Text-To-Speech (Robo-Shaul)

Welcome to the Robo-Shaul repository! This project enables you to train your own Robo-Shaul or use pre-trained models to convert Hebrew text into speech using the Tacotron 2 TTS framework.

Robo-Shaul was originally developed for a competition, where the winning model was trained for only 5k steps. After the competition, a more advanced model was trained for 90k steps using improved methodologies and a wider range of training data, resulting in significantly better performance.

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🚀 Quick Start

Prerequisites

• Python 3.10

Installation

1. Clone the repository:

   git clone https://github.com/maxmelichov/Text-To-speech.git
   cd Text-To-speech

2. Set up a virtual environment:

   python3.10 -m venv venv
   source venv/bin/activate  # Linux/Mac
   # or
   activate.bat  # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Clone required submodules and dependencies:

   git clone https://github.com/maxmelichov/tacotron2.git
   git submodule init
   git submodule update
   git clone https://github.com/maxmelichov/waveglow.git
   cp waveglow/glow.py ./

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📁 Project Structure

The main directories used in this project are:

Text-To-speech/
├── data/                  # Place the SASPEECH dataset here
├── checkpoints/           # Stores Tacotron2 model checkpoints (*.pt files)
├── waveglow_weights/      # Stores WaveGlow model checkpoint (*.pt file)
├── tacotron2/             # Tacotron2 source code (cloned as submodule)
├── waveglow/              # WaveGlow source code (cloned as submodule)
├── ...

• data/: Put your downloaded and preprocessed dataset here.
• checkpoints/: Save and load Tacotron2 model weights (e.g., checkpoint_90000.pt).
• waveglow_weights/: Place the WaveGlow model checkpoint file (e.g., waveglow_256channels.pt).

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📦 Download Pre-trained Models

• Weights for RoboShaul: Download Waveglow is a must and the 5K steps or 90K steps is for you to choose

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📚 Dataset

• Download the SASPEECH dataset from OpenSLR.

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🛠️ Usage

1. Preprocess the data:

   python data_preprocess.py

   After running the script, ensure you generate a .txt file in the same format as the examples in the filelists directory:

   path/to/audio.wav|transcript in Hebrew that using English letters
   

2. Train the model:

   python train.py

3. Generate speech (inference):

   python inference.py

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💡 Demos & Resources

• Live Demo: Project Site
• Demo Page: here
• Quick Start Notebook: Notebook |
• Project Podcast: חיות כיס episode
• Training & Synthesis Videos: Part 1 | Part 2

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📝 Model Details

• The system uses the SASPEECH dataset, a collection of unedited recordings from Shaul Amsterdamski for the 'Hayot Kis' podcast.
• The TTS system is based on Nvidia's Tacotron 2, customized for Hebrew.

Note: The model expects diacritized Hebrew (עברית מנוקדת). For diacritization, we recommend Nakdimon (GitHub).

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👥 Contact

Maxim Melichov	Tony Hasson
LinkedIn	LinkedIn

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Feel free to reach out with questions or suggestions!