TinySteno

Inspired by StenoAI. Minimal meeting recorder with Obsidian export (local filesystem vault).

Features

• Record meetings (indefinite duration, Ctrl+C to stop)
• Captures mic + system audio simultaneously — no virtual audio device required
• Transcribe with faster-whisper (local, runs on CPU)
• Summarize via OpenAI-compatible API (Ollama, OpenAI, OpenRouter, etc.)
• Handles long transcripts automatically — splits into chunks, summarizes each, then merges
• Persona system — choose how recordings are summarized and formatted
• Auto-generate note titles and tags from content
• Export structured markdown notes to Obsidian vault

Prerequisites

• Python 3.12+
• UV
• An OpenAI Compatible API
  • Ollama
  • LMStudio
  • OpenRouter
  • MORE!

Installation

UV:

uv tool install git+https://github.com/onyxhat/TinySteno

Manually:

git clone https://github.com/onyxhat/TinySteno.git
cd TinySteno
uv sync
uv run tinysteno --help

Usage

# Update config interactively (also seeds built-in personas to ~/.tinysteno/personas/)
tinysteno setup

# Reset built-in personas to defaults (overwrites any edits you've made to them)
tinysteno setup --reset-personas

# Record a meeting (uses default persona)
tinysteno record

# Record with custom name
tinysteno record --name "Budget Review"

# Record using a specific persona
tinysteno record --persona rca

# Process existing audio file
tinysteno process recordings/Meeting.wav

# Process with a specific persona
tinysteno process recordings/Meeting.wav --persona executive-summary

# Verify setup
tinysteno test

# List processed meetings
tinysteno list --vault /path/to/vault

# Show current config
tinysteno config

# Edit config in $EDITOR
tinysteno config --edit

Personas

Personas control what the LLM extracts from a transcript and how the Obsidian note is rendered. Each persona defines a system prompt, an output schema, and a Jinja2 note template.

Built-in personas

Built-in personas are seeded to ~/.tinysteno/personas/ on first run, so you can inspect and edit them directly. To restore them to their defaults, run tinysteno setup --reset-personas.

Slug	Name	Use for
default	Meeting Summary	General meetings — overview, participants, key points, action items
rca	Root Cause Analysis	Postmortems — timeline, root cause, contributing factors, corrective actions
irm	Incident Response & Management	Incident calls — severity, impact, responders, mitigations, follow-ups
sprint	Sprint Ceremony	Planning/review/retros — completed work, blockers, retrospective notes
kickoff	Project Kickoff	Project kickoffs — objectives, stakeholders, scope, risks, decisions
executive-summary	Executive Summary	Concise summaries — key decisions, risks, asks

Custom personas

Drop a directory into ~/.tinysteno/personas/<slug>/ with three files:

~/.tinysteno/personas/
└── my-persona/
    ├── persona.yaml      # name, description, schema
    ├── system_prompt.md  # LLM system message
    └── template.md       # Jinja2 Obsidian note template

persona.yaml example:

name: My Persona
description: What this persona does.
schema:
  summary:
    type: string
    description: A brief summary
  highlights:
    type: list
    description: Key highlights as strings

Schema field types are string or list (list of strings). Field names must be valid Python identifiers and cannot collide with reserved metadata variables: title, date, duration, transcript, detected_language, generated_tags.

template.md receives all schema fields plus the metadata variables above as Jinja2 context.

Configuration

Config file: ~/.tinysteno/config.yaml

Run tinysteno setup to create it interactively, or create it manually:

# Obsidian vault path (~ is expanded)
obsidian_vault: "~/Obsidian/Vault"

# Output settings
output_folder: "meetings"   # subfolder inside vault

# Where to store raw audio recordings
# Defaults to <obsidian_vault>/<output_folder>/audio if not set
# recordings_path: "~/recordings"

# Persona to use when --persona is not specified
persona: "default"

# OpenAI-compatible API settings
# Ollama (local):  base_url: "http://localhost:11434/v1", api_key: "ollama"
# OpenAI (cloud):  base_url: "https://api.openai.com/v1", api_key: "sk-..."
api_key: "ollama"
base_url: "http://localhost:11434/v1"
model: "llama3.2:3b"
auto_title: true            # generate note titles from content
auto_tags: true             # generate tags from content

# Transcription
# Model sizes (speed ↔ accuracy): tiny · base · small · medium · large
whisper_model: "small"

# Feature flags
diarization: false          # enable [You]/[Others] speaker labels
                            # when system audio is captured, output is automatically
                            # stereo (L=mic, R=system audio) and diarization will work

# Audio recording
sample_rate: 44100
channels: 1                 # mic input channels; output is automatically stereo
                            # when system audio loopback is available

Output Format

Note format is controlled by the active persona's template. The default persona produces:

---
created: 2024-01-15 14:30
type: meeting
tags: [meeting, budget, planning]
duration: 00:45:12
participants: Alice, Bob
---

# Budget Review

## Overview
AI-generated summary of the meeting.

## Participants
- Alice
- Bob

## Key Points
1. Point one
2. Point two

## Action Items
- [ ] Follow up with the team (Alice)

## Transcript
\```
Full transcript text here...
\```

When diarization is enabled and system audio is captured, the transcript is interleaved chronologically with speaker labels:

[You] I think we should go with option A.
[Others] That makes sense, let's proceed.
[You] Great, I'll follow up with the team.

Requirements

• Python 3.12+
• Optional: Ollama or OpenAI API key

macOS — System Audio Capture

TinySteno captures system audio via ScreenCaptureKit (macOS 12.3+). To enable it:

1. Open System Settings → Privacy & Security → Screen Recording
2. Enable permission for your terminal application (e.g. Terminal, iTerm2, Ghostty)

Without this permission, only the microphone will be recorded.

License

MIT