transcript-fixer

Transcription error correction uses dictionary-based rules and AI to automatically fix ASR mistakes in meeting notes, lectures, and interviews. This Skill applies domain-specific corrections, context-aware fixes, and continuous learning to generate clean transcripts with minimal manual review.

Yes. This Skill handles bilingual content and technical terminology by combining dictionary-driven corrections, AI-powered edits via GLM, and a learning engine that builds per-domain correction dictionaries for homophones and specialized terms.

The Skill implements a three-stage pipeline with SQLite persistence to store corrections you make. Your personal correction dictionary grows as you fix transcripts, allowing the system to apply learned patterns to future batches automatically.

Initialize the system, add 5-10 common corrections for your domain, then run the full pipeline on a transcript file. The Skill generates stage1 dictionary corrections and stage2 AI-assisted corrections, plus a diff report showing all changes.

No. The three-stage pipeline applies dictionary corrections automatically and generates diff reports for AI edits, reducing manual proofreading significantly. You review only flagged or uncertain corrections before finalizing transcripts.

The Skill uses httpx (≥0.24.0) for HTTP requests and filelock (≥3.13.0) for concurrent access. It processes transcript files and generates cleaned outputs with structured diffs; specific input formats are configured during initialization.