PLDA model trained by BUT Speech@FIT (https://speech.fit.vut.cz/).
+  Integration of VBx in pyannote.audio by Jiangyu Han and Petr Pálka.
+
+The full text of the CC-BY-4.0 license is available at:
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LICENSE.MIT

@@ -0,0 +1,27 @@
+MIT License
+
+Copyright (c) 2023 CNRS
+
+This MIT license applies to:
+  segmentation-3.0.onnx
+
+Author: Hervé Bredin (CNRS / IRIT) — pyannote.audio author and lead trainer.
+Source: https://huggingface.co/pyannote/segmentation-3.0
+
+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
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,210 @@
+---
+license: other
+license_name: mixed-mit-cc-by-4-apache-2
+license_link: LICENSE
+language:
+- en
+- multilingual
+library_name: onnx
+tags:
+- speaker-diarization
+- diarization
+- pyannote
+- speaker-embedding
+- wespeaker
+- segmentation
+pipeline_tag: voice-activity-detection
+---
+
+# dia-models — pyannote community-1 model bundle for the `dia` Rust crate
+
+A single-repo distribution of every model artifact the
+[`dia`](https://github.com/al8n/diarization) Rust crate needs to run
+end-to-end speaker diarization with **pyannote-community-1** parity:
+
+- The **segmentation-3.0** powerset speaker network (16 kHz audio →
+  per-frame speaker activations).
+- The **WeSpeaker ResNet34-LM** speaker-embedding network, in three
+  forms (external-data ONNX, single-file ONNX, TorchScript).
+- The **PLDA** whitening + LDA weights from the
+  [`pyannote/speaker-diarization-community-1`](https://huggingface.co/pyannote/speaker-diarization-community-1)
+  pipeline, in both `.npz` (build-time) and raw little-endian f64
+  `.bin` (runtime) form.
+
+`dia` already embeds the segmentation model and the PLDA weights into
+the compiled binary via `include_bytes!`; the **WeSpeaker** ONNX is
+the only artifact callers must download separately. This repo lets
+callers grab any individual model — or the whole bundle — without
+spelunking through the upstream pyannote / WeSpeaker repos.
+
+> **Attribution: this is a redistribution, not new model training.**
+> All weights come from upstream pyannote / WeSpeaker / BUT Speech@FIT.
+> The licenses below MUST be preserved by anyone redistributing.
+
+## Files
+
+| File | Size | Format | License |
+|---|---:|---|---|
+| `segmentation-3.0.onnx` | 5.99 MiB | ONNX (single file) | MIT |
+| `wespeaker_resnet34_lm.onnx` | 256 KiB | ONNX header (external data) | Apache-2.0 |
+| `wespeaker_resnet34_lm.onnx.data` | 25.3 MiB | external-data weights | Apache-2.0 |
+| `wespeaker_resnet34_lm_packed.onnx` | 25.5 MiB | ONNX (single file, repacked) | Apache-2.0 |
+| `wespeaker_resnet34_lm.pt` | 25.6 MiB | TorchScript | Apache-2.0 |
+| `plda/eigenvectors_desc.bin` | 128 KiB | f64 (128×128 row-major) | CC-BY-4.0 |
+| `plda/lda.bin` | 256 KiB | f64 (256×128 row-major) | CC-BY-4.0 |
+| `plda/mean1.bin` | 2 KiB | f64 (256,) | CC-BY-4.0 |
+| `plda/mean2.bin` | 1 KiB | f64 (128,) | CC-BY-4.0 |
+| `plda/mu.bin` | 1 KiB | f64 (128,) | CC-BY-4.0 |
+| `plda/phi_desc.bin` | 1 KiB | f64 (128,) | CC-BY-4.0 |
+| `plda/psi.bin` | 1 KiB | f64 (128,) | CC-BY-4.0 |
+| `plda/tr.bin` | 128 KiB | f64 (128×128 row-major) | CC-BY-4.0 |
+| `plda/plda.npz` | 131 KiB | numpy (`mu`, `tr`, `psi`) | CC-BY-4.0 |
+| `plda/xvec_transform.npz` | 131 KiB | numpy (`mean1`, `mean2`, `lda`) | CC-BY-4.0 |
+
+## Which file do I want?
