Whisper
State-of-the-art speech recognition by OpenAI — run locally or via API
⚡ TL;DR
🎯 The Problem It Solves
You need to transcribe audio or video files — meetings, interviews, podcasts, phone calls. Cloud solutions cost money and upload sensitive data. Whisper from OpenAI is the best general-purpose speech recognition model available, and it can run locally via various wrappers.
🔧 How It Works
Whisper is an encoder-decoder Transformer model trained on 680,000 hours of multilingual data. Audio goes through log-Mel spectrogram processing, then the encoder-decoder pipeline. Output: text with word-level timings. Five model sizes: tiny, base, small, medium, large-v3. Larger = better accuracy but slower and more VRAM.
🚀 Installation & Quick Start
Installation
pip install openai-whisperQuick Start
- pip install openai-whisper
- whisper audio.mp3 --model base
- whisper audio.mp3 --model large-v3 # best quality
✅ Pros
- •Best open-source whisper model
- •Multilingual
- •Runs locally (API also available)
- •Free and open-source
- •Word-level timing
❌ Cons
- •Large model needs GPU for real-time
- •Struggles with uncommon accents
- •Hallucinations possible
💬 Practitioner Verdict
"Whisper is the default speech recognition solution. Best accuracy per compute, multilingual, and available as local code or API. Developers should start here."
📊 Specifications
- Language
- Python
- License
- MIT
- Platform
- Linux, macOS, Windows
- Kill Chain
- None
💰 Pricing Reality
Whisper is free (MIT) as an open-source model. OpenAI API whisper is $0.006/min. Running locally costs nothing beyond electricity.