Work with YazSes: students, researchers, industry¶

YazSes is a production dictation tool and an open research platform for voice-first human-computer interaction. Everything runs on-device, every subsystem sits behind a pluggable seam, and the project is small enough that one person can still understand the whole pipeline — which makes it unusually good material for coursework, theses, papers, and pilots.

This page maps what the project offers to each audience. Questions and proposals are welcome in GitHub Discussions — the Research Corner threads are the standing venue for scientific discussion.

flowchart TD
    Q{What do you want<br/>to walk away with?} --> A[A graded project<br/>or a thesis]
    Q --> B[A publishable<br/>measurement]
    Q --> C[A deployment<br/>in my organisation]
    A --> A1["Pick from the eight scoped projects below<br/>— each has an open issue and an evaluation"]
    B --> B1["Take an open research question<br/>from the survey pages"]
    C --> C1["Read the privacy statement and<br/>Meeting Mode, then open a Discussion"]
    A1 --> D[Open a Discussion to claim it]
    B1 --> D
    C1 --> D
    D --> E["Fast review · co-authorship where earned ·<br/>your name in the release notes"]

Why YazSes works as a research platform¶

The architecture separates every scientifically interesting component behind a small interface, so an experiment replaces one box without touching the rest:

Two properties matter for studies: everything is offline (no cloud confound, works in a lab without agreements with a third-party processor), and the opt-in learning corpus is encrypted on-device, which is the right starting point for an ethics/IRB conversation rather than an obstacle.

For students: project-sized problems¶

Each of these is a real, wanted contribution with an open issue, a defined evaluation, and a maintainer who reviews quickly. They are sized between a course project and a bachelor/master thesis:

Supervisors: if you want to run one of these as a course assignment or thesis, open a Discussion — scoping a variant for your semester is exactly the conversation we want to have.

For researchers: evaluation, citation, collaboration¶

• Cite the project with the repository's CITATION.cff (GitHub's Cite this repository button), which also references the companion preprint (arXiv:2607.28878) — the BibTeX entry is on the research index.
• Reproducibility: pinned Python dependencies, CPU-int8 inference, offline models — a described configuration re-runs bit-for-bit on commodity hardware. The benchmark harness (#72) is the intended home for shared methodology.
• Study directions already grounded in the codebase: multimodal input (gaze + voice + EMG), personal VAD and the cocktail-party problem on short windows, code-switching dictation (Polyglot), privacy-preserving on-device personalization (encrypted corpus + local fine-tuning), and dictation UX for people with RSI or dysfluent speech.
• The research section surveys the literature behind the post-keyboard input roadmap (eye control, voice control, muscle & brain interfaces).

For industry: private dictation you can actually deploy¶

• Nothing leaves the machine — see the privacy statement. No per-seat cloud subscription, no audio retention, no data-processing agreement needed for the dictation path.
• Meeting transcription on-premises — Meeting Mode produces speaker-labelled transcripts and minutes without audio ever reaching a third party.
• Scriptable deployment — plain TOML config, a CLI for everything, systemd/launchd/SCM lifecycle, and (coming) a container image for the headless transcription pipeline (#76).
• Pilots and integration questions: open a Discussion or contact the maintainer (see the repository profile). Honest expectations: this is an open-source project, not a vendor with an SLA — what you get is source access, fast issue turnaround, and a say in the roadmap.

For everyone: the community rhythm¶

• Research Corner (Discussions) — a recurring scientific thread: one paper, question, or experiment per thread, tied where possible to something measurable in YazSes. Bring results, not just links.
• Contributor lanes — the pinned issue groups every open task by interest: GUI, packaging, speech/ML, voice-control features, docs and no-code testing.
• Benchmarks — add a row for your machine once the harness lands.