Strigov/babki-models

Babki model bundles

Signed, self-contained model bundles for the Babki desktop transcription application. The application downloads exactly the archives referenced by its signed release (SHA-256 and size are pinned inside the installer); nothing here is meant to be used directly.

Contents

Path	What	Upstream	License
bundles/babki-quality-cpu-v*.zip	Quality (CPU): GigaAM v3 e2e-RNNT ONNX FP32, pyannote Community-1, closed CPU-only Python 3.11 runtime with torch 2.8.0+cpu	see below	see below
bundles/babki-quality-gpu-v*.zip	Quality (GPU): same profile with torch 2.8.0+cu128	see below	see below
models/gigaam-v3-e2e-rnnt-onnx-fp32-v1.tar	GigaAM v3 e2e-RNNT ONNX FP32 export	ai-sage/GigaAM-v3, ONNX conversion istupakov/gigaam-v3-onnx	MIT
models/pyannote-community-1.tar	pyannote speaker-diarization-community-1 at revision 3533c8cf8e369892e6b79ff1bf80f7b0286a54ee, unmodified	pyannote/speaker-diarization-community-1	CC-BY-4.0 (weights), MIT (pyannote.audio)

Exact upstream revisions, SHA-256 values and the conversion command are in the *.artifact-release-record.json files next to each model and inside every bundle under notices/. The Python runtime inside the Quality bundles is assembled from published wheels (PyPI and download.pytorch.org); their exact filenames and SHA-256 values are listed in notices/python-wheelhouse-inventory.json, and each wheel's own license text ships in Lib/site-packages/*.dist-info.

Attribution

• GigaAM: SberDevices / Salute Developers, MIT.
• GigaAM ONNX conversion for onnx-asr: Ilya Stupakov, MIT.
• pyannote speaker-diarization-community-1: pyannote (Hervé Bredin et al.), CC-BY-4.0. This mirror redistributes the unmodified weights with attribution as the license requires; the original gated page collects contact details for the pyannote newsletter, which this mirror does not do.
• Silero VAD v6: Silero Team, MIT.
• ONNX Runtime: Microsoft, MIT. FFmpeg (LGPL build): FFmpeg team.
• CPython 3.11 (python-build-standalone): PSF / MPL-2.0. PyTorch: BSD-3-Clause.