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Describe Babki model bundles
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metadata 
license: other
license_name: mixed-upstream
license_link: https://github.com/strigov/babki

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): Community-1 voice model with its CUDA runtime; uses GigaAM and other models from the basic pack 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: Copyright (c) 2024 GigaChat Team. MIT.
  • Thanks to Ilya Stupakov for the GigaAM ONNX conversion for onnx-asr. Its LICENSE.txt is identical to the upstream GigaAM MIT notice above.
  • pyannote speaker-diarization-community-1: pyannote (Hervé Bredin et al.), CC-BY-4.0. No copyright notice was supplied. Offered as-is, without warranties; see Section 5 of CC BY 4.0. Naming these projects does not mean that their authors or licensors endorse Babki. Segmentation, embedding and PLDA files are used without modification. 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.