+
+### Segmentation
+Use `segmentation-3.0.onnx`. It feeds `dia::segment::SegmentModel`
+(or any pyannote-segmentation-compatible runtime). Single file, no
+external data, works on every ORT execution provider.
+
+### Embedding (WeSpeaker)
+Three forms, same weights, pick by use case:
+
+- **`wespeaker_resnet34_lm.onnx` + `wespeaker_resnet34_lm.onnx.data`**
+  — the default ONNX layout. Loads on CPU / TensorRT / CUDA / OpenVINO
+  / DirectML. The `.onnx` and `.onnx.data` files MUST sit next to
+  each other on disk; ORT resolves the external pointer by relative
+  path.
+- **`wespeaker_resnet34_lm_packed.onnx`** — same model with all
+  weights inlined into one file. Use this if you want a single-file
+  artifact, or if the runtime is **CoreML** (Apple Silicon — Apple's
+  graph optimizer chokes on external initializers and reports
+  `model_path must not be empty`; the packed form sidesteps it).
+  Otherwise functionally identical.
+- **`wespeaker_resnet34_lm.pt`** — TorchScript export for the
+  `tch` backend. Bit-exact to upstream PyTorch on hard cases (heavy-
+  overlap fixtures where the ONNX→ORT path can drift by O(1) per
+  element). Pulls in libtorch (~600 MB shared library).
+
+### PLDA
+The eight `.bin` files are the runtime data — raw little-endian f64
+blobs that `dia::plda` embeds via `include_bytes!`. The two `.npz`
+files are the build-time sources (`xvec_transform.npz` exposes
+`mean1` / `mean2` / `lda`; `plda.npz` exposes `mu` / `tr` /
+`psi`); they are mirrored from the upstream pyannote-community-1
+snapshot for traceability and so the `.bin` extraction can be
+re-run via `scripts/extract-plda-blobs.sh` in the dia repo.
+
+`eigenvectors_desc.bin` and `phi_desc.bin` are scipy-derived
+eigenvectors of the PLDA generalized eigenproblem `(B, W)` — pinned
+to avoid LAPACK eigenvector-sign indeterminism (which produced a
+38% DER divergence on three-speaker fixtures when nalgebra and
+scipy disagreed on 67 of 128 column signs). See
+[`models/plda/SOURCE.md`](https://github.com/al8n/diarization/blob/main/models/plda/SOURCE.md)
+in the dia repo for the regeneration procedure.
+
+## Provenance
+
+### segmentation-3.0.onnx
+- **Upstream:** [`pyannote/segmentation-3.0`](https://huggingface.co/pyannote/segmentation-3.0)
+- **Original layout:** `pytorch_model.onnx` in the upstream HF repo.
+- **License:** MIT — Copyright (c) 2023 CNRS
+- **Author:** Hervé Bredin (CNRS / IRIT), pyannote.audio author and
+  lead trainer.
+- **SHA-256:** `057ee564753071c0b09b5b611648b50ac188d50846bff5f01e9f7bbf1591ea25`
+
+### wespeaker_resnet34_lm.onnx (+ .data) / .pt / _packed.onnx
+- **Upstream model architecture:** WeSpeaker ResNet34 with
+  large-margin (LM) angular fine-tuning, trained on VoxCeleb-2.
+- **Upstream sources:**
+  - [WeSpeaker project](https://github.com/wenet-e2e/wespeaker) (Apache-2.0)
+  - [`onnx-community/wespeaker_resnet34_lm`](https://huggingface.co/onnx-community/wespeaker_resnet34_lm)
+    for the ONNX export.
+- **License:** Apache-2.0.
+- **`_packed.onnx` derivative:** produced by loading
+  `wespeaker_resnet34_lm.onnx` + `.onnx.data` via the `onnx` Python
+  library (`onnx.load(path, load_external_data=True)`) and re-saving
+  with `save_as_external_data=False`. Same weights, no external file.
