Key takeaways
- Whisper is OpenAI's open-source speech-to-text model, released in 2022 under the MIT licence.
- It is an encoder-decoder transformer trained on 680,000 hours of multilingual audio, which makes it robust to accents and noise.
- You can run it locally for free, so audio never leaves your machine; the hosted API is paid per use.
- It translates speech from many languages into English, but not into other languages.
- Wispr Flow is a separate dictation product from a different company, not OpenAI's Whisper.
Whisper is an open-source speech recognition model from OpenAI that turns spoken audio into text in dozens of languages, and can translate speech into English. It was released in 2022 under the MIT licence, so you can run it on your own computer for free, or use OpenAI's hosted speech-to-text API. This page explains what the model is and how it works, who owns it, running it locally, whether the API is free, translation, security, and how it differs from Wispr Flow, a similarly named dictation app.
What is OpenAI Whisper?
Whisper is an automatic speech recognition (ASR) system made by OpenAI. It transcribes audio into text, detects the spoken language, adds timestamps and can translate other languages into English. OpenAI released the code and model weights openly in September 2022.
Because it is open source and strong out of the box, Whisper became the default choice for transcription in many apps: meeting notes, podcast transcripts, subtitles, voice notes and accessibility tools. It handles accents, background noise and technical vocabulary better than many earlier systems, though not perfectly. Developers use it locally through Python or faster community implementations, or through OpenAI's API, which also offers newer transcription models.
What is the Whisper AI model?
Whisper is a family of transformer models in several sizes, from tiny to large, trading speed for accuracy. Smaller models run quickly on a laptop CPU; the large models are most accurate and benefit from a GPU.
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The sizes are tiny, base, small, medium and large, with English-only variants of the smaller sizes and later large versions, including a faster "turbo" version, that improved accuracy and speed. Choosing a size:
- tiny or base: quick drafts, low-power devices.
- small: a good default on laptops.
- medium or large: best accuracy, especially for non-English audio and noisy recordings.
Test two sizes on your own audio; the right choice depends on your accent mix, noise and speed needs.
How does Whisper AI work?
Whisper converts audio into a log-Mel spectrogram, a picture of sound frequencies over time, in 30-second windows. A transformer encoder processes that spectrogram, and a decoder generates the text token by token, along with special tokens for language, task and timestamps.
Its strength comes from training data: 680,000 hours of multilingual audio paired with transcripts collected from the web, described as weak supervision because the labels are imperfect. Training on such varied data made it robust to accents, recording quality and domains without fine-tuning. The same model handles several tasks, chosen by special tokens: transcribe, translate to English, detect language. Long recordings are processed window by window. Its known weaknesses include occasional hallucinated text during silence or music, and errors with rare names.
Who owns Whisper AI?
OpenAI created Whisper and maintains the official repository. Because it is released under the MIT licence, anyone can use, modify and include it in commercial products, provided they keep the licence notice.
That openness is why many companies and open-source projects build on it, including faster reimplementations such as whisper.cpp and faster-whisper, and products that wrap it with interfaces, diarisation and editing tools. OpenAI also offers hosted transcription through its API, including newer proprietary transcription models. Apps that say they use "Whisper" may be running the open model themselves or calling OpenAI's API, which affects where your audio goes.
Is Whisper AI local?
It can be. The open-source Whisper model runs entirely on your own computer, offline, with no audio sent anywhere. OpenAI's API version runs on OpenAI's servers instead.
Running locally is useful for privacy, such as interviews, medical or legal recordings and personal voice notes, and for cost, since there is no per-minute charge. The trade-off is speed and setup: large models are slow on a CPU, so use a GPU or an optimised implementation such as whisper.cpp or faster-whisper. Apple Silicon Macs and modern NVIDIA GPUs run Whisper well. For live captions with low latency, streaming setups need more engineering than batch transcription.
How do I use Whisper AI locally?
Install Python and ffmpeg, install the Whisper package, and run it on an audio file from the command line or from Python.
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pip install -U openai-whisper
whisper meeting.mp3 --model small --language en --output_format srt
import whisper
model = whisper.load_model("small")
result = model.transcribe("meeting.mp3")
print(result["text"])
Output formats include plain text, subtitles (SRT, VTT) and JSON with timestamps. For speed on a laptop, try faster-whisper or whisper.cpp, which run the same models more efficiently. Set the language explicitly when you know it; automatic detection can misfire on short clips.
Is the Whisper AI API free?
No. OpenAI's hosted speech-to-text API is paid per minute of audio. The open-source model is free to run on your own hardware, which is the free route.
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The API is convenient: no setup, fast processing and access to newer transcription models in addition to Whisper. It suits apps that need reliable transcription at scale without managing GPUs. For students and personal projects, running the open model locally or in Google Colab is free and often enough. Check OpenAI's pricing page for current rates and file limits, which apply to uploads, so split very long recordings into parts.
Can Whisper AI translate?
Yes, but only into English. Whisper can take speech in many languages and output an English translation directly, using its translate task. It does not translate English speech into other languages.
Use it with --task translate on the command line or task="translate" in Python. Quality depends on the source language and audio; widely spoken languages translate best. For translation into Hindi or other languages, transcribe first in the original language, then translate the text with a language model or a translation service. For Indian languages, test carefully, because accuracy varies by language and accent, and mixed Hindi-English speech is challenging for most systems.
Is Whisper AI secure?
Run locally, Whisper is as secure as your own computer: the audio and text never leave it. Through an API or a third-party app, security depends on that provider's handling of your recordings.
Points to check before using a hosted service for sensitive audio: whether recordings and transcripts are stored and for how long, whether they are used for training, where processing happens, and whether a data processing agreement is available. Get consent before recording anyone. Also watch for hallucinated text: Whisper sometimes produces words that were never spoken, especially during silence or music, which matters for legal, medical or accessibility uses, so review transcripts against the audio.
What is Wispr Flow?
Wispr Flow is a voice dictation app from a company called Wispr that lets you speak anywhere you type, on desktop and mobile, and turns speech into cleaned-up text. Despite the similar name, it is a separate product, not OpenAI's Whisper model.
Dictation apps like Wispr Flow focus on the writing experience: removing filler words, fixing punctuation, adapting tone to the app you are typing in, and working across your applications. Which speech models such apps use behind the scenes is up to the company. If you want free, private transcription of recordings, open-source Whisper on your machine is the closer match; if you want fast dictation across every app, a dedicated dictation tool is more convenient. Check any app's privacy policy, because dictated text often includes sensitive messages.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
