---
license: mit
language:
- en
tags:
- speaker
- speaker_Recognition
- gender
- voicebased
- ai
- ml
---


# 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 higher
- Required Python libraries:
  - `tensorflow`
  - `numpy`
  - `librosa`
  - `gradio`
  - `scikit-learn`

Install the required libraries with:

```
pip install tensorflow numpy librosa gradio scikit-learn
```

### Installation

1. **Clone the Repository**:

```
git clone https://github.com/your-username/speaker-gender-prediction.git
cd speaker-gender-prediction
```

2. **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:

```
python app.py
```

### 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 documentation
```

## Example 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: Male
```

### Unknown Speaker:

```
The top 3 predicted speakers are:
Unknown: 45.23%

The predicted gender is: Unknown
```

## How It Works

1. **Feature Extraction**:
   - Extracts **MFCCs**, **chroma features**, and **spectral contrast** from the input audio file using `librosa`.

2. **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.

3. **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:

1. Fork the repository.
2. Create a feature branch (`git checkout -b feature-branch-name`).
3. Commit your changes (`git commit -m "Add new feature"`).
4. Push to the branch (`git push origin feature-branch-name`).
5. Open a Pull Request.

## License

This project is licensed under the **MIT License**. See the [LICENSE](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.