Whisper

Multilingual speech recognition transcribes spoken audio into accurate text across 99 languages using the Whisper model. It supports auto language detection and translation to English, making it suitable for transcribing podcasts, lectures, and meetings.

Speech recognition generates captions for podcasts and videos by transcribing spoken audio into text. Whisper supports media captioning across many languages, improving accessibility and searchability with optional word-level timestamps for precise alignment.

Yes, ffmpeg is required to run Whisper for audio transcription. In addition to ffmpeg, you need Python and the Whisper model installed via pip to process audio files and generate multilingual transcripts successfully.

Whisper offers flexible model sizes ranging from tiny to large, allowing you to balance transcription speed and accuracy. Smaller models process audio faster while larger models deliver higher accuracy for complex multilingual speech recognition workloads.

Word-level timestamps for transcribed audio are generated as an optional output during speech recognition. Whisper supports this feature, providing precise timing alignment for each word to facilitate media captioning and detailed transcript analysis.