Local-first AI-powered transcription and clip discovery for interview-driven workflows.
Documentary films, corporate video, podcasts, news, legal depositions, customer testimonials, training content. If someone is talking on camera and you need to find the best moments, this tool does the heavy lifting.
Drop in footage, transcribe it, run AI analysis to surface story beats and soundbites, highlight and organize selects with color labels, and export pre-cut timelines directly to Final Cut Pro. Everything runs on your machine. Nothing uploads. Nothing leaves.
I'm a documentary filmmaker and I needed a way to find story beats and soundbites across hours of interview footage without uploading client material to cloud services. Existing tools were either too expensive, too slow, or required sending sensitive footage to third-party servers. So I built something that runs entirely on my Mac, uses AI locally, and exports directly to my Final Cut Pro timeline.
Transcription
- Drag and drop video/audio files (MP4, MOV, WAV, MP3, MXF, etc.)
- Transcribes locally with OpenAI Whisper — no cloud uploads
- Word-level timestamps for precise sync
- Click speaker names to assign who said what
Transcript Viewer
- Clean paragraph layout grouped by speaker
- Video player synced to transcript with word-level highlighting
- Click any word to jump to that moment
- Color highlighter — drag across words to create clips (like highlighting in a document)
- 5 renamable color labels for organizing selects
Clip Library
- All highlights collected in a visual grid
- Each clip has play/pause, scrub bar, duration, and transcript excerpt
- Checkbox select for batch export
- Add clips from transcript, AI analysis, or AI chat
AI Analysis (powered by Ollama — free, local)
- Story structure with beats (hook, context, rising action, climax, resolution)
- Social media clip suggestions with platform recommendations
- Strongest soundbites identified
- Every item has play/scrub controls and one-click "Add to Clips"
AI Chat
- Conversational AI that knows your transcript
- Ask for clips, themes, story angles, soundbites
- AI suggests clips with timecodes — play them instantly or add to your library
- Follow-up questions maintain context
FCPX Export
- Pre-cut timeline — each clip becomes an actual edit referencing your source media
- Import the .fcpxml and your selects are ready to review in Final Cut Pro
- Keyword ranges on source clip for browser filtering
- Also exports SRT subtitles, plain text, and JSON
Client Sharing
- One-click Cloudflare Tunnel generates a public URL
- Clients see the full project: transcript, player, highlighting tools
- No destructive controls exposed — clients can highlight and listen
- No accounts or signups needed
Project Organization
- Folder system for organizing by client
- Rename, move, clear, delete projects
- Multi-project workspace — combine interviews in one view
Dark / Light Theme
- Toggle between dark and light mode
- Persists across sessions
Download Doza Assist (macOS)
- Download the
.dmgfile from the link above - Open it and drag Doza Assist to your Applications folder
- Double-click to launch
First launch: macOS may block the app. Go to System Settings > Privacy & Security, scroll down, and click "Open Anyway" next to the Doza Assist message. This only happens once.
On first launch, the app will automatically install everything it needs. You may be asked for your Mac password once during setup. The AI model download (~3-5 GB) takes a few minutes — the app shows progress the whole time.
That's it. No Terminal required.
If you prefer to run from source:
- macOS (tested on Mac Studio M2)
- Python 3.11+
- ffmpeg (
brew install ffmpeg) - Ollama for AI features (
https://ollama.com)
git clone https://github.com/DozaVisuals/doza-assist.git
cd doza-assist
# Create virtual environment
python3 -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
# Install ffmpeg if you don't have it
brew install ffmpeg./start.shLocal with Ollama (free, private — recommended):
# Install and start Ollama
brew install ollama
ollama serve
# Pull a model (gemma4 recommended)
ollama pull gemma4Cloud with Claude API (higher quality, optional): If you want better AI analysis quality, you can use Anthropic's Claude API as an alternative backend. Transcription still runs locally — only the AI analysis and chat use the API.
export ANTHROPIC_API_KEY=sk-ant-your-key-here
# Add to ~/.zshrc to persistThe app automatically tries Ollama first and falls back to Claude if configured.
- Add a file — Paste a path, browse, or drag a video/audio file
- Transcribe — Click "Transcribe" to process locally with Whisper
- Assign speakers — Click speaker names in the transcript to toggle between speakers
- Highlight clips — Select a color and drag across words to mark selects
- Discover with AI — Run AI Analysis or ask the Chat for clips and story structure
- Export to FCPX — Export pre-cut timeline with your clips as edits on the timeline
- Share with clients — Click Share to generate a public link for client review
- Backend: Python / Flask
- Frontend: Vanilla JS, CSS custom properties
- Transcription: OpenAI Whisper (local, runs on CPU)
- AI: Ollama with Gemma 4 (local, free) or Claude API (optional)
- Audio: ffmpeg for extraction
- Sharing: Cloudflare Tunnel (free, no account needed)
- Export: FCPXML 1.11 with asset references
- Storage: JSON files per project (no database)
doza-assist/
├── app.py # Flask server + all routes
├── transcribe.py # Whisper transcription engine
├── ai_analysis.py # AI analysis + chat (Ollama/Claude)
├── fcpxml_export.py # FCPXML generation with pre-cut timelines
├── start.sh # Launch script (developer mode)
├── install.sh # Manual setup (developer mode)
├── setup_runner.sh # Auto-setup phase 1 (Xcode CLT, Homebrew, Python)
├── setup_assistant.py # Auto-setup phase 2 (browser UI for remaining deps)
├── dep_check.sh # Quick dependency checker for app launches
├── build_launcher.sh # Builds .app bundle + .dmg
├── requirements.txt # Python dependencies
├── static/
│ └── style.css # All styles (dark + light themes)
├── templates/
│ ├── dashboard.html # Projects page with folders
│ ├── project.html # Main project view (all tabs)
│ └── ...
├── projects/ # User data (gitignored)
└── exports/ # FCPXML exports (gitignored)
- All transcription runs locally on your machine
- AI analysis uses Ollama (local) by default — nothing leaves your computer
- Audio/video files are never uploaded anywhere
- Client sharing uses a temporary tunnel URL that stops when you quit the app
- All project data stored as local JSON files
MIT
Built by Doza Visuals