AuthEcho_Project
This project contains well-trained deep learning models to predict the Speaker and their Gender .
The repository offers a Speaker and Gender Prediction System built using TensorFlow , Librosa , and Gradio . The application predicts the top 3 speakers and their probabilities from an audio file, determines the speaker's gender, and classifies unknown speakers using a confidence threshold.
Features
Predicts the top 3 speakers from an audio file.
Determines the gender of the speaker.
Identifies unknown speakers with a confidence threshold.
Provides a Gradio interface for easy testing.
Getting Started
Prerequisites
To run this application, you need:
Python : Version 3.8 or higherRequired Python libraries:
tensorflownumpylibrosagradioscikit-learn
Install the required libraries with:
pip install tensorflow numpy librosa gradio scikit-learnInstallation
Clone the Repository :
git clone https://github.com/your-username/speaker-gender-prediction.git
cd speaker-gender-prediction
Add Pre-Trained Models and Label Encoders :
Place the following files in the repository's root directory:
lstm_speaker_model.h5: Pre-trained speaker recognition model.lstm_gender_model.h5: Pre-trained gender prediction model.lstm_speaker_label.pkl: Label encoder for speaker classes.lstm_gender_label.pkl: Label encoder for gender classes.
Usage
Run the application using:
Gradio Interface
The Gradio interface allows you to:
Upload an audio file or record audio directly.Predict the top 3 speakers and their probabilities.
Determine the gender of the speaker.
Detect and classify unknown speakers using confidence thresholds.
Project Structure
.
├── app.py # Main application file
├── models/lstm_speaker_model.h5 # Pre-trained speaker model (to be added)
├── models/lstm_gender_model.h5 # Pre-trained gender model (to be added)
├── models/lstm_speaker_label.pkl # Speaker label encoder (to be added)
├── models/lstm_gender_label.pkl # Gender label encoder (to be added)
├── requirements.txt # Python dependencies
└── README.md # Project documentationExample Output
Top 3 Predicted Speakers:
The top 3 predicted speakers are:
Speaker 1: 85.23%
Speaker 2: 10.12%
Speaker 3: 4.65%
The predicted gender is: MaleUnknown Speaker:
The top 3 predicted speakers are:
Unknown: 45.23%
The predicted gender is: UnknownHow It Works
Feature Extraction :
Extracts MFCCs , chroma features , and spectral contrast from the input audio file using librosa.
Speaker and Gender Models :
Speaker Model : A pre-trained LSTM model classifies the speaker based on extracted features.Gender Model : A separate LSTM model determines the gender.
Unknown Detection :
If the highest confidence score for a speaker is below a defined threshold, the speaker is classified as "Unknown."
Roadmap
Add support for real-time audio predictions.
Improve unknown speaker detection using open-set recognition techniques.
Expand the dataset for more robust gender classification.
Contributing
Contributions are welcome! To contribute:
Fork the repository.
Create a feature branch (git checkout -b feature-branch-name).
Commit your changes (git commit -m "Add new feature").
Push to the branch (git push origin feature-branch-name).
Open a Pull Request.
License
This project is licensed under the
MIT License . See the
LICENSE file for details.
Acknowledgments
TensorFlow : For building the deep learning models.Librosa : For audio processing and feature extraction.Gradio : For creating the user interface. 