+
+### plda/
+- **Upstream:** [`pyannote/speaker-diarization-community-1`](https://huggingface.co/pyannote/speaker-diarization-community-1)
+- **License:** CC-BY-4.0
+- **Snapshot revision:** `3533c8cf8e369892e6b79ff1bf80f7b0286a54ee`
+- **Original layout in the upstream HF repo:**
+  `plda/xvec_transform.npz` and `plda/plda.npz`.
+- **Attribution (per upstream `plda/README.md`):**
+  PLDA model trained by [BUT Speech@FIT](https://speech.fit.vut.cz/);
+  integration of VBx in pyannote.audio by Jiangyu Han and Petr Pálka.
+
+## Usage
+
+### From `dia` (Rust)
+```rust
+use diarization::{
+  embed::EmbedModel,
+  plda::PldaTransform,
+  segment::SegmentModel,
+};
+// Segmentation + PLDA are bundled by default — no download needed.
+let mut seg = SegmentModel::bundled()?;
+let plda = PldaTransform::new()?;
+// WeSpeaker is BYO; download from this repo.
+let mut emb = EmbedModel::from_file("wespeaker_resnet34_lm.onnx")?;
+# Ok::<(), Box<dyn std::error::Error>>(())
+```
+
+### Direct download
+```bash
+# whole bundle
+hf download FinDIT-Studio/dia-models --local-dir ./dia-models
+
+# just the embedding model (default ONNX form)
+hf download FinDIT-Studio/dia-models \
+  wespeaker_resnet34_lm.onnx wespeaker_resnet34_lm.onnx.data \
+  --local-dir ./models
+
+# CoreML-friendly single-file form
+hf download FinDIT-Studio/dia-models \
+  wespeaker_resnet34_lm_packed.onnx --local-dir ./models
+```
+
+## Licenses
+
+This repository **redistributes** model artifacts under three different
+licenses. Each artifact retains its upstream license. By using this
+bundle you agree to comply with **all three**:
+
+- **MIT** for `segmentation-3.0.onnx` (Copyright © 2023 CNRS, Hervé Bredin).
+  See `LICENSE.MIT`.
+- **Apache-2.0** for the WeSpeaker artifacts. See `LICENSE.APACHE-2.0`.
+- **CC-BY-4.0** for everything under `plda/`. See `LICENSE.CC-BY-4.0`.
+  Required attribution: *PLDA model trained by BUT Speech@FIT;
+  integration of VBx in pyannote.audio by Jiangyu Han and Petr Pálka.*
+
+The `dia` Rust crate that consumes these models is itself dual-licensed
+MIT OR Apache-2.0; that licensing applies to the source code, not to the
+model weights bundled here.
+
+## Citation
+
+If you use these weights in academic work, please cite the upstream
+papers / model cards:
+
+- **Segmentation-3.0:** Hervé Bredin, *pyannote.audio 2.1 speaker
+  diarization pipeline: principle, benchmark, and recipe*, Interspeech
+  2023.
+- **WeSpeaker:** Wang et al., *WeSpeaker: A research and production
+  oriented speaker embedding learning toolkit*, ICASSP 2023.
+- **PLDA / VBx:** Landini et al., *Bayesian HMM clustering of x-vector
+  sequences (VBx) in speaker diarization: theory, implementation and
+  analysis on standard tasks*, Computer Speech & Language, 2022.
+
+## Issues / questions
+
+This repo is a **redistribution** of upstream artifacts. Please file
+issues against:
+
+- The dia Rust crate: <https://github.com/al8n/diarization/issues>
+- The pyannote.audio project: <https://github.com/pyannote/pyannote-audio/issues>
+- The WeSpeaker project: <https://github.com/wenet-e2e/wespeaker/issues>

plda/eigenvectors_desc.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:caaa0425dc9cc73ffe559f5abe0b8010e31792050f6bd5922eb15ddb84b4f5ee
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