音声
whispering-ui
Native desktop UI for live Whisper transcription and translation.
★ 329 スターMIT
ガイドを見る →WhisperLiveは、collaboraが公開しているオープンソースの音声プロジェクトです（GitHub: collabora/WhisperLive）。GitHubスターは4,309、フォークは603で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年10月7日です。
WhisperLive: a nearly-live OpenAI Whisper transcription server for files, mics, and streams
Decide which backend fits your hardware — faster-whisper (default), TensorRT, or OpenVINO — and your client types.
Run the setup script for PortAudio, create a Python 3.12 environment, and pip-install whisper-live.
Launch run_server.py on a port with your client and connection limits.
Send a file with the client script, or use the Python TranscriptionClient from your own code.
faster-whisper, TensorRT, and OpenVINO backends for different hardware.
Word-level timestamps and confidence scores, not just plain text.
Optional diarization via pyannote.audio identifies who spoke when.
Bias recognition toward custom terms and names.
Transcribe files, live microphone input, raw PCM, and RTSP/HLS streams; batch inference is supported.
An --enable_rest flag exposes an OpenAI-style transcription API alongside the streaming protocol.
Clone the project and enter its directory.
git clone https://github.com/collabora/WhisperLive.git
cd WhisperLiveInstall system audio dependencies, then the Python package (in a Python 3.12 venv).
bash scripts/setup.sh
pip install whisper-liveRun the transcription server with the faster-whisper backend.
python3 run_server.py --port 9090 --backend faster_whisper --max_clients 4 --max_connection_time 600Collabora, the open-source consultancy, maintains WhisperLive.
Yes, MIT-licensed; Whisper models run locally on your hardware.
No cloud key is needed. Optional speaker diarization uses pyannote.audio models from Hugging Face, which may require accepting model terms there.
The API is a managed per-minute service; WhisperLive is a self-hosted streaming server with multiple local backends, diarization, and stream inputs.
Run `bash scripts/setup.sh`, `pip install whisper-live`, start `run_server.py`, then send audio with `run_client.py --files <file>`.
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