Tip

AI-Assisted Install: Just tell any AI assistant:
Setupdictaskfromhttps://github.com/hyperpolymath/dictask
The AI reads the manifest, asks you a few questions, and handles everything.

Overview

dictask automates the ingestion of speech notes from a portable voice recorder into a structured, prioritised, auditable task list.

Plug in your recorder → dictask detects it, archives the audio, transcribes it, extracts tasks with confidence scores, deduplicates against your existing list, and either auto-applies high-confidence items or queues ambiguous ones for review.

This project follows a Dual-Track architecture:

• Root: High-level orientation and rich documentation for humans.

• Sub-directories: Machine-readable metadata and technical implementation.

Architecture

Pipeline (10 stages, batch)

Recorder USB
→ [1] udev/systemd detect
→ [2] Local archive (SHA-256 checksummed)
→ [3] Encrypted cloud backup (retry 3x / quarantine)
→ [4] Whisper/Vosk transcription → transcript JSON
→ [5] Haskell megaparsec NLP → candidate intents (with confidence)
→ [6] Normalise (resolve dates, assign priority scores)
→ [7] Deduplicate (exact + semantic matching)
→ [8] Review queue (low confidence) or auto-apply (high confidence)
→ [9] Canonical SQLite store → views (Markdown, JSON, CSV)
→ [10] Notify (dashboard / email alerts for review items)

Components

Component	Language/Tool	Purpose
Ingest	Rust	Detect recorder insertion (udev), archive audio, compute checksums, upload encrypted backup.
Transcription	Rust (whisper-rs / vosk)	Convert audio to text using offline ASR. No cloud dependency.
Task Parser	Haskell (megaparsec)	Extract tasks, deadlines, priorities from transcripts. Pure functions, idempotent.
Canonical Store	Rust + SQLite	Audited, versioned task database. WAL mode. JSON1 for structured fields.
Review System	Rust	Review queue for low-confidence items. Dashboard alerts.
ABI / Schema Proofs	Idris2	Dependent-type proofs for task schema correctness and confidence thresholds.
FFI Bridge	Zig	C-compatible bridge between Idris2 ABI and Rust components.
Deployment	Ansible + Terraform	Local machine setup (Ansible) + cloud provisioning (Terraform).

Task Schema

-- Haskell type (src/parse/)dataTask=Task{taskId::UUID
, title::Text
, description::MaybeText
, sourceAudioId::AudioHash-- SHA-256 of source recording
, sourceTranscriptId::TranscriptId-- reference to transcript version
, createdAt::UTCTime
, updatedAt::UTCTime
, status::TaskStatus-- Pending | InProgress | Done | ReviewNeeded
, priorityScore::PriorityScore-- urgency * 0.5 + importance * 0.3 + deadline_proximity * 0.2
, dueDate::MaybeDay
, tags:: [Text]
, project::MaybeText
, supersedesTaskId::MaybeUUID-- links to replaced task
, duplicateOfTaskId::MaybeUUID-- links to canonical duplicate
, reviewState::ReviewState-- Approved | PendingReview | Rejected
, confidence::Confidence-- 0.0–1.0, from parser
, parserVersion::Version-- which parser version produced this}

Policies

Confidence & Automation

Confidence	Threshold	Action
High	>= 0.8	Auto-apply to canonical store
Medium	0.3–0.8	Queue for human review
Low	< 0.3	Log only, do not create task candidate

Human Review Boundaries

Actions that always require confirmation:

• Deletions of existing tasks

• Deadline changes on existing tasks

• Low-confidence merges (semantic deduplication)

• Any update to a task marked Approved

Deduplication

• Exact match: Same title + same project → auto-merge

• Semantic duplicate: Similar intent, different wording → flag for review

• Recurring task: Same task pattern across recordings → link to parent with supersedesTaskId

Privacy & Retention

• Raw audio: Encrypted at rest, retained 30 days locally, cloud backup encrypted

• Transcripts: Stored locally only, redacted if sensitive content detected

• Cloud backups: Encrypted, configurable retention

• Secrets: Managed via rokur (Stapeln), never in code or repo

Audit Trail

Every task carries its full provenance chain:

original_audio_hash (SHA-256)
→ transcript_version (Whisper v3 / Vosk v0.3.45)
→ parser_version (dictask-parse v0.1.0)
→ change_set { timestamp, action, user_confirmation_state }

All transformations are logged for reproducibility. Reprocessing the same audio file with the same parser version MUST produce identical candidate tasks (idempotency).

Failure & Recovery

Failure	Handling
Failed cloud upload	Retry 3x with exponential backoff, then quarantine locally
Partial transcription	Flag for review, log incomplete segments
Corrupted audio file	Skip, log with checksum, alert user
Low-confidence parse	Route to review queue, never auto-apply
Dedup false positive	Show both candidates in review queue

All pipeline stages are idempotent — reprocessing the same input produces the same output.

Repository Structure

Directory	Purpose
.github/	Forge-specific metadata (CODEOWNERS, SECURITY.md, workflows).
.machine_readable/	Canonical project state (6 a2ml files), bot directives, and AI guides.
src/ingest/	Rust crate: udev detection, file archival, cloud upload.
src/transcribe/	Rust crate: Whisper/Vosk ASR integration.
src/parse/	Haskell package: megaparsec NLP task extraction.
src/store/	Rust crate: SQLite canonical store + view generation.
src/review/	Rust crate: review queue + notification.
src/interface/	Verified Interface Seams (Idris2 ABI, Zig FFI, generated C headers).
deploy/ansible/	Ansible playbooks for local machine setup.
deploy/terraform/	Terraform configs for cloud provisioning (bucket, IAM).
schemas/	SQLite schema, JSON schemas for intermediate formats.
container/	Stapeln container ecosystem.
docs/	Technical documentation (architecture, theory, practice).

Quick Start

just init # Interactive bootstrap
just build # Build all components
just test# Run all tests
just ingest # Run ingest pipeline manually
just transcribe FILE # Transcribe a specific audio file
just parse FILE # Parse a transcript file
just views # Generate Markdown/JSON/CSV views
just container-build # Build verified OCI image

Deployment

Local Setup (Ansible)

cd deploy/ansible
ansible-playbook setup.yml

Sets up: udev rules, systemd service, Rust/Haskell toolchains, SQLite.

Cloud Provisioning (Terraform)

cd deploy/terraform
terraform init
terraform apply

Provisions: encrypted storage bucket, IAM roles, lifecycle rules.

Testing

• Haskell parser: HSpec + QuickCheck property-based tests

• Rust components:cargotest with integration tests against real SQLite

• Pipeline replay: Re-run historical audio through newer parser versions

• Idris2 proofs: Compile-time verification (no runtime tests needed)

Success Metrics

• % of tasks auto-processed vs. requiring manual review

• False positive/negative rates for intent detection

• Time saved vs. manual note-taking and task entry

• Pipeline end-to-end latency (target: < 5 minutes per recording)

Documentation

• Contributing Guide

• Roadmap

• Architecture Topology

• Architecture Details

License

SPDX-License-Identifier: CC-BY-SA-4.0
See LICENSE and docs/legal/ for details.