Projects Overview

This repository contains two projects: a Text-to-Speech (TTS) project using Microsoft's SpeechT5 model and a YOLO Object Detector project using the YOLOv5 model.

Text-to-Speech Project

Description

This project demonstrates the use of the Microsoft SpeechT5 model for text-to-speech synthesis. It provides examples of generating speech from text using pre-trained models and speaker embeddings.

Requirements

• torch
• transformers
• datasets
• soundfile

Install the required packages using:

pip install torch transformers datasets soundfile

Usage

1. Initialize the TTS pipeline:

   • Use the pipeline function from transformers to create a text-to-speech pipeline with the microsoft/speecht5_tts model.

2. Load speaker embeddings:

   • Load speaker embeddings from the Matthijs/cmu-arctic-xvectors dataset.

3. Generate and save speech:

   • Generate speech from text and save it as an audio file.

Example Commands

1. Initialize the pipeline and synthesizer.
2. Load the embeddings dataset and extract a specific embedding.
3. Generate speech and save it to a file.

License

This project is licensed under the MIT License.

YOLO Object Detector Project

Description

This project demonstrates the implementation of the YOLOv5 object detection model. It provides examples of loading the model, preprocessing images, performing object detection, and visualizing the results.

Requirements

• torch
• opencv-python
• matplotlib
• yolov5 (from the official YOLOv5 repository)

Install the required packages using:

pip install torch opencv-python matplotlib git+https://github.com/ultralytics/yolov5.git

Usage

1. Load the YOLOv5 model:

   • Load the pre-trained YOLOv5 model using the torch.hub.load method.

2. Preprocess images:

   • Preprocess input images to the required format for YOLOv5.

3. Perform object detection:

   • Use the model to detect objects in the preprocessed images.

4. Draw bounding boxes and visualize results:

   • Draw bounding boxes around detected objects and display/save the result.

Example Commands

1. Load and preprocess an image.
2. Perform object detection and apply non-max suppression.
3. Draw bounding boxes on detected objects and display/save the resulting image.

Contributing

• George Youhana - [email protected]
• Mostafa Magdy - [email protected]
• Abdallah Alkhouly - [email protected]