What is Transcription?

Automatically converting speech (from meetings, calls or video) into text. Often paired with AI summaries and action items.

Transcription (also called speech-to-text) automatically converts spoken audio into written text. Modern AI transcription models handle accents, background noise, and technical vocabulary far better than earlier systems, and typically add timestamps, punctuation, and speaker labels (diarization) so you can tell who said what. It works in two modes: batch, where you upload a recorded file and get a transcript back, and real-time streaming, used for live captions and voice interfaces. Accuracy is measured by word error rate and varies with audio quality, so a clear podcast transcribes better than a crowded conference call. In practice, transcription is usually the first step in a pipeline: meeting tools transcribe the call, then pass the text to an LLM for summaries, action items, and search.

Example

A consultant records a one-hour client call, and a tool like Otter or Whisper produces a speaker-labeled transcript in minutes, which an LLM then condenses into a summary with action items.

Why it matters

Compare tools on accuracy with your kind of audio, speaker labeling, language support, and price per hour. If you plan to summarize or search the output, pick a tool that integrates transcription with an LLM step. Browse the AI tools directory or the model leaderboard to put it into practice.