NeMo
English
audio
speech
forced-alignment
reference-text-alignment
speech-processing
mfa
whisperx
python
metric:mae
Eval Results (legacy)
Instructions to use vertox-ai/speech-text-data-aligners with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use vertox-ai/speech-text-data-aligners with NeMo:
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- Notebooks
- Google Colab
- Kaggle
Commit History
-
chore(repo): finalize licensing and documentation
- Add the standard Apache License 2.0 and VertoX-AI copyright headers - Preserve deliberately empty package markers and the standalone worker shebang policy - Rewrite the README as a concise Hugging Face repository landing page - Add project status, modular architecture, and detailed workflow documentation - Add ASCII diagrams for application, benchmark, and backend execution flows - Normalize public branding to VertoX-AI and add repository-specific citation metadata - Add concise upstream acknowledgments and official project links - Add deterministic license-header and empty-package-marker validation - Preserve all Stage 0-5 reports, benchmark evidence, runtime pins, and implementation behavior51e84d2
-
feat(aligners): add backend-neutral alignment framework
- Add reusable core, data, application, benchmarking, runtime, and CLI layers - Preserve authoritative metadata and protect source/output artifact publication - Add deterministic transcript normalization and monotonic DP reconciliation - Require explicit backend-owned authorization for ignorable observed alignment units - Add lazy backend registration without introducing concrete aligner dependencies - Enforce modular import boundaries, complete typing and documentation, and focused tests - Achieve at least 90% per-file coverage with 99.20% aggregate Stage 1 coverage - Record Stage 1 validation and readiness for NeMo backend implementationfc1dcbc