Whisper is OpenAI's open-source speech recognition model. It transcribes audio to text in 99 languages with near-human accuracy, handles accents and background noise better than any previous open-source model, and runs free on your own hardware. It also powers most of the transcription features built into other AI tools you may already use.
Stuck at any point? Ask an AI about this page →Whisper is a speech recognition model — it listens to audio and produces accurate text. Released by OpenAI in September 2022 as an open-source model, it marked a step change in what free, accessible speech-to-text could do. Before Whisper, high-quality transcription required expensive proprietary services. Whisper made near-professional quality transcription available to anyone with a computer.
It is not a product like ChatGPT. There is no Whisper app or website. It is a model — a trained neural network — that developers and technical users can run directly, or that powers transcription inside other products you may already use.
Where you already use Whisper without knowing it: Otter.ai, Descript, Adobe Podcast, many podcast transcription services, and OpenAI's own speech-to-text API all use Whisper or are based on it. It is the engine underneath most modern AI transcription.
Official site: openai.com ↗ — for pricing, live docs, and to sign up.
Whisper comes in five sizes trading accuracy for speed: Tiny (fastest, lowest accuracy), Base, Small, Medium (recommended for most use cases), and Large-v3 (highest accuracy, what the API uses). For most professional transcription, medium locally or large via the API gives the best results.
Whisper transcribes — it does not understand. It will not summarise or take action on transcripts. It is designed for batch file processing, not live streaming. For real-time transcription, Otter.ai or Google Live Transcribe are better suited. It also does not identify who is speaking — no speaker diarisation built in.
Option 1 — OpenAI API (easiest, $0.006/min): Send an audio file to OpenAI's transcription endpoint, receive a transcript. No setup, no GPU. Uses large-v3 automatically.
Option 2 — Run locally (free, needs Python): Install via pip, run one command with your audio file. CPU (slow) or GPU (fast). Best for high volumes where API cost adds up.
Option 3 — GUI apps (no coding): MacWhisper (Mac), Whisper Desktop (Windows), Buzz (cross-platform) wrap Whisper with a UI. Drop in an audio file, get a transcript.
Whisper is a sequence-to-sequence transformer trained on 680,000 hours of multilingual audio from the internet. The encoder processes a mel spectrogram of the audio; the decoder generates text autoregressively. The same architecture handles transcription, translation, and language detection through special tokens — not separate model variants. Published in "Robust Speech Recognition via Large-Scale Weak Supervision" (Radford et al., OpenAI, 2022, arXiv:2212.04356).
Whisper large-v3 (November 2023) is the current best-performing version with approximately 10-20% WER reduction versus v2. The OpenAI API uses large-v3 by default. On standard English benchmarks (LibriSpeech clean), large-v3 achieves approximately 2.7% word error rate.
The OpenAI API accepts files up to 25MB. For longer files: split audio into chunks using pydub, transcribe each chunk, concatenate results. Split at silence points rather than mid-word. The pyannote.audio library assists with silence detection.
Whisper does not separate speakers. The workaround: combine Whisper with pyannote.audio (open-source diarisation) and align the outputs. Commercial services like Otter.ai add their own diarisation layer on top of Whisper.
Source note: Technical specifications from the Whisper research paper and OpenAI API documentation. Pricing from openai.com/pricing, April 2026.
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