Kolyid
A working system around Yiddish speech recognition.
Kolyid brings audio, automated transcription, transcript cleaning, comparison, and human review into one model-training and production workflow. Reviewers can correct training material and examine uncertain output while staying connected to the source audio.
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Focus human review on the words the speech-recognition model is least certain about.
Build better
training data.
Speech-recognition models depend on transcripts that match what was actually said. Kolyid turns that preparation into a structured review process.
LLM and rule-based cleaning can prepare a draft. The workspace then shows word-level differences, keeps each word tied to its audio timecode, and gives a reviewer the context to resolve the change. Human decisions remain part of the pipeline instead of living in disconnected notes or files.
Use the model broadly. Put attention where it is needed.
Kolyid supports both sides of the work: preparing dependable examples for model training and reviewing the transcripts a trained model produces.
From model output to focused review
Once the model produces a transcript, Kolyid organizes it for production review. Low-confidence words are surfaced for a person to hear in context, correct, and mark as reviewed. Reviewers can work through the uncertainty without rereading every word as if the model had done nothing.
Advanced AI, made operational
The product coordinates audio, text cleaning, model output, comparison, confidence signals, and review. The review workspace keeps disputed words connected to the original recording, so the people responsible for the result can inspect and operate the process.