TalaX

Adaptive dictation for developers. Local Whisper transcription with a 3-layer correction pipeline that improves as you use it.

Built by PureTensor Inc.

Tala -- Icelandic for "to speak."

• Learns your vocabulary -- corrections are persisted to SQLite; after 3 consistent fixes for the same word, corrections auto-apply
• 3-layer correction pipeline -- dictionary substitution (<1ms), n-gram context scoring (<50ms), heuristic expansion (<10ms)
• Fully local -- whisper.cpp via whisper-rs, no audio leaves your machine, no subscription
• Voice profiles -- separate correction databases per context (work, personal, project-specific)

Development Status

TalaX is functional but early-stage. Here is what works today and what is still in progress.

Working:

• Local Whisper transcription (whisper.cpp via whisper-rs, CPU-only)
• Push-to-talk recording with energy-based VAD and pre-roll buffer
• L1 Dictionary correction -- regex substitution from learned patterns, auto-apply after 3 consistent fixes
• L3 Heuristic correction -- Levenshtein fuzzy matching, Double Metaphone phonetic matching, compound word detection, acronym restoration, number normalization
• SQLite persistence -- sessions, segments, and correction patterns survive app restarts
• Voice profiles with independent correction databases
• Text injection via clipboard paste or keystroke simulation
• Model download with progress tracking and integrity verification
• Tauri v2 desktop app with Svelte 5 frontend (7 views)

Working but cold-start dependent:

• L2 N-gram correction -- the interpolated trigram model is fully implemented (training, scoring, save/load, apply), but it only activates after you have reviewed and corrected enough transcriptions to build a meaningful language model. On a fresh profile, L2 is inert.
• The correction feedback loop is wired end-to-end: frontend edit -> save_corrections IPC -> word-level diff -> pattern extraction -> auto-apply flag rebuild -> pipeline reload (including n-gram retrain). This works within and across app sessions. However, effectiveness scales with usage -- a fresh install has no training data.

Planned / not yet implemented:

• Cross-profile pattern sharing
• Audio excerpt extraction for fine-tuning
• Batch correction review workflows
• Platform-specific packaging and distribution

Architecture

Tauri v2 (Svelte 5 frontend, Rust backend)
  |
  +-- whisper.cpp (whisper-rs) -- local STT, CPU-only
  |     tiny.en bundled, small.en-q5_1 recommended
  |
  +-- 3-Layer Correction Pipeline
  |     L1: Dictionary    -- regex substitution from learned patterns
  |     L2: N-gram        -- interpolated trigram model (0.6/0.3/0.1)
  |     L3: Heuristic     -- Levenshtein, Double Metaphone, compounds
  |
  +-- SQLite (WAL) -- corrections, sessions, learning loop
  |
  +-- System Integration
        Global hotkey (rdev) -- push-to-talk
        Text injection (arboard + enigo) -- clipboard or keystroke
        Audio capture (cpal) -- 16kHz mono with energy VAD
        System tray -- recording indicator

How It Works

1. Hold the hotkey (default: Ctrl+Shift+Space)
2. Speak naturally
3. Release
4. TalaX transcribes locally, runs corrections, and injects text into your active app

When you review and correct a transcription, the diff is extracted at the word level and stored as correction patterns. Patterns that recur 3+ times with high confidence are promoted to auto-apply. The n-gram model retrains on your reviewed corpus each time the pipeline reloads, improving context-aware corrections over time.

Correction Pipeline

Layer 1 -- Dictionary: Exact regex substitutions from learned patterns. Longest-match-first ordering, automatic case preservation.

Layer 2 -- N-gram: Interpolated trigram model (0.6 tri + 0.3 bi + 0.1 uni) trained on your reviewed correction history. Flags low-probability words given their context. Starts inert on a fresh profile and activates as you build up reviewed transcriptions.

Layer 3 -- Heuristic Expander: Catches what the first two layers miss:

• Fuzzy matching (Levenshtein distance <= 2)
• Phonetic matching (Double Metaphone)
• Compound word detection (split/join)
• Acronym restoration (lowercase to uppercase)
• Number normalization ("v two" to "v2", "node zero" to "node0")

Voice Profiles

Separate correction databases per context. Switch freely between profiles. Config, profiles, and models now live under the platform-managed Tauri config/data directories rather than a hard-coded path.

<app-config-dir>/
    config.toml

<app-data-dir>/
    profiles/
        default/
            corrections.db
            ngram.bin
            domain_context.json
            profile.toml
        work-devops/
            ...
    models/
        ggml-small.en-q5_1.bin
        ...

Whisper Models

Downloaded from HuggingFace on first use with progress tracking and integrity verification.

Development

Prerequisites

• Rust (stable, edition 2024)
• Node.js 24+ recommended
• Linux system libraries: libasound2-dev, libx11-dev, libxtst-dev, cmake, pkg-config

Build and Run

git clone https://github.com/puretensor/talax-dictation.git
cd talax-dictation
cd ui && npm install && cd ..

# Development mode
cargo tauri dev

# Engine tests
cargo test -p talax-engine

# Type-check frontend
cd ui && npx svelte-check

Test Coverage

The engine test suite currently includes 137 unit tests, 34 integration tests, and doctests. Coverage focuses on:

Release Validation

• Generated Tauri schema output under crates/talax-app/gen/ is treated as disposable generated output and is not committed.
• Before packaging a release, run cargo test -p talax-engine, cd ui && npm run check, and a manual desktop smoke test on the target platform.

Tech Stack

Project Structure

talax/
    Cargo.toml                   # Workspace: talax-engine + talax-app
    crates/
        talax-engine/         # Core library (no UI dependency)
            src/
                audio/           # cpal capture, VAD, ring buffer
                db/              # SQLite schema, corrections, sessions
                hotkey/          # Global hotkey detection
                inject/          # Text injection
                pipeline/        # dict_corrector, ngram, heuristic expander
                profile/         # Voice profile management
                whisper/         # Transcriber + model manager
        talax-app/            # Tauri v2 application
            src/
                commands.rs      # 21 IPC command handlers
                recording.rs     # Recording state machine
                tray.rs          # System tray
    ui/                          # Svelte 5 frontend

License

Business Source License 1.1. Copyright 2026 PureTensor Inc. Converts to Apache 2.0 on 2030-03-28. See LICENSE.