Audio to Transcript Converter

The tool ingests an audio file and runs an automated speech recognition (ASR) pipeline to produce a text transcript. It supports multiple languages, optional timestamps, and speaker diarization to identify who spoke when. It is designed for journalists, researchers, podcasters, educators, and teams needing fast, indexable transcripts of interviews, lectures, and meetings. Conceptually, acoustic signals are converted into tokens, aligned with words, and assembled into readable text.

Users provide an audio file (MP3, WAV, FLAC) and may specify a language code or rely on auto-detection. If diarization is enabled, the transcript includes speaker segments with labels. The system emphasizes proper punctuation and readability while tolerating varying recording quality.

Core features include audio ingestion, language handling, optional timestamps (SRT/VTT), optional speaker labels, punctuation/capitalization, and multiple export formats (TXT, SRT, VTT, JSON). Advanced users can tune models, run batch transcriptions, and obtain per-segment confidence data.

Typical workflows cover podcasts, interviews, lectures, and research sessions. The combination of diarization, multi-format exports, and batch processing provides efficiency gains for content teams, educators, and media outlets seeking searchable archives and accessibility improvements.