Voice Cloning for Any Language Using the Tortoise TTS Model

Introduction

• Objective: Fine-tune the Tortoise TTS model for any language.
• Example Language: German.

Nvidia RTX 3080 TI GPU Giveaway

• Eligibility: Attend Nvidia's 2024 GTC Conference and send proof of attendance (screenshot).

Getting Started

Prerequisites

• Dataset: Speech data in the target language (e.g., a German dataset containing 97 hours of audio from 117 speakers).
• Dataset Structure: Follow the LJ speech format.
  • Folder with audio samples: filenames containing the transcription.
  • Includes train and validation subsets.
  • Example naming: identifier|waveform file|transcription.

Download and Format Dataset

• Download Script: Provided in the video/script info.
• Unzip and Format: Adjust folder structure and create the dataset.
  • Example steps: Move all files to one folder, adjust filenames, and divide into train/validation.

Fine-Tuning the Tortoise TTS Model

Preprocessing

1. Transliterate/Normalize Text: Modify the text normalization function (cleanup.py file).
   • Use transliterate library (e.g., German transliterate library) to handle language-specific characters.
2. Special Characters: Add the special characters for your language in symbols.py (both lowercase and uppercase).
3. YAML Configuration: Modify YAML file (config.yaml) to specify training and validation data.
   • Example changes: custom language name, tokenizer vocabulary, paths, number of iterations.
4. Tokenizer Training: Train a tokenizer using the transcriptions.
   • Create a file with all transcriptions concatenated.
5. Adjust Sampling Rate: Ensure all audio samples have a sampling rate of 22.05 kHz.
6. Training: Use the modified scripts to train the model on your dataset.
   • Example steps: Fork the GitHub repository, clone and adjust the code, and train the model.

Changes to Code and Configuration

• fork the Repository: GitHub alterations and fork it to make changes.
• Download and Install Modules: Follow the provided scripts and use specific library versions as needed (e.g., Transformers library version 4.29.2).
• Tokenizer Training Code: Adjust text cleaners and special characters regex.
• Adjust Cleaning and Punctuation Handling: Improve overall quality.
• Additional Changes: Modify fine-tuning script if necessary.

Training Execution

• Running the Training: Example provided for NVIDIA GPUs.
• Depending on GPU power, training can take hours up to a day.
• Checkpoint Saving: Models save at specified steps (every 500 steps).
  • Important: stop training only after a completed step set to avoid losing progress.
• Resume Training: Update the config file to use the last checkpoint for further fine-tuning.

Post Training

• Access the fine-tuned model checkpoint files from the specified directory.
• Next Steps: Use the fine-tuned model to generate speech in your language (covered in part 2).

Conclusion

• Future videos: Detailed creation of your own speech dataset and adjusting inference code for multiple languages.
• Reminder: Subscribe to the channel for more tutorials and updates.