🎙️ Empathy Engine (Assignment)

This project is an Emotion-Aware Text-to-Speech (TTS) system that detects emotions from input text and generates emotionally aligned speech.
It combines sentiment analysis with speech synthesis parameters (rate, volume, voice) to make the output sound more human-like and empathetic.

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🚀 Features

• Emotion Detection → Uses TextBlob to analyze sentiment and map text into happy, sad, or neutral with intensity.
• Dynamic Speech Synthesis → Adjusts speech rate, volume, and voice based on detected emotion and intensity.
• FastAPI Backend → Exposes a /speak API endpoint to generate emotional speech.
• Streamlit Frontend → Simple UI for users to input text and listen to emotional speech.
• Female Voice Support → Configured pyttsx3 to use a female voice (instead of default male).

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🛠️ Project Structure

.
├── app.py              # FastAPI backend (API to generate speech)
├── config.py           # Emotion mapping (rate, volume adjustments per emotion)
├── emotion_detector.py # Emotion detection using TextBlob sentiment analysis
├── streamlit_app.py    # Streamlit frontend UI
├── tts_engine.py       # Text-to-Speech generation logic

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⚙️ How It Works

1. User Input → Text is entered via Streamlit frontend.
2. Emotion Detection → emotion_detector.py uses sentiment polarity:
   • Positive → Happy 😃
   • Negative → Sad 😢
   • Neutral → Neutral 😐
     Intensity is scaled between 0 → 1.
3. Parameter Mapping → In config.py, each emotion has base values for:
   • Speech rate (words per minute)
   • Speech volume (loudness)
   • Variations depending on intensity
4. Speech Synthesis → tts_engine.py uses pyttsx3 to generate speech:
   • Adjusts rate & volume dynamically
   • Selects a female voice (voices[1].id)
5. API Response → app.py streams back .wav audio to the frontend.
6. Frontend Playback → Streamlit displays detected emotion + plays audio.

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▶️ Usage

Follow these steps to set up and run the project:

1️⃣ Clone the Repository

git clone https://github.com/Anshul21107/Empathy-Engine
cd Empathy-Engine

2️⃣Create & Activate Virtual Environment

python -m venv venv
venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣Run Backend (FastAPI)

python app.py

5️⃣ Run Frontend (Streamlit)

streamlit run streamlit_app.py

Example

• Input: "I am so excited about this new project!"
• Detected: Emotion = happy, Intensity ≈ 0.9
• Output: Faster, louder, female-voiced audio with happy tone.

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📂 API Endpoint

POST /speak

{
  "text": "Your input sentence here"
}

Response: WAV audio stream + headers:

• X-Emotion: Detected emotion
• X-Intensity: Emotion intensity

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Points

• Implemented sentiment-based prosody control (rate, volume, voice).
• Used FastAPI for scalable backend + Streamlit for quick prototyping UI.
• Showed ability to integrate NLP + TTS + Web APIs.
• Designed EMOTION_MAP for realistic emotion simulation.
• Extended TTS to female voice selection (instead of default male).

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📸 Screenshots

🔹 FastAPI Endpoint

🔹 Streamlit UI

🔊 Sample Audio Output

Here’s an example of emotionally generated speech of this text: "I am deeply saddened by this unfortunate event. It's truly heartbreaking.":

🎧 ▶️ Listen to This Example

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👨‍💻 Author

Developed by Anshul Katiyar