whisper

Multilingual speech-to-text transcription converts spoken audio into accurate text across 99 languages. You can automatically detect the language in your recordings or specify it manually to generate readable transcripts for podcasts, interviews, and meetings.

Yes, translation to English is supported for non-English audio transcripts. The speech-to-text pipeline can automatically transcribe multilingual recordings and translate the output into English for quick understanding and analysis.

You can balance transcription speed and accuracy by selecting from various model sizes, ranging from tiny to large. Loading a smaller model speeds up processing for large archival recordings, while larger models deliver higher accuracy.

Yes, timestamp generation is supported for transcription workflows. Adding timestamps to meeting and interview transcripts helps segment spoken dialogue and aligns the text output with specific points in the original audio recording.

The best way to transcribe podcasts across multiple languages is using automatic language detection within a multilingual speech-to-text model. This approach accurately processes diverse audio inputs and outputs readable text for search and analysis.

No, you do not need to specify a language for automatic speech-to-text detection. The transcription model automatically identifies the spoken language from the audio input, though you can manually specify it to fit specific pipeline requirements.