## 1️⃣   PhonoCoach
Chrome extension with FastAPI backend for real-time pronunciation feedback using phoneme analysis leveraging OpenAI's whisper ASR model.

---


## 2️⃣   Getting Started

### Prerequisites

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


```bash
# 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
[![PhonoCoach in Action](https://i.postimg.cc/1tjyg0rG/2025-08-16-05-17.png)](https://postimg.cc/VrnxpCTv)
[![PhonoCoach in Action](https://i.postimg.cc/0jvr50kV/2025-08-16-05-38.png)](https://postimg.cc/Vr7zDMbt)
### 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.
```python
import whisper

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