faster-whisper vs RealtimeSTT
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
Faster Whisper speech-to-text transcription using CTranslate2, a Python reimplementation of OpenAI's Whisper.
Open source · MIT
Python library that turns live microphone or fed audio into text, with voice detection and wake words.
The short answer
Choose faster-whisper if you need…
- Python developers who need to add speech-to-text to an application
- People transcribing large batches of audio who want lower time or memory use
- Users with an NVIDIA GPU or a CPU who want to run Whisper locally
- Anyone who wants word-level timestamps or voice activity filtering on transcripts
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
Side-by-side
| faster-whisper | RealtimeSTT | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 26.4k | 10.2k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 6 days ago | 7 days ago |
What each one is
faster-whisper
faster-whisper is a Python library that transcribes speech in audio files to text, using OpenAI's Whisper models. It reimplements Whisper on top of CTranslate2, an inference engine for Transformer models. The README says it is up to 4 times faster than openai/whisper for the same accuracy and uses less memory, with further speedups from 8-bit quantization.
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.
Best for
faster-whisper
- Python developers who need to add speech-to-text to an application
- People transcribing large batches of audio who want lower time or memory use
- Users with an NVIDIA GPU or a CPU who want to run Whisper locally
- Anyone who wants word-level timestamps or voice activity filtering on transcripts
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
Strengths
faster-whisper
- The README reports up to 4x speed gains over openai/whisper at the same accuracy, with lower memory use
- Supports 8-bit quantization on both CPU and GPU to reduce resource needs
- Does not require FFmpeg to be installed separately, since audio decoding uses bundled PyAV libraries
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
Not ideal for
faster-whisper
- People who want a ready-made transcription app with a graphical interface, since this is a code library (the README shows only Python usage)
- Users with an older NVIDIA setup, because the README says the latest ctranslate2 supports only CUDA 12 and cuDNN 9, and older setups need pinned versions
- Users who need Python 3.8 or older, because the README requires Python 3.9 or greater
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
Trade-offs
faster-whisper
- GPU use requires separately installed NVIDIA cuBLAS and cuDNN libraries, which adds setup work
- Benchmarks in the README are specific to particular hardware and audio, so real results will vary
- The README is truncated and does not fully document all options; it points to source code for VAD parameters
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
Getting started
faster-whisper
Install from PyPI
pip install faster-whisperRealtimeSTT
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-devFrequently asked questions
What is the difference between faster-whisper and RealtimeSTT?
faster-whisper: Faster Whisper speech-to-text transcription using CTranslate2, a Python reimplementation of OpenAI's Whisper. RealtimeSTT: Python library that turns live microphone or fed audio into text, with voice detection and wake words. faster-whisper suits python developers who need to add speech-to-text to an application, while RealtimeSTT suits developers prototyping a voice input feature in python.
Are faster-whisper and RealtimeSTT free?
faster-whisper: Free and open source. RealtimeSTT: Free and open source.
Is faster-whisper or RealtimeSTT more popular?
faster-whisper has more GitHub stars (26.4k) than RealtimeSTT (10.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to faster-whisper and RealtimeSTT?
Other options people compare them with include Whisper, whisper.cpp, Nano-vLLM, FunASR, turbovec.
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.