RealtimeSTT
RealtimeSTT is a Python speech-to-text library for applications that need
voice activity detection, fast transcription, optional realtime text updates,
wake words, and direct access to audio streams. It is designed for assistants,
dictation tools, browser streaming servers, and prototypes that need to turn
speech into text with only a few lines of code.
The general-purpose default path uses faster_whisper. Other engines are
available through install extras when their optional dependencies and models
are present.
Recommended Engine Profiles
- CUDA / GPU: Keep using the established
faster_whisper CUDA setup. It
remains the recommended general-purpose GPU path.
- CPU: For production streaming on Linux x86-64, the strongly recommended
profile is
sherpa-onnx-nemotron-3.5-asr-streaming-0.6b-560ms-int8 for fast,
replaceable realtime text together with
sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8 for the single authoritative
final transcript. Nemotron processes only new audio frames during the turn;
Parakeet then refines the complete turn once at finalization. This pairing
provides substantially better CPU streaming behavior than repeatedly
retranscribing a growing audio buffer while preserving a high-quality final.
Install the CPU server stack and both pinned model bundles with:
python -m pip install "RealtimeSTT[server,sherpa-onnx]"
stt-install-sherpa-models --root ./models/sherpa-onnx --model all
See the production server guide for
the authenticated HTTP/WebSocket deployment recipe and exact pinned model
directories.
Support RealtimeSTT
If RealtimeSTT saved you time, one GitHub star is a simple way to help make it more stable.
Stars improve visibility and visibility brings more users, more real-world testing, more bug reports, more fixes, and better releases for everyone.
Demo
https://github.com/user-attachments/assets/797e6552-27cd-41b1-a7f3-e5cbc72094f5
CLI demo code (reproduces the video above)
Featured Integration: Kroko/Banafo ASR
RealtimeSTT includes native support for kroko_onnx, the local streaming ASR
engine from the Kroko/Banafo team.
This integration has been on my wishlist for a long time. Kroko is a strong fit
for RealtimeSTT's goals: fast, accurate local speech recognition.
Start with the public Community models for local testing, or see Kroko/Banafo's
commercial model options if you need production licensing and higher-end models.
pip install "RealtimeSTT[kroko-builder,silero-onnx-cpu]"
stt-install-kroko --build
The silero-onnx-cpu extra gives AudioToTextRecorder a local VAD backend for
recorder-based smoke tests and live microphone use.
See the Kroko-ONNX engine guide,
Kroko ASR docs, and
kroko-onnx on GitHub.
Install
The current CI matrix covers Python 3.11 and 3.12. Python 3.13 and newer are
not release targets until dependency and CI gates are available.
pip install "RealtimeSTT[faster-whisper]"
On Linux, install PortAudio headers before installing the package:
sudo apt-get update
sudo apt-get install python3-dev portaudio19-dev
On macOS:
brew install portaudio
For CUDA, platform notes, and optional engine stacks, see
docs/installation.md.
Microphone Example
This waits for speech, stops after the detected utterance, and prints the final
transcript:
from RealtimeSTT import AudioToTextRecorder
if __name__ == "__main__":
with AudioToTextRecorder() as recorder:
print("Speak now")
print(recorder.text())
Use the if __name__ == "__main__": guard when running scripts, especially on
Windows, because RealtimeSTT uses multiprocessing for model work.
Automatic Recording Loop
For continuous dictation, pass a callback to text() so transcription work can
complete asynchronously while your loop keeps listening:
from RealtimeSTT import AudioToTextRecorder
def process_text(text):
print(text)
if __name__ == "__main__":
recorder = AudioToTextRecorder()
while True:
recorder.text(process_text)
External Audio
Set use_microphone=False when audio comes from a file, stream, websocket, or
another process. Feed 16-bit mono PCM chunks at 16 kHz, or pass the original
sample rate so RealtimeSTT can resample:
from RealtimeSTT import AudioToTextRecorder
if __name__ == "__main__":
recorder = AudioToTextRecorder(use_microphone=False)
with open("audio_chunk.pcm", "rb") as audio_file:
recorder.feed_audio(audio_file.read(), original_sample_rate=16000)
print(recorder.text())
recorder.shutdown()
More examples are in docs/quick-start.md and
docs/external-audio.md.
Configuration Reference
Every AudioToTextRecorder constructor parameter is documented in
docs/configuration.md, including model/engine
selection, realtime transcription, VAD timing, wake words, callbacks, external
audio, logging, and executor injection.
Features
- Voice activity detection with WebRTC VAD and Silero VAD.
- Final and realtime transcription with selectable engines.
- Optional wake word activation through Porcupine or OpenWakeWord.
- Direct microphone input or application-fed audio chunks.
- Event callbacks for recording, VAD, realtime text, transcription, and wake
word state.
- A packaged production FastAPI server with versioned HTTP/WebSocket contracts,
session isolation, bounded shared inference resources, authentication, and
readiness/capabilities endpoints.
- A browser streaming reference app for source checkouts.
Documentation
- Quick start: shortest demos and common recording
patterns.
- Installation: platform setup, CUDA notes, and optional
dependencies.
- Configuration: complete
AudioToTextRecorder
parameter reference.
- Transcription engines: engine selection and
setup links.
- Custom transcription engines: public
base class, executor integration, streaming sessions, and contribution guide.
- Wake words: Porcupine and OpenWakeWord setup.
- External audio: feeding audio without a microphone.
- Testing: maintained unit and opt-in golden test workflow.
- Test scripts: demos, manual tests, regressions, and
legacy experiments under
tests/.
- FastAPI server: browser server configuration,
protocol, metrics, and deployment notes.
- Production server: packaged remote
HTTP/WebSocket API, authentication, limits, and deployment recipe.
- Troubleshooting: common install, audio, CUDA,
model, dependency, and runtime errors.
- Engine licenses: license notes for optional engine
runtimes and model families.
Engine-specific references:
Production Server
The supported remote server is packaged as an optional install. It binds to
loopback by default and exposes versioned health, readiness, capabilities,
raw-PCM final transcription, and ordered streaming WebSocket endpoints.
Direct non-loopback binds require both a bearer token and Uvicorn TLS
certificate/key files; for a reverse-proxy deployment, keep the server on
loopback and terminate TLS at the proxy.
python -m pip install "RealtimeSTT[server,faster-whisper]"
stt-server-production --host 127.0.0.1 --port 8010
For CPU INT8 deployment, the recommended pairing is
sherpa-onnx-nemotron-3.5-asr-streaming-0.6b-560ms-int8 for live hypotheses
and sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8 for authoritative final
transcription. Install RealtimeSTT[server,sherpa-onnx] and both pinned model
bundles into persistent storage before following the server recipe. The
server extra includes the local Silero ONNX VAD runtime used by legacy
recorder-backed server paths. The versioned production WebSocket path owns its
turn state and does not derive finalization from recorder VAD, so production
startup does not need an interactive Torch Hub download:
stt-install-sherpa-models --root ./models/sherpa-onnx --model all
See
PRODUCTION_SERVER.md.
The interactive browser reference app remains in example_fastapi_server for
source checkouts. See docs/fastapi-server.md for its
UI, engine recipes, protocol details, and metrics.
Contributing
Focused tests and small changes are easiest to review. The project keeps fast
unit tests separate from opt-in real-model tests; see docs/testing.md.
License
MIT
Author
Kolja Beigel
一个 Python 语音转文字库，能把麦克风或流式音频低延迟地转成文字：包含语音活动检测、可选的唤醒词、实时部分文字输出，以及只需几行代码就能得到的最终转写文本。