Whisper AI Transcription: Complete Guide to OpenAI's Speech-to-Text

If you've been researching transcription apps, you've probably seen "Whisper" mentioned everywhere — Reddit threads, app descriptions, tech blogs. But what actually is Whisper AI, and why does it matter for someone who just wants to transcribe meetings?

This guide explains OpenAI's Whisper speech to text model in plain language: what it does, how accurate it is, what languages it supports, and which apps let you use it without being a developer.

What Is Whisper AI?

Whisper is an automatic speech recognition (ASR) model created by OpenAI — the same company behind ChatGPT and DALL-E. Released in September 2022 and continuously improved since, Whisper converts spoken language into written text.

What makes Whisper significant:

1. It's open-source. OpenAI released the model weights publicly, meaning anyone can use it — including app developers who build it into their products.
2. It's multilingual. Whisper was trained on 680,000 hours of audio across 99 languages.
3. It runs locally. The model can run on your phone or laptop without an internet connection.
4. It's accurate. Whisper approaches human-level accuracy for clear audio in major languages.

Before Whisper, good speech-to-text required expensive cloud APIs (Google Speech, AWS Transcribe, Azure Speech). Whisper democratized high-quality transcription — and made offline, privacy-first transcription apps possible.

How Accurate Is Whisper AI Transcription?

This is the question everyone asks. The short answer: very accurate for clear audio, with caveats.

Word Error Rate (WER)

Accuracy in speech recognition is measured by Word Error Rate — the percentage of words the model gets wrong. Lower is better.

Scenario	Whisper Large V3 WER	Cloud Service Average
Clear English, single speaker	3-5%	3-5%
Meeting with 2-3 speakers	6-10%	5-8%
Heavy accent	8-15%	7-12%
Background noise	10-20%	8-15%
Non-English (major languages)	5-12%	5-10%

For practical purposes: if you're in a reasonably quiet room speaking clearly, Whisper AI transcription will capture 95%+ of your words correctly. That's comparable to major cloud transcription services.

Where Whisper Excels

• Clear speech in quiet environments: Near-perfect accuracy
• Multiple languages: Strong performance across 90+ languages, exceptional in English, Spanish, French, German, Portuguese, Japanese, Chinese
• Technical vocabulary: Handles domain-specific terms better than many cloud services (likely due to massive training data)
• Long-form audio: Maintains accuracy across extended recordings

Where Whisper Struggles

• Heavy overlapping speech: When multiple people talk simultaneously, accuracy drops
• Extreme background noise: Construction sites, loud restaurants, wind
• Very thick accents: Particularly non-standard accents in underrepresented languages
• Whispered or very quiet speech: The model needs reasonable audio volume
• Real-time streaming: Whisper processes complete audio chunks, not real-time streams (some apps work around this)

Whisper vs. Cloud Services

The honest assessment: for most use cases, Whisper speech to text is on par with Google Speech-to-Text, AWS Transcribe, and Azure Speech Services. Cloud services have a slight edge in noisy environments and with speaker diarization (identifying who said what), but the gap has narrowed significantly.

The real difference isn't accuracy — it's where the processing happens. Cloud services require your audio to leave your device. Whisper can run entirely locally.

Whisper Model Sizes Explained

Whisper comes in multiple sizes. Think of them as different trade-offs between speed and accuracy:

Model	Parameters	English Accuracy	Speed	VRAM/RAM Needed
Tiny	39M	Good	Very fast	~1 GB
Base	74M	Good+	Fast	~1 GB
Small	244M	Very good	Moderate	~2 GB
Medium	769M	Excellent	Slow	~5 GB
Large V3	1.55B	Best	Very slow	~10 GB
Large V3 Turbo	809M	Near-best	Moderate	~6 GB

For phone apps: Most use Small or Medium models, optimized for mobile hardware. The Large V3 Turbo model hits a sweet spot — nearly as accurate as Large V3 but much faster.

For desktop apps: Can run larger models since computers have more RAM and processing power.

Key insight: The difference between Small and Large is meaningful for difficult audio (accents, noise) but minimal for clear speech. For most meeting transcription, a well-optimized Small or Medium model delivers excellent results.

What Languages Does Whisper Support?

Whisper supports transcription in 99 languages. However, quality varies significantly:

Top Tier (near-English accuracy)

Spanish, French, German, Portuguese, Italian, Dutch, Japanese, Chinese (Mandarin), Korean, Russian, Polish, Turkish

Strong Performance

Arabic, Hindi, Thai, Vietnamese, Indonesian, Czech, Romanian, Greek, Hungarian, Swedish, Danish, Finnish, Norwegian

Usable but Lower Accuracy

Many African languages, smaller Asian languages, indigenous languages — Whisper can transcribe them but with higher error rates due to less training data.

Translation Feature

Whisper can also translate speech from any supported language to English text. You speak in Japanese, and the output is English text. This is separate from transcription (which outputs text in the original language).

For the complete language list and benchmarks, see OpenAI's Whisper documentation.

Which Apps Use Whisper AI?

Here's where "Whisper" becomes practical for non-developers. You don't need to install Python or run command-line tools. These apps package Whisper into user-friendly interfaces:

Mobile Apps (Whisper Transcription Apps)

Desktop Apps

MacWhisper (macOS) — Multiple Whisper model options. Batch processing, subtitle export. Free basic / $29 Pro. Full review →

360Converter Offline Transcriber (Windows, macOS) — Whisper-based desktop tool. Free basic / $9.99 Pro.

Cloud APIs Using Whisper

OpenAI Whisper API — $0.006/minute. OpenAI hosts Whisper on their servers. Fastest processing, but audio goes to the cloud.

Groq, Replicate, Together AI — Various pricing. Third-party hosting of Whisper models. Fast cloud inference. Same cloud privacy trade-offs.

