Metadata-Version: 2.5
Name: voxint
Version: 0.23.1
Summary: From sound to intelligence: end-to-end transcription, diarization, and speaker identity with human-grade quality gates.
Project-URL: Repository, https://github.com/bengizmo/voxint
Project-URL: Issues, https://github.com/bengizmo/voxint/issues
Project-URL: Documentation, https://github.com/bengizmo/voxint/tree/main/docs
Author: Voxint contributors
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Keywords: asr,diarization,pyannote,speaker-identification,transcription,whisper
Classifier: Development Status :: 2 - Pre-Alpha
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Classifier: Programming Language :: Python :: 3.11
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Description-Content-Type: text/markdown

# Voxint

**Turn any recording into a speaker-labelled transcript, on your own
computer.** Voxint transcribes your audio or video, works out who spoke when,
and gives you a simple review screen to confirm the speakers and fix the wording
before you export.

It is built for individuals and small teams (researchers, journalists,
educators) who need their recordings to stay local. **Your audio is transcribed
on your own hardware by default:** no cloud account, no per-minute fees, nothing
uploaded. (Two optional features *do* reach the network when you turn them on:
fetching a recording from a URL, and sending transcript text to an outside AI
model to polish it. Both are off or opt-in, and clearly labelled.)

> **Early days (pre-alpha).** Voxint works end to end, but it is young software.
> The interface, database, and settings can still change between releases through
> the 0.x series. Great for hands-on use and feedback; not yet for
> mission-critical archives.

![Reviewing a transcript in Voxint: a waveform strip showing who spoke when, above the transcript with a verify-and-advance review loop](docs/images/transcript-review.png)

## See it in action

*(All screenshots use a small synthetic three-speaker sample that ships with
Voxint.)*

| | |
|---|---|
| ![The adjudication queue: completed runs with voices still needing a decision](docs/images/review-queue.png) | ![The workbench: each voice shows its evidence: a confident match to accept, a heard name that is only a guess, or no name at all](docs/images/review-workbench.png) |
| **Your review queue**: completed recordings waiting for your decisions. | **Attribute each voice**: accept a confident match, judge a heard name, or leave a voice unknown. |
| ![The guided setup wizard's readiness checks, each dependency shown as ready, failed, or unverified](docs/images/setup-wizard.png) | ![The dashboard: run health, throughput, roster size, and per-stage timing](docs/images/dashboard.png) |
| **Guided setup in the browser**: honest readiness checks, plain-language fixes. | **A dashboard** for run health and throughput at a glance. |

## What it does

Voxint takes a recording and walks it through four steps:

1. **Add your recording**: upload it in the browser, paste a URL, or point
   Voxint at a file it can already see.
2. **Voxint does the heavy lifting**: it transcribes the words and works out
   who spoke when, then suggests who each voice is.
3. **You review**: confirm each speaker, and fix any wording, in a screen built
   for exactly this. Machine guesses stay separate from your decisions; you
   always have the final say.
4. **Read or export**: read a finished transcript on screen, or download a clean,
   speaker-labelled copy (plain text, Markdown, subtitles, or structured data).

Once you have a few transcripts, you can also **search across all of them by
meaning**, not only by the exact words. Type what you are looking for and Voxint
finds the closest passages from every recording, each with a link straight to
that spot. This search runs on your own hardware too, with no outside AI.

The models all run **locally**: transcription (Whisper), speaker separation
(pyannote), and voice identity (TitaNet). Everything they need is bundled into
Voxint, so there is **no Hugging Face account or token** to set up.

## Quickstart

You need **[Docker](https://docs.docker.com/get-started/get-docker/) with the
Compose plugin (≥ 2.24)**. One command takes a fresh copy to a running console:

```bash
git clone https://github.com/bengizmo/voxint.git && cd voxint
./scripts/install.sh
```

The installer asks only for what it can't invent (an admin password, a folder
for your media, and which hardware runs the models), then generates everything
else, starts Voxint, waits until it is healthy, and prints the console address.

> **On an Apple Silicon Mac and would rather not install Docker Desktop?** A
> docker-free **native preview** runs the whole stack under macOS's own service
> manager instead. It is a macOS-only technical preview (a few shell commands,
> not the one-command install above), so read
> [docs/native-macos-preview.md](docs/native-macos-preview.md) if that is you.
> Every other install path, on any operating system, needs Docker.

