Deep Learning Audio Project

A practical audio processing system that uses AI to understand and work with speech and sounds.

What This Project Does

This project brings together several audio AI capabilities:

• Speaker Recognition — Identify who's speaking by analyzing their voice characteristics
• Speech Transcription — Convert spoken words into text
• Language Detection — Identify which language is being spoken
• Voice Activity Detection — Detect when someone is actually talking (vs. silence or noise)
• Sound Classification — Identify different types of sounds and audio events
• Text-to-Speech — Generate natural-sounding speech from text
• Speaker Verification — Check if a voice matches a known speaker (like voice biometrics)

How It Works

The project uses pre-trained AI models from SpeechBrain and Whisper. It can:

1. Record audio from your microphone
2. Analyze the audio to extract information about the speaker and speech
3. Store speaker profiles by creating "embeddings" (mathematical representations of a person's voice)
4. Compare new audio against stored profiles to verify identity

Key Files

• speech_verification_demo.py — Interactive demo to verify who's speaking
• speech_full_system_optimized.py — Complete audio analysis pipeline
• speech_vad.py — Detects when speech is present
• speech_language_identification.py — Identifies spoken language
• speech_sound_classification.py — Classifies different sounds
• speech_tts.py — Generates speech from text

Setup

The project uses Python with deep learning libraries (PyTorch) and pre-trained models stored in pretrained_models/. Audio samples and embeddings are organized by speaker in the embeddings/ and transcriptions/ folders.

Use Cases

• Voice authentication systems
• Automatic speech recognition
• Audio analysis and categorization
• Voice biometrics applications