Running Whisper Yourself (Technical)

If you're a developer, you can run Whisper directly:

• whisper.cpp — C++ port, runs on Mac/Linux/Windows, highly optimized
• faster-whisper — Python, CTranslate2 backend, 4x faster than original
• whisper.rn — React Native wrapper (this is what Viska uses)
• Original OpenAI implementation — Python, requires NVIDIA GPU for best performance

For non-developers: just use one of the apps above. The developer options require command-line comfort and technical setup.

Whisper vs. Other Speech-to-Text Technologies

Whisper vs. Google Speech-to-Text

	Whisper	Google STT
Accuracy	Comparable	Slightly better in noisy environments
Languages	99	125+
Offline capable	✅ Yes	❌ Cloud only
Open source	✅ Yes	❌ No
Speaker diarization	❌ Not built-in	✅ Yes
Cost	Free (local) / $0.006/min (API)	$0.006-0.024/min
Privacy	Full control (local)	Audio sent to Google

Whisper vs. AWS Transcribe

AWS Transcribe offers real-time streaming and custom vocabulary. Whisper offers open-source flexibility and offline capability. For batch transcription, accuracy is similar. For real-time needs, AWS has the edge. For privacy, Whisper wins decisively.

Whisper vs. Apple Speech Recognition

Apple's on-device speech recognition (used in Siri and Dictation) is fast and power-efficient but significantly less accurate than Whisper for extended transcription. Apple optimized for short commands and dictation; Whisper was trained specifically for long-form speech recognition.

Common Whisper AI Questions from Reddit

We've read hundreds of Reddit threads about Whisper. Here are the questions that come up most:

"Can I run Whisper on my phone?"

Yes — through apps like Viska (iPhone/Android) and WhisperNotes (iPhone). These apps run optimized Whisper models on your phone's neural processor. You don't need a powerful computer or GPU.

"Is Whisper better than Otter.ai?"

They serve different purposes. Whisper is the underlying AI model; Otter.ai is a cloud service that used to use its own models (and may now incorporate Whisper). The key difference: Whisper can run offline on your device, while Otter requires internet and sends your data to their servers. For accuracy, they're comparable for clear audio.

"Can Whisper identify different speakers?"

Not by default. Whisper transcribes speech but doesn't label who said what (speaker diarization). Some apps add speaker identification using additional models, but most Whisper-based offline apps don't include this yet. Google Recorder (Pixel only) is one of the few offline apps with speaker labels.

"Is Whisper free?"

The model itself is free and open-source. Running it on your own computer costs nothing but electricity. Apps that package Whisper into user-friendly interfaces charge for the app development — typically $5-30 as one-time purchases. OpenAI's cloud API charges $0.006/minute.

"How long does Whisper take to transcribe?"

Depends on the model size, your hardware, and the recording length. Rough estimates for a 30-minute recording:

• On phone (Viska/WhisperNotes): 5-10 minutes
• On Mac with M-series chip: 2-5 minutes
• On GPU (RTX 3080+): 1-2 minutes
• On OpenAI's API: ~30 seconds

The Future of Whisper AI

OpenAI continues to improve Whisper. The progression from V1 to V3 to V3 Turbo has brought meaningful accuracy gains and speed improvements. We expect:

• Better multilingual accuracy — particularly for underrepresented languages
• Faster on-device processing — as phone chips improve and model optimization advances
• Speaker diarization — either built into Whisper or through companion models
• Real-time capable models — current models process chunks, future versions may handle true streaming
• Smaller, more efficient models — maintaining accuracy while reducing hardware requirements

The trend is clear: high-quality transcription is becoming a commodity. The differentiator will be what happens around the transcription — summaries, action items, insights — and where the processing happens (cloud vs. local).

Apps like Viska are positioning for this future by combining Whisper transcription with on-device AI intelligence. The transcript is the starting point; the meeting summary is the product.

Frequently Asked Questions

What is Whisper AI transcription?

Whisper AI is an open-source speech-to-text model created by OpenAI. It converts spoken language into written text with high accuracy across 99 languages. Unlike cloud-only speech recognition, Whisper can run directly on your phone or computer, making it the foundation for privacy-first, offline transcription apps.

Is Whisper AI transcription accurate?

Yes. For clear audio in major languages, Whisper achieves 95-97% accuracy (3-5% word error rate), which is comparable to commercial cloud services like Google Speech-to-Text and AWS Transcribe. Accuracy decreases with background noise, overlapping speakers, and underrepresented languages.

What is the best Whisper transcription app?

For mobile: Viska (iPhone & Android, $6.99-$4.99) offers Whisper transcription plus AI meeting summaries, all offline. WhisperNotes ($4.99, Apple only) is excellent for pure transcription. For Mac desktop: MacWhisper (free/$29) provides the most control. See our full offline transcription app comparison.

Can I use Whisper AI without coding?

Absolutely. Apps like Viska, WhisperNotes, and MacWhisper package Whisper into user-friendly interfaces. You download the app, record or import audio, and get a transcript. No command line, no Python, no technical setup required.

Does Whisper AI work offline?

Yes — that's one of its biggest advantages. When running through an app like Viska or WhisperNotes, Whisper processes audio entirely on your device with no internet connection required. This is different from OpenAI's Whisper API, which is cloud-based.

How many languages does Whisper support?

Whisper supports transcription in 99 languages and can translate speech from any of those languages to English. Quality is highest for well-resourced languages (English, Spanish, French, German, etc.) and lower for languages with less training data.

Is Whisper AI free?

The Whisper model itself is free and open-source. Apps that use Whisper typically charge a one-time fee ($5-30) for the app development and user interface. OpenAI's cloud-hosted Whisper API charges $0.006 per minute of audio.