| Whisper (OpenAI) | Google Speech to Text | Azure Speech | AssemblyAI | Deepgram | |
|---|---|---|---|---|---|
| Open source | Yes (MIT) | ||||
| Run locally | Yes (free) | ||||
| Languages | 98 | 125+ | 100+ | ~20 | 36+ |
| English accuracy | Excellent | Excellent | Very good | Excellent | Very good |
| API price (per min) | $0.003 to $0.006 | $0.006 to $0.024 | $0.0053+ | $0.0037+ | $0.0043+ |
| Streaming | API only (gpt-4o models) | ||||
| Speaker diarization | API only (diarize variant) | ||||
| Custom vocabulary | Via prompting | Custom model training | Custom model training | Custom vocabulary | Keywords boosting |
| Best for | Open source, privacy, high volume | Google Cloud users, many languages | Enterprise, Microsoft ecosystem | Accuracy focused, AI features | Speed and real time |
Community ecosystem highlights
C
whisper.cpp
C/C++ port of Whisper that runs efficiently on CPUs without Python or PyTorch. Supports Apple Silicon, AVX2, and WebAssembly. Ideal for edge deployment.
F
faster-whisper
CTranslate2 based reimplementation that runs up to 4x faster than the original with lower memory usage. Supports batched inference and GPU acceleration.
I
insanely-fast-whisper
Optimized inference pipeline using HuggingFace Transformers with Flash Attention 2 and batched decoding. Processes audio at 150x real time on modern GPUs.
Bottom line
Whisper is the best choice if you need an open source model you can run locally with zero per minute costs, especially for high volume or privacy sensitive workloads. Google and Azure are better if you are already in their cloud ecosystems and need enterprise support. AssemblyAI leads on accuracy and built in AI features. Deepgram excels at real time, low latency transcription. For most developers starting a new project, Whisper is the safest starting point because it is free to experiment with and you can always switch to a paid API later.