whisper

Transcribe multilingual audio recordings into timestamped text by processing files with speech-to-text models that support 99 languages. The system automatically identifies the spoken language and outputs segment-level timestamps for accurate text transcripts.

Yes, you can automatically translate non-English audio to English during transcription. The process detects the spoken language and generates an English translation alongside the original timestamped segments for easy analysis.

Generate SRT subtitles from podcast or meeting recordings by running batch audio processing with timestamped segment extraction. This converts the spoken audio into accurate text files formatted for subtitle use.

Yes, faster-whisper supports GPU acceleration for batch audio processing. This integration enables streaming capabilities and allows you to trade transcription speed for accuracy using multiple model sizes.

Balance speed and accuracy for speech-to-text conversion by selecting from multiple model sizes available in the system. You can also use initial prompt context to guide the transcription and improve output accuracy.

Yes, ffmpeg is integrated into the workflow to process audio files for transcription. It handles the audio decoding required before converting the recordings into accurate text transcripts and English translations.