Parakeet Web

⚠️ EXPERIMENTAL WIP – This is a heavily modified fork, purely vibe-coded. Expect bugs, breaking changes, and rough edges.

Browser-based speech-to-text running entirely client-side using NVIDIA's Parakeet TDT 0.6B v3 model (converted to ONNX format by istupakov) via WebGPU/WASM.

What It Does

• Runs speech-to-text entirely in your browser (nothing sent to servers)
• Supports WebGPU (fast) and WASM (compatible) backends
• Word-level timestamps and confidence scores
• File upload or microphone recording
• Model quantization options (fp32/int8)
• Installable as a PWA (Progressive Web App) for app-like experience

Status

• 🚧 Work in progress
• 🧪 Experimental and unstable
• 📝 Licensed under AGPLv3
• 🎨 Vibe-coded with aider.chat and Claude Code

Live Demo

A running instance is available at https://pw.olicorne.org/ (no installation required).

Dictation Mode

Parakeet Web includes an experimental dictation mode that post-processes transcriptions using regex rules to clean up spoken punctuation, medical vocabulary, and unit abbreviations. This is especially useful for French medical dictation. It

The regex rules are sourced from the Murmure repository by the non profit interhop.org for the Murmure software, which I also use when I can. All CSV files in that folder are automatically downloaded — when new rule files are added upstream, they are picked up on the next container restart.

So far medical categories are in french and include categories like:

• ponctuation — spoken punctuation to symbols ("virgule" → ,, "retour à la ligne" → newline, "ouvrez les guillemets" → ", etc.)
• constante — unit abbreviations ("milligrammes" → mg, vital signs formatting)
• controle — clinical exam template expansions
• medicament — medication name corrections from common speech-to-text errors
• vocabulaire_medical — medical vocabulary corrections ("disney" → "dyspnée", "avait ces" → "AVC", etc.)

This feature is very early and will improve rapidly.

How it works

• Docker: The entrypoint script queries the Murmure GitLab API to discover all .csv files in the regex/ folder and downloads them on first startup. Falls back to a known file list if the API is unreachable.
• Local development: Run ./scripts/download-dictation-regex.sh to fetch the rules into app/ui/public/dictation-regex/.
• Frontend: The app loads the CSV rules at startup via a manifest file and applies them as JavaScript RegExp replacements. After regex processing, each line is stripped of leading/trailing whitespace and its first letter is capitalized. Three display modes are available per transcription: Raw, Confidence (heatmap), and Dictation (regex-cleaned).
• Custom regex source: Set the DICTATION_REGEX_SOURCE environment variable to override the default Murmure URL. This can be a GitLab-compatible repo URL (e.g. https://framagit.org/interhop/murmure) or a local folder path containing CSV regex files (e.g. /path/to/my/regex-csvs). This allows you to iterate on regex rules locally without waiting for upstream changes.

Quick Start

# 1. Copy the example env file and edit it with your own values
cp docker/env.example docker/.env

# 2. Run the demo locally with Docker
sudo docker compose -f docker/docker-compose.yml up

3. Then visit http://localhost:5173

Local Model Fallback

If HuggingFace is blocked or unreachable in your environment, you can serve model weights directly from the container:

# 1. Install the HuggingFace CLI and download the model files locally
pip install huggingface-hub
hf download istupakov/parakeet-tdt-0.6b-v3-onnx --local-dir ./fallback_models/istupakov__parakeet-tdt-0.6b-v3-onnx

# 2. In docker/docker-compose.yml, uncomment the volume bind:
#   - ./fallback_models/istupakov__parakeet-tdt-0.6b-v3-onnx:/app/ui/public/models/istupakov/parakeet-tdt-0.6b-v3-onnx:ro

# 3. In docker/.env, enable the fallback:
VITE_LOCAL_MODEL_FALLBACK=true

The downloaded files are git-ignored. When VITE_LOCAL_MODEL_FALLBACK=true is set, the app will check for the local model files on startup and refuse to load if they are missing.

License

AGPLv3 – See LICENSE file

Acknowledgments

• ysdede/parakeet.js – Original project this is forked from
• nvidia/parakeet-tdt-0.6b-v3 – The underlying ASR model by NVIDIA
• istupakov/parakeet-tdt-0.6b-v3-onnx – ONNX conversion of the model
• istupakov/onnx-asr – Python reference implementation
• ONNX Runtime Web – Makes browser inference possible

Credits

This fork is based on ysdede/parakeet.js – all the heavy lifting and original implementation credit goes there. This would not exist without their excellent work.