eval-transcription

Measure transcription quality by running an evaluation script to compute WER, hallucination rate, and latency against a stored baseline. This automated process detects regressions before changes are shipped to production.

Multilingual speech to text evaluation metrics include Word Error Rate (WER), language detection accuracy, hallucination rate, and latency profiling. These metrics are compared against a baseline to validate model performance across different languages.

Prevent transcription regressions by applying governance policies that block deployments or flag warnings when evaluation metrics fall below defined thresholds. Baseline metrics are stored to enforce these blocking rules automatically.

Run a transcription pipeline evaluation whenever changes affect daemon.py, model selection, post-processing logic, or audio capture. Evaluating after each modification ensures quality does not drift across real-world scenarios.

Yes, evaluating audio transcription latency requires a defined evaluation dataset and an eval/baseline.json file. These inputs provide the reference metrics needed to profile latency and compare performance against prior results.

Automate hallucination rate detection by integrating an evaluation script into your pipeline that computes metrics against a baseline dataset. This approach flags anomaly rates automatically whenever transcription models or processing logic change.