Speech recognition—also called audio-to-text—turns spoken audio into text a computer can search, store, summarize, or otherwise process. It is a practical shortcut for extracting the useful parts of recordings without sitting through every last minute. section 4.9
A common workflow is pleasantly lazy, in the productive sense:
- Send a missed video-conference recording to a transcription service and receive a full text script. section 4.9
- Convert the script to a PDF, upload it to ChatPDF, and request a focused summary. section 4.9
- Use a video URL with Summarize.tech for a quick summary, or use transcription APIs when the workflow needs automation. section 4.9
Turn a 90-minute call into a short briefing
Extract the audio from a recorded business call, use OpenAI Whisper to create a transcript, export it to PDF, and ask ChatPDF about the topics that matter. One demonstrated workflow reduced a 90-minute, roughly 15,000-word recording to key moments in 10 minutes. section 8.1
Whisper is one option for automated transcription. Beyond multilingual speech recognition, it can identify languages and translate speech. Google Speech-to-Text is another named API option. In a media-production workflow, Whisper can also transcribe a generated narration track and compare that transcript with the original script. section 4.9 section 4.11
import whisper
model = whisper.load_model("base")
result = model.transcribe("MyAudio.flac")
print(result["text"])
This Python example loads Whisper's base model, transcribes an audio file, and prints the resulting text. section 4.9
