transcribe

To transcribe audio with speaker identification, this skill uses OpenAI models to convert speech to text while applying speaker diarization. It labels different speakers in interviews or meetings and outputs the result in plain text or diarized JSON format.

Speech to text diarization works with both audio and video files. The transcription process extracts spoken content from your recordings, identifies individual speakers, and converts the dialogue into searchable text using OpenAI's models.

You need an OpenAI API key and the openai Python library installed to convert speech to text. The transcription script relies on these dependencies to process your audio files and generate text output.

The best way to extract text from a recorded meeting is using AI transcription with speaker diarization. This approach accurately converts spoken audio into searchable text while labeling individual speakers, making it ideal for identifying key issues and resolutions.

When transcribing audio files, you can output the results in plain text, JSON, or diarized JSON formats. These options allow you to convert spoken content into searchable text or structured data for extracting information from recordings.

You can provide known-speaker hints during audio transcription to improve speaker identification accuracy. The skill uses these hints alongside OpenAI models to correctly label speakers in meetings, interviews, and customer service calls.