Audio to SRT Converter

This Python-based tool converts audio into SRT subtitles by running a transcription pipeline. It targets video editors, podcasters, training teams, and accessibility professionals who require accurate, time-aligned captions without manual transcription. The approach is reproducible and suitable for batch processing, and it runs locally to protect sensitive media and intellectual property. Outputs are standards-compliant and immediately usable in most video platforms.
Conceptually, the system runs a speech-to-text model on input audio, segments the resulting transcript into caption blocks, and formats them into standard SRT with incremental indices and time stamps. The user selects a language code and a model variant (base or large); the tool then optimizes line length and duration for readability and latency.
Core differentiators include offline processing, adjustable caption length and frame rates, optional speaker labeling via diarization, and robust handling of long recordings by chunking. It supports common audio formats and produces clean SRT that can be fed into editors, video platforms, or automated pipelines.
Common use cases include post-production for YouTube videos, podcast video clips, webinar replays, and accessibility workflows. By exposing a programmable API and CLI-like behavior, teams can automate caption generation in CI pipelines or batch jobs, reducing manual effort and ensuring consistent timing across outputs.
Assumptions include a Python runtime with the necessary libraries installed; it does not rely on external cloud services by default, and human review is advised for high-accuracy captions. The tool provides validation, deterministic outputs, and audit-friendly logs for reproducibility.