1️⃣ PhonoCoach

Chrome extension with FastAPI backend for real-time pronunciation feedback using phoneme analysis leveraging OpenAI's whisper ASR model.

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2️⃣ Getting Started

🛠️ Prerequisites

• Python 3.12+
• pip
• Chrome browser
• ffmpeg installed and available in your system PATH.

# a)Clone the Repository
git clone https://github.com/Manoj-HV30/phonocoach.git
cd phonocoach
# b)Create a virtual environment
python3 -m venv venv

### c)Activate the virtual environment
# Linux/macOS:
source venv/bin/activate
# Windows (PowerShell):
# venv\Scripts\Activate.ps1

# d)Install dependencies
pip install -r requirements.txt

# e)Start the FastAPI backend server
uvicorn backend.server:app --reload

🖥️ Load the Chrome Extension Locally

1. Open Chrome and go to chrome://extensions/
2. Enable Developer mode (toggle in the top-right) 🛠️
3. Click Load unpacked and select the frontend folder inside the cloned repo 📂
4. The PhonoCoach icon should appear in your toolbar 🚀

3️⃣ PhonoCoach in action

Using PhonoCoach

1. Select any text on a webpage.
2. Open the PhonoCoach popup.
3. Click 🎙 Record to start recording your voice.
4. Click Stop to upload audio and analyze pronunciation.
5. View similarity score, phoneme-level feedback, and improvement tips.

4️⃣ Features ✨

• Real-time pronunciation analysis for any selected text on any webpage
• Phoneme-level feedback highlighting correct, incorrect, missing, and extra sounds
• Similarity score to quantify pronunciation accuracy
• improvement tips based on your performance
• Uses OpenAI's Whisper ASR for accurate speech-to-text transcription
• Lightweight FastAPI backend for fast processing

5️⃣ Dev Notes

• Make sure the backend server is running before using the Chrome extension.
• Ensure ffmpeg is installed and accessible in your system PATH. ⚡
• ⚠️ The Chrome extension is loaded locally and is NOT YET PUBLISHED on the Web Store.
• In backend/server.py, the Whisper model "small" is loaded by default. Users can change it to other models based on their system’s processing power:

Model	Approx. RAM Required	Recommended Use Case
tiny	~1 GB	Low-resource machines, faster processing
base	~2 GB	Lightweight, reasonable accuracy
small	~4 GB	Default, good balance of speed and accuracy
medium	~8 GB	Higher accuracy, slower processing
large	~16+ GB	Maximum accuracy, requires powerful CPU/GPU

• Users can adjust the model in server.py according to their available RAM and processing power.

import whisper

# Change the model here:
# Options: "tiny", "base", "small", "medium", "large"
model = whisper.load_model("small")