Speaker Recognizer

This project is a simple speaker recognition application that uses machine learning to identify speakers from audio recordings. The application is built with Python and utilizes various libraries for audio processing and visualization.

Features

• Record audio and identify the speaker.
• Import WAV files and identify the speaker.
• Visualize audio spectrum during recording.

Requirements

• Python 3.7 or higher
• Required libraries: numpy, matplotlib, librosa, scikit-learn, sounddevice, pyaudio, tkinter, scipy

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/speaker-recognizer.git
   cd speaker-recognizer

2. Install the required Python libraries:

   pip install numpy matplotlib librosa scikit-learn sounddevice pyaudio scipy

3. Ensure you have the following files in the project directory:

   • microphone.ico
   • background.png
   • background2.png
   • img0.png
   • img1.png
   • img2.png

4. Create a directory named Voices in the project directory. This folder should contain WAV files for training the model. The filenames should start with the speaker's name (e.g., alice_01.wav, bob_02.wav).

Usage

1. Run the Application:

   python main.py

2. Main Window:

   • Click on the microphone icon to start recording.
   • Click on the upload icon to import a WAV file.

3. Recording Interface:

   • Recording starts automatically after a 1-second delay.
   • The predicted speaker is displayed after recording.
   • The audio spectrum is visualized in real-time.

Code Overview

• main.py: Main application script.
• Voices/: Directory containing training audio files.
• extract_features(): Function to extract features from audio files.
• import_wav(): Function to import a WAV file and identify the speaker.
• change_colors(): Function to switch to the recording interface and start recording.

Training the Model

The model is trained using the K-Nearest Neighbors (KNN) algorithm. Training data is loaded from the Voices directory. MFCC features are extracted from the audio files for training.

Example Training Data Structure

Voices/
├── alice_01.wav
├── alice_02.wav
├── bob_01.wav
└── bob_02.wav

Acknowledgements

This project uses the following open-source libraries:

• librosa
• scikit-learn
• sounddevice
• pyaudio
• tkinter
• matplotlib