realtimestt 1.0.0

A fast Voice Activity Detection and Transcription System

RealtimeSTT lets you choose the transcription and wake-word dependencies you want to install.

Recommended default local Whisper install:

pip install "realtimestt[faster-whisper]"

Core package only, without a transcription engine or wake-word backend:

pip install realtimestt

Install multiple extras by separating them with commas:

pip install "realtimestt[faster-whisper,porcupine]"
pip install "realtimestt[whisper-cpp,openwakeword]"

Available extras include:

• faster-whisper: default CTranslate2 Whisper backend
• whisper-cpp: whisper.cpp backend through pywhispercpp
• openai-whisper: original OpenAI Whisper Python backend
• sherpa-onnx: sherpa-onnx CPU backends
• parakeet: NVIDIA NeMo Parakeet backend
• transformers: shared Transformers dependency for Moonshine, Granite, and Cohere
• moonshine, granite, cohere: aliases for the Transformers dependency set
• qwen: Qwen ASR backend
• qwen-vllm: Qwen ASR with vLLM extras
• porcupine: Porcupine wake-word backend
• openwakeword: OpenWakeWord wake-word backend
• wakewords: both wake-word backends
• recommended/default: faster-whisper backend
• all: all PyPI-installable optional backends

The WebRTC VAD and Silero VAD dependencies are still part of the core install because AudioToTextRecorder currently initializes both VAD paths.

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 recommended default path uses faster_whisper. Other engines are available through install extras when their optional dependencies and models are present.

Install

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 FastAPI browser streaming server example with multi-user session isolation, shared inference resources, metrics, and health endpoints.

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.
• 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.
• Troubleshooting: common install, audio, CUDA, model, dependency, and runtime errors.

Engine-specific references:

• faster-whisper
• whisper.cpp
• OpenAI Whisper
• Moonshine
• sherpa-onnx
• Parakeet NeMo
• Transformers engines
• Cohere Transcribe

Server Example

The browser server lives in example_fastapi_server:

python -m pip install -r example_fastapi_server/requirements.txt
python example_fastapi_server/server.py --host 0.0.0.0 --port 8010

Open http://localhost:8010. See docs/fastapi-server.md for engine recipes, websocket protocol details, health checks, 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