Whisper Large v3
Open SourceOpenAI · 2024-09
OpenAI's state-of-the-art speech recognition model with multilingual transcription at high accuracy.
Quick Facts
Parameters
~1.55B
Context Window
N/A
Modalities
audio, text
Open Source
Yes
License
MIT
Pricing
Free (open-source) / API from $0.006/minute
Released
2024-09
Developer
OpenAI
About
Whisper Large v3 is OpenAI's most advanced automatic speech recognition (ASR) model, capable of transcribing and translating audio in 99+ languages with near-human accuracy. With approximately 1.55 billion parameters, it is the largest and most capable model in the Whisper family, offering the highest transcription accuracy at the cost of requiring more computational resources. Built on a Transformer-based encoder-decoder architecture trained on 680,000 hours of weakly supervised multilingual audio data, Whisper models demonstrate remarkable robustness across diverse real-world conditions: background noise, multiple speakers, varied accents, different recording qualities, and technical terminology. The model supports multilingual transcription (transcribing audio in its original language), direct translation to English, automatic language identification, and timestamp generation for each transcribed segment. What makes Whisper technologically significant is its MIT open-source license — unlike cloud speech APIs that charge per minute of audio and require internet connectivity, Whisper can be run locally on your hardware, providing unlimited transcription with complete privacy. This makes it invaluable for processing sensitive audio like medical recordings, legal proceedings, or confidential meetings. Available in multiple sizes (tiny, base, small, medium, large) to suit different hardware capabilities and accuracy requirements — the tiny model runs on a Raspberry Pi while the large model delivers the highest accuracy on GPU hardware. For developers building speech-enabled applications, organizations with privacy requirements for audio processing, and researchers working on multilingual speech recognition, Whisper Large v3 offers research-grade accuracy in an open-source package. Compared to cloud alternatives like Google Speech-to-Text or Azure Speech Services, Whisper offers comparable accuracy with zero per-minute costs and complete data locality.
Strengths
- +Near-human accuracy across 99+ languages
- +Open-source with permissive MIT license
- +Runs locally for privacy-sensitive use cases
- +Handles noise, multiple speakers, and various accents
Weaknesses
- −Large model requires significant compute for real-time
- −Less accurate on very specialized domain terminology
- −No speaker diarization built-in
Best For
Multilingual audio transcription at scale
Privacy-sensitive speech processing
Content accessibility and subtitling
Voice-controlled applications and assistants
Pricing
Open Source
$0
- Full model weights
- Local execution
- Unlimited use
- Full privacy
API
From $0.006/minute
- Scalable deployment
- No GPU needed
- 99+ languages
- Translation
Benchmarks
| Benchmark | Whisper Large v3 | Competitor |
|---|---|---|
| Common Voice | 15.1% WER | Google Speech: 18.2% WER |
Technical Specs
Parameters
~1.55B
Context Window
N/A
Modalities
audio, text
Languages
Open Source
Yes
License
MIT
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