RealtimeSTT vs whisper.cpp
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Python library that turns live microphone or fed audio into text, with voice detection and wake words.
Open source · MIT
Fast C/C++ port of OpenAI's Whisper speech-to-text model, runs locally on CPU or GPU.
The short answer
Choose RealtimeSTT if you need…
- Developers prototyping a voice input feature in Python
- Builders of dictation or voice-command tools that need a microphone-to-text loop
- Teams that want to feed their own audio chunks from a websocket or file into a transcriber
- Anyone running speech recognition on a CPU server on Linux x86-64 who can follow the README's sherpa-onnx setup
Choose whisper.cpp if you need…
- Developers who want to embed offline speech-to-text in a C, C++, or other native app
- People on Apple Silicon who want GPU or Neural Engine accelerated transcription
- Users who need to transcribe audio privately on their own computer
- Projects targeting constrained or embedded hardware such as Raspberry Pi
Side-by-side
| RealtimeSTT | whisper.cpp | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 10.2k | 54.9k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | C++ |
| Last code update | 7 days ago | 7 days ago |
What each one is
RealtimeSTT
RealtimeSTT is a Python library for converting speech to text. It listens for speech using voice activity detection, can wait for a wake word, and can produce optional live text updates before a final transcript. The default path uses faster_whisper, and other speech engines are available as optional installs. Developers use it to add voice input to assistants, dictation tools, or browser streaming servers with a few lines of code.
whisper.cpp
whisper.cpp is a C/C++ implementation of OpenAI's Whisper automatic speech recognition model, which turns spoken audio into text. It is written to run with no external dependencies and can work offline on the device. People use it to add transcription to their own apps or to transcribe audio on their own machine without sending it to a cloud service.
Best for
RealtimeSTT
- Developers prototyping a voice input feature in Python
- Builders of dictation or voice-command tools that need a microphone-to-text loop
- Teams that want to feed their own audio chunks from a websocket or file into a transcriber
- Anyone running speech recognition on a CPU server on Linux x86-64 who can follow the README's sherpa-onnx setup
whisper.cpp
- Developers who want to embed offline speech-to-text in a C, C++, or other native app
- People on Apple Silicon who want GPU or Neural Engine accelerated transcription
- Users who need to transcribe audio privately on their own computer
- Projects targeting constrained or embedded hardware such as Raspberry Pi
Strengths
RealtimeSTT
- Offers several voice activity detection and wake word options, including WebRTC VAD, Silero VAD, Porcupine and OpenWakeWord
- Supports many transcription engines (faster-whisper, whisper.cpp, Moonshine, sherpa-onnx, Parakeet and others) through install extras
- Includes documentation for configuration, installation, troubleshooting and a packaged production server
whisper.cpp
- No external dependencies in the core implementation, which eases integration into many platforms
- Supports a wide range of hardware including CPU, Metal, CUDA-based NVIDIA GPUs, Vulkan, and Core ML
- Includes quantization options that reduce model size and memory use
Not ideal for
RealtimeSTT
- Non-programmers looking for a ready-made app, since the README documents only Python usage and a server recipe
- Users on Python 3.13 or newer, because the README says the current CI matrix covers only 3.11 and 3.12
- Anyone needing commercial-grade support guarantees, since the README gives no support or SLA information
whisper.cpp
- Non-technical users who want a ready-made app with a graphical interface, since the README centers on command-line usage and building from source
- Anyone who needs audio in formats other than 16-bit WAV without first converting it, because the CLI example only accepts 16-bit WAV
- Teams needing a hosted transcription API, since this is a local inference library rather than a service
Trade-offs
RealtimeSTT
- Setup can need system audio libraries (PortAudio) and, for GPU use, a CUDA setup described in separate docs
- The README's engine guidance is layered and some engines require extra installs and downloaded models, which adds complexity
- The README is truncated here, so details such as exact latency figures and resource needs are not confirmed by this analysis
whisper.cpp
- Command-line example only accepts 16-bit WAV, so audio often needs conversion first
- Building and choosing models requires comfort with CMake and the terminal
- Core ML and some accelerator paths require extra setup and platform-specific steps
Getting started
RealtimeSTT
Install with the default faster-whisper engine
pip install "RealtimeSTT[faster-whisper]"Linux: install PortAudio headers (run first)
sudo apt-get install python3-dev portaudio19-devwhisper.cpp
Clone the repository
git clone https://github.com/ggml-org/whisper.cpp.gitDownload the base.en model
sh ./models/download-ggml-model.sh base.enFrequently asked questions
What is the difference between RealtimeSTT and whisper.cpp?
RealtimeSTT: Python library that turns live microphone or fed audio into text, with voice detection and wake words. whisper.cpp: Fast C/C++ port of OpenAI's Whisper speech-to-text model, runs locally on CPU or GPU. RealtimeSTT suits developers prototyping a voice input feature in python, while whisper.cpp suits developers who want to embed offline speech-to-text in a c, c++, or other native app.
Are RealtimeSTT and whisper.cpp free?
RealtimeSTT: Free and open source. whisper.cpp: Free and open source.
Is RealtimeSTT or whisper.cpp more popular?
whisper.cpp has more GitHub stars (54.9k) than RealtimeSTT (10.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to RealtimeSTT and whisper.cpp?
Other options people compare them with include faster-whisper, Handy, Whisper, Nano-vLLM, FunASR.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.