Audio Transcription

Audio transcription covers a wide range of source material: podcast episodes, recorded interviews, voice memos, lecture recordings, meeting captures, dictation, and field recordings. Each shape has its own quirks — podcasts are usually well-mic'd and stereo, voice memos are mono and noisy, field recordings have wind and ambient sound, lectures are single-speaker and reverberant. Modern AI transcription engines (built on OpenAI's Whisper and successors) handle all of these without per-format tuning.

The right tool depends less on the format than on what you'll do with the text. If you want a clean, editable document for an interview transcript, an audio-to-Word DOCX export is what you want. If you're producing video captions, generate SRT subtitles instead. If you're processing 50+ recordings at once for research, bulk transcription handles the queue automatically.

On accuracy: assume 95-97% on clear, single-speaker recordings; 88-94% on conversational multi-speaker audio with standard accents; and 80-88% on noisy or heavily-accented audio. Plan for a light editing pass on professional output.