OpenAI Whisper
Open source speech recognition model supporting 98 languages, available both as a free local tool and through OpenAI's transcription API.
What it is
Whisper is a general purpose automatic speech recognition (ASR) model created by OpenAI. It uses a transformer encoder-decoder (sequence to sequence) architecture and was originally trained on 680,000 hours of multilingual and multitask supervised data collected from the web. The model is fully open source under the MIT license and has earned 101,000+ stars on GitHub, making it one of the most popular open source AI projects in the world. Whisper performs multilingual transcription, translation to English, language identification, and voice activity detection across 98 languages. Model sizes range from tiny (39M parameters) to large (1.55B parameters), with a turbo variant (809M parameters, a pruned version of large-v3) that offers fast inference with minimal quality loss. The latest open source release is v20250625. On the API side, OpenAI offers whisper-1 at $0.006 per minute, plus newer gpt-4o-transcribe models with improved accuracy and streaming support. A massive community ecosystem has emerged around Whisper, including whisper.cpp (C/C++ port for CPU inference), faster-whisper (CTranslate2 based, 4x faster), and insanely-fast-whisper (batched inference on GPU). The large-v3-turbo model alone has over 3.38 million monthly downloads on HuggingFace.
Who it is for
Developers building speech to text features into applications who want a proven, reliable transcription engine
Companies wanting on premises transcription without sending sensitive audio data to the cloud
Researchers needing an open model they can fine tune, modify, and study for specialized domains
Content creators transcribing podcasts, videos, interviews, and recordings at scale
Multilingual organizations needing transcription across many languages with a single model
Startups that want to start with the free open source model and optionally scale to the API later
Pros and cons
Pros
- +Fully open source under MIT license with no usage restrictions, allowing unlimited commercial and personal use at zero cost
- +Free to run locally on your own hardware, from a Raspberry Pi (tiny model) to a workstation GPU (large model)
- +98 language support with automatic language detection, one of the broadest multilingual ASR models available
- +Multiple model sizes let you choose the right tradeoff between speed and accuracy for your hardware and use case
- +Massive community ecosystem including whisper.cpp, faster-whisper, and insanely-fast-whisper with significant performance improvements
- +API option provides a managed, hassle free experience for teams that do not want to manage GPU infrastructure
- +Word level timestamps enable precise subtitle generation, audio editing, and synchronized text overlays
Cons
- −The large-v3 model requires approximately 10GB of VRAM, putting it out of reach for machines without a dedicated GPU
- −The turbo model cannot translate (only transcribe), so translation still requires the full large model or the older models
- −API pricing adds up for high volume workloads; at $0.006/min, 1,000 hours of audio costs $360
- −The newer gpt-4o-transcribe models are not open source, so the best API accuracy is locked behind the paid service
- −Accuracy varies significantly by language; high resource languages (English, Spanish, French) perform much better than low resource ones
- −The 30 second processing window can cause issues with long pauses, silence, or non speech audio segments
Pricing
- All model sizes (tiny to large-v3)
- Turbo variant included
- 98 language support
- $0.006 per minute of audio
- Managed infrastructure
- Automatic language detection
- $0.003 per minute of audio
- Improved accuracy over whisper-1
- Streaming support
- Managed infrastructure
- $0.006 per minute of audio
- Highest accuracy
- Full streaming support
Prices change; check the official pricing →