Caption Local turns microphone audio into text using a locally running speech model. Open its capture page in a browser, choose a microphone, and start captions. Use the text locally, download a transcript, or send it to the caption.ninja editor for a person to correct before publishing to OBS, a venue screen, phones, or a webpage.

You do not need an API key, a paid transcription account, or an AI assistant. The project is open source and runs independently of caption.ninja.

What to expect

Transcription: captions in the language being spoken.
Translation: speech translated into English, or original captions and English together. Other translation targets are not included.
Local processing: audio is processed on the machine running the service. Sharing with caption.ninja is off by default; enabling it sends caption text through its relay.
A few seconds of delay: continuous speech is normally collected in roughly six-second windows before processing. Human review adds more time.
Automatic captions need review: names, accents, overlapping voices and noisy rooms can produce mistakes. Rehearse with your actual event audio.
No automatic recording: audio and text are held in memory. Use Stop, let pending captions finish, then download your transcript before closing the tab.

Release scope: Linux x86-64 CPU is tested with native Python and Docker. Windows launchers and NVIDIA GPU configurations are supplied but have not been validated on that hardware. Twelve simultaneous English streams passed a five-minute CPU test with the faster preset; that is not a promise of twelve accurate multilingual streams on any computer. Read the measurements and limits.

What you need

Requirement	Details
Inference computer	An Intel/AMD 64-bit Linux computer for the validated configuration. Ubuntu with Python 3.12 is the tested native environment.
Installation method	Python 3.12 with venv support or Docker with the Compose plugin. Docker does not require host Python.
Memory and storage	Start with at least 4 GiB RAM and several GB of free disk for one stream; these are planning allowances, not tested minimums. The twelve-stream test host had a six-core Ryzen 5 5500 and 32 GiB RAM.
Browser and audio	A current desktop Chrome or Edge browser and a microphone/audio input visible to it. Chrome was exercised by the automated browser tests.
Initial internet access	Needed to install dependencies and download a speech model. Later operation can be offline once the selected model is cached.
Remote use	SSH access to the inference computer. Each producer opens the capture page through a local SSH tunnel.

A GPU is optional. No system FFmpeg installation is needed for normal use. This is a local/trusted-producer service, not a public website with user accounts.

Install and start

Download the latest release and extract it, or use Git:

git clone https://github.com/steveseguin/caption-local.git
cd caption-local

Run the following commands inside the extracted or cloned project folder. Choose one installation method.

Option 1: Linux script

./start.sh

The script creates a local Python environment, installs dependencies and downloads the default small speech model. If Python's venv support is missing, follow the Linux setup instructions.

Wait for the server to report that it is ready, then open http://localhost:8765. Keep the terminal open while using captions. Press Ctrl+C to stop the server.

Option 2: Docker

docker compose up --build -d
docker compose logs -f

The first build and model download can take several minutes. Open http://localhost:8765 when docker compose ps shows the service is healthy. Ctrl+C exits the log view; the service continues running in the background.

docker compose down

This stops the service and keeps downloaded models for next time. Do not add -v unless you intend to delete the model cache. No microphone passthrough is needed.

Other installation options

Option	Instructions
Windows script — not hardware-validated	Run `.\start.ps1`, or `py -3.12 deploy.py run`. Windows and native setup
NVIDIA GPU — not hardware-validated	GPU requirements and Docker/native commands
Linux server, microphone on another computer	SSH tunnel guide
Install with an AI assistant	Copy-and-paste setup prompt and deployment skill
Offline use or automatic restart	Operations guide

Your first captions

Open the local capture page and select your microphone and spoken language.
Choose transcription, English translation, or both. Start with the default small model, especially for multilingual use.
Click Start captions, speak a few sentences, and check the text and microphone meter.
To use human review, follow the caption.ninja editor walkthrough. Use a separate editor source room for each producer.
Click Stop, wait for pending speech to finish, and download the transcript if you want to keep it.

The first-event guide covers rehearsal, audio routing, producer/editor roles, and what to do if captions fall behind.

Several microphones or producers

Each capture tab has its own microphone selection, transcript, retry buffer and stream ID. Independent producers can connect to the same inference service through separate SSH tunnels. The service admits up to twelve stream sessions by default; your CPU, chosen model and translation workload determine how many keep up.

For English throughput on a six-core CPU, an explicit faster preset is available:

./start.sh --model base --workers 6 --threads 1 --beam-size 1
# Or use Docker:
docker compose -f compose.yaml -f compose.cpu-throughput.yaml up --build -d

This preset passed twelve simultaneous English browser captures, but failed the Spanish quality checks. The normal small default passes those checks and needs more processing time. Use fewer streams for multilingual work on this CPU, or validate stronger hardware. Do not switch models during a live event without rehearsal.

Capacity results explain response times, accuracy, queue limits and the failed tests that informed this preset.

Guides and help

I want to…	Read
Prepare an event and use the human editor	First event
Install, configure hardware, or connect remotely	Deployment
Fix a problem	Troubleshooting
Run unattended, upgrade, or roll back	Operations
Ask an AI assistant to install it	AI setup
Send audio from my own application	HTTP audio API
Check exactly what was tested	Version 1.1 validation
Report a bug or contribute accessibility improvements	Contributing

Use GitHub issues for bugs and feature requests. Reproduce with a short, shareable example when possible; do not post private event audio, credentials, or caption room names.

License

MPL-2.0. The included WebSocket publisher comes from caption.ninja. Dependencies and model weights retain their own licenses. Model weights and test audio are downloaded separately and are not bundled in source releases.