PDFAITools
Whisper
OpenAI's open-source speech recognition system. State-of-the-art accuracy across 100 languages.
What is Whisper?
What is Whisper?
Whisper is OpenAI's open-source automatic speech recognition (ASR) system, trained on 680,000 hours of multilingual and multitask supervised data collected from the web. It achieves state-of-the-art accuracy across 100 languages and a wide variety of accents, audio conditions, and technical domains — making it one of the most capable and widely deployed speech recognition systems available.
Open Source and Developer Focused
Whisper is released as an open-source model, meaning developers can download and run it locally without sending audio to any external service. This makes it particularly valuable for privacy-sensitive applications. Multiple model sizes are available — from tiny models suitable for real-time use on consumer hardware to large models optimized for maximum accuracy. It powers speech recognition in countless third-party applications and services.
- State-of-the-art accuracy across 100+ languages
- Open-source — run locally with full data privacy
- Multiple model sizes for different speed/accuracy trade-offs
- Handles diverse accents, noise, and technical vocabulary
- Automatic language detection
Who Uses Whisper
Whisper is used by developers, researchers, and businesses building speech-to-text applications where accuracy, multilingual support, and data privacy are priorities. It underpins many commercial transcription and voice assistant products.
Key Features
Industry-leading transcription accuracy across accents, noise, and domains.
Transcribe and translate speech from over 100 languages automatically.
Fully open-source — run locally for complete data privacy and no API costs.
Choose from tiny to large models balancing speed vs. accuracy needs.
Automatically identifies the spoken language without configuration.
Who Uses Whisper?
Build transcription and voice features into apps using Whisper's open-source model.
Transcribe sensitive audio locally without sending data to external services.
Accurately transcribe content in rare or regional languages.
Use Whisper as a foundation for speech recognition and NLP research.
Pros & Cons
✅ Pros
- Open-source with no usage fees when self-hosted
- Best-in-class multilingual accuracy across 100+ languages
- Privacy-preserving local deployment option
- Handles challenging audio conditions and technical vocabulary well
- Widely supported — enormous ecosystem of tools built on Whisper
❌ Cons
- Requires technical knowledge to set up and run locally
- Large model sizes require significant compute for real-time use
- No built-in speaker diarization (speaker identification)
- API via OpenAI has per-minute pricing for cloud use
Whisper Pricing
Self-Hosted
- All model sizes
- Unlimited use
- Full privacy
- Open-source license
OpenAI API
- Cloud inference
- No setup required
- Whisper-1 model
- Simple REST API
Whisper earns a 4.4/5 rating from our editorial team. It's completely free to use with no hidden costs, making it one of the most accessible tools in the Audio & Voice space. Standout strengths include open-source with no usage fees when self-hosted and best-in-class multilingual accuracy across 100+ languages.
Get Started with Whisper →