> **No graphics card? That's fine.** Voxint runs the whole pipeline on an
> ordinary computer's CPU (needs roughly **8 GB of memory** free). It is slower,
> and a long recording can take hours rather than minutes, but it works anywhere. A
> GPU (NVIDIA, AMD, or an Apple Silicon Mac) just makes it faster.

> **Have a GPU? How much VRAM you need.** The transcription suite (Whisper +
> pyannote + TitaNet) shares one card and fits comfortably on **8 GB** (e.g. RTX
> 3050/3060 Ti/4060). Turning on the optional bundled local LLM adds ~5 GB, so
> running everything on one card wants **12 GB** (e.g. RTX 3060 12 GB) or more.
> AMD cards work via the ROCm tier. Full breakdown and card examples →
> [docs/setup.md](docs/setup.md#nvidia-gpu--the-fast-path).

When it finishes, open the console at **`http://127.0.0.1:8080/`** and sign in
with the username and password you set. On a fresh install Voxint walks you
through a short in-browser **setup wizard** and an optional **guided tutorial**
on the bundled sample, so you see the whole review loop before pointing it at
your own audio.

**Full setup for your operating system and hardware → [docs/setup.md](docs/setup.md).**
First-run walkthrough → [docs/onboarding.md](docs/onboarding.md).

## Using Voxint

Once it is running, these short guides cover the day-to-day tasks:

- **[Add media & manage runs](docs/how-to/add-media-and-manage-runs.md)**:
  upload a file, paste a URL, or watch a folder; follow a run and requeue,
  cancel, or archive it.
- **[Review & adjudicate](docs/how-to/reviewing-and-adjudicating.md)**:
  confirm speakers, correct the transcript, keyboard shortcuts, the waveform,
  splitting and reassigning segments.
- **[Manage speakers & export](docs/how-to/managing-speakers-and-exporting.md)**:
  the speaker roster and the export formats.
- **[Settings & troubleshooting](docs/how-to/settings-and-troubleshooting.md)**:
  configure everything from the browser, and fix common problems.

## A little more depth

You don't need any of this to use Voxint, but if you're curious or evaluating it:

- **Nothing is lost to a crash.** Every run's progress lives in a database, so a
  restart resumes where it left off, and pausing for review is just a saved row.
- **Speaker identity keeps a paper trail.** Voxint matches voices against a
  roster that grows as you use it, and keeps machine proposals strictly separate
  from your rulings.
- **Optional AI polish.** Voxint can send transcript text to any
  OpenAI-compatible model to tidy it up and suggest names. It is off by default,
  and a slow or failing model never blocks a run.
- **Swappable vocabulary.** Names, jargon, and prompts load from a *domain pack*
  you can pick per folder, so specialist terms transcribe correctly. See
  [docs/domain-packs.md](docs/domain-packs.md).
- **Held to measured gates.** The models are pinned and their outputs are held to
  measured-equivalence gates, so an upgrade can't quietly change results. See
  [docs/gpu-contracts.md](docs/gpu-contracts.md).

## For developers

The console is server-rendered (FastAPI + Jinja + htmx) with a few small React
"islands"; the model services are separate containers behind versioned HTTP
contracts. To run the code you checked out instead of the release images, layer
the build overlays; to work without Docker at all:

```bash
uv sync --extra dev
uv run pytest tests/unit
uv run uvicorn voxint.api.app:app --reload
```

There is also a standalone, database-free scoring harness. `pip install voxint`
gives you the `voxint score` CLI for speaker-attribution metrics (see
[`examples/`](examples/README.md)). Architecture, contracts, operations, and the
release process are documented under **[docs/](docs/README.md)**.

## License

Apache-2.0. See [LICENSE](LICENSE) and [NOTICE](NOTICE). Vendored model weights
are redistributed under their own licenses with attribution (titanet:
CC-BY-4.0; pyannote segmentation: MIT; WeSpeaker embedding: CC-BY-4.0). See the
provenance files under `services/*/models/` and the model-asset releases.
