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:
# tag did not correspond to a valid NeMo domain.
- Notebooks
- Google Colab
- Kaggle
Commit History
-
feat(readiness): materialize full-word timing consensus
- add reusable deterministic full-word timing materialization from accepted alignment evidence - enforce exact frozen semantic-boundary reproduction before successful output - add deterministic mapping, provenance, publication, and failure-path coveraged6e2489
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feat(readiness): add W1-T semantic timing proxy
- Add backend-neutral semantic-readiness timing extraction and cross-aligner consensus - Add deterministic W1-token to lexical-alignment mapping with explicit uncertainty handling - Add reuse-first NeMo, MFA, and WhisperX evidence validation and provenance checks - Add CLI and application workflows for offline-only semantic-readiness timing - Add runtime-isolation, architecture, token-mapping, consensus, and coverage tests - Document the provisional non-human-measured W1-T evaluation contractc9761c3
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fix(nemo): close empirical alignment validation
- Correct NeMo native word-CTM manifest handling - Close real GPU smoke, five-row, and full-36 evidence - Record the final three-aligner selection and final 36-row annotation - Preserve historical Stage 5 evidence and bounded architecture588788b -
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
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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
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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