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Whisper

Whisper is a family of OpenAI automatic speech-recognition models that transcribe and translate spoken audio, including multilingual audio.

How 2 Manning books explain it

Data Analysis with LLMs

whisper-1 is the model identifier for the Whisper speech-transcription model in the OpenAI API.

How it is used
Transcribes recorded speech to text and supplies voice input to downstream applications.
How deep it goes
Defines Whisper as a multilingual Transformer model and demonstrates its API call, stopping short of explaining model internals.
Examples
Uses MP3 recordings, a literary quote, microphone input, and a voice interface that queries an SQLite database.
What you can do after
Operate Whisper via OpenAI’s Python library to transcribe supported audio files and feed transcripts into applications.

Read how Data Analysis with LLMs explains Whisper →

Build Applications with Local AI Models on a Mac

Whisper is an automatic speech-recognition model developed by OpenAI for transcribing multilingual audio.

How it is used
Provides offline speech-to-text input for a voice-enabled chatbot.
How deep it goes
Explains the spectrogram-to-encoder-to-decoder flow and gives recording, model-selection, and transcription guidance without detailing model training.
Examples
Uses microphone recording, a transcribe() call, and a reusable voice_input.py module.
What you can do after
Apply it via MLX Whisper to transcribe recordings and create reusable microphone voice-input functions.

Read how Build Applications with Local AI Models on a Mac explains Whisper →

Related concepts

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