Real-Time Speech & Emotion Analysis: What I Built with Whisper + NLP

Why I Started This Project

While working in AI, I've always been fascinated by audio-based projects. Detecting emotions in speech can make human-computer interaction feel more natural. This motivated me to build a real-time speech transcription and emotion analysis system.

Technologies Used

• OpenAI Whisper — Powerful open-source model for Automatic Speech Recognition (ASR)
• HuggingFace Transformers — Pre-trained NLP model for emotion classification
• Flask + WebSocket — Real-time web interface
• PyTorch — Model inference

System Architecture

The system consists of three main components:

1. Frontend: Captures microphone audio from the browser and sends it to the backend via WebSocket
2. ASR Module: Converts speech to text using Whisper
3. NLP Module: Takes the text and extracts sentiment labels (positive, negative, neutral)

import whisper
from transformers import pipeline

asr_model = whisper.load_model("base")
sentiment_pipeline = pipeline("sentiment-analysis")

def analyze(audio_bytes):
    result = asr_model.transcribe(audio_bytes)
    text = result["text"]
    sentiment = sentiment_pipeline(text)[0]
    return {"text": text, "sentiment": sentiment}

What I Learned

Whisper delivers surprisingly accurate results even for short audio clips. I used the cardiffnlp/twitter-roberta-base-sentiment model for sentiment analysis, which performs well on general text.

The WebSocket architecture turned out to be a perfect choice for managing real-time data streams. The flask-socketio library made this integration quite straightforward.

Conclusion

This project deepened my interest in multimodal AI systems. Systems combining audio, text, and images will become increasingly important in the future. Check out the project on GitHub!