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
-
fix(nemo): harden pinned runtime execution
- Preserve the configured NeMo virtualenv interpreter path instead of resolving through uv symlinks - Accept pinned NeMo 3.0.0 source builds with PEP 440 local-version suffixes - Provide a workspace-local Numba cache for isolated NFA execution - Add symlink, runtime-version, cache, and real-integration regression coverage1d92c96
-
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(whisperx): add reference-text alignment backend
- Add isolated WhisperX reference-text alignment with typed configuration and provider boundaries - Align authoritative transcripts directly without using Whisper ASR transcription as ground truth - Parse native word timings strictly with explicit missing-timestamp and alignment failure handling - Pin the WhisperX runtime and English WAV2VEC2 alignment model with reproducible provenance - Preserve heavyweight model imports inside the isolated worker and keep neutral imports optional - Preserve programming and systemic worker failures at the whole-process boundary - Add deterministic unit, architecture, coverage, and opt-in real integration tests - Record the real five-row evaluation with 5/5 successes and 0.0154 s first-word-end MAEc3b6e69 -
feat(mfa): add forced alignment backend
- Add typed MFA configuration, corpus materialization, subprocess execution, and TextGrid parsing - Preserve authoritative transcripts with reversible MFA-specific normalization and lexical mapping - Add strict handling for OOVs, silence intervals, malformed output, and timestamp validation - Keep MFA isolated in its dedicated Conda environment and behind the shared backend protocol - Add deterministic unit, architecture, coverage, and opt-in real integration tests - Record the real five-row MFA evaluation with 5/5 successes and 0.0162 s first-word-end MAE - Document runtime, model, dictionary, G2P, provenance, and Stage 4 readinessfab9ab0 -
feat(nemo): add forced alignment backend
- Add typed NeMo configuration, reference-text manifest generation, and isolated NFA execution - Parse native word CTMs into backend-neutral alignments with strict transcript reconciliation - Enforce authoritative reference-text mode and pinned NeMo script/runtime provenance - Preserve optional native alignment artifacts and expose them through benchmark reports - Add lazy library and CLI composition without importing NeMo or Torch in the parent process - Add deterministic unit, architecture, coverage, and opt-in real integration tests - Document reproducible NeMo setup and explicitly record the blocked five-row empirical run61fc10e