Today, we delve into the exciting world of multilingual text-to-speech (TTS) systems powered by the PL-BERT checkpoint developed by Papercup. With this guide, you’ll learn how to adapt the StyleTTS2 framework to support multiple languages effectively.

Understanding the Basics: PL-BERT and StyleTTS2

Before we embark on our training journey, let’s clarify what PL-BERT and StyleTTS2 are:

• PL-BERT: A pre-trained language model specifically tailored for phoneme-based text-to-speech systems. It has been expanded to support multiple languages.
• StyleTTS2: An advanced framework used for generating natural-sounding speech from text. This tool can harness PL-BERT’s capabilities to enhance its multilingual functionalities.

Steps to Train StyleTTS2 with the New PL-BERT Checkpoint

Let’s break down the complexity of training your multilingual TTS model into simple steps:

1. Create a new folder under Utils in your StyleTTS2 repository.
   • For example, name it PLBERT_all_languages.
2. Copy and paste the following files into your new folder:
   • config.yml
   • step_1100000.t7
   • util.py
3. Adjust your StyleTTS2 configuration:
   • Change PLBERT_dir to Utils/PLBERT_all_languages.
   • Update the import statement from:
     from Utils.PLBERT.util import load_plbert
     to
     from Utils.PLBERT_all_languages.util import load_plbert
   • You may opt to replace the relevant files in Utils/PLBERT directly to avoid code changes.
4. Create train and validation files:
   • Utilize espeak to generate files in the same format as those in the Data folder of your repository.
   • Change the language argument for phonemizing text if not in English; reference the language codes here.
   • For instance, use es-419 for Latin American Spanish.

Here’s the Analogy: Training Your Multilingual Model

Think of training your multilingual StyleTTS2 model like preparing a multi-course meal:

• Ingredients: Just like you need fresh ingredients tailored to each dish, you’ll gather the necessary files and configurations for your model.
• Tools: You’ll use various kitchen tools (the utilities in your code) to mix and prepare each ingredient for cooking (training your model).
• Recipe: The steps outlined (just like in a recipe) guide you through the cooking process, ensuring each course is ready to serve at the right time.
• Final Dish: Just as a well-made meal is a delight, your finished multilingual TTS model is your masterpiece, ready to produce natural-sounding speech.

Troubleshooting Tips

Even the best chefs face challenges. If you encounter issues during the training process, consider the following tips:

• Ensure that all necessary files are correctly placed in your new folder.
• Double-check your configuration settings to ensure paths are accurate.
• Look for any errors related to file loading that may indicate incorrect settings or missing dependencies.
• To resolve tokenizer issues, verify that you’re using bert-base-multilingual-cased and that it is properly installed.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With perseverance and the right instructions, you can train a sophisticated multilingual StyleTTS2 model using the PL-BERT checkpoint. Thank you to Aaron (Yinghao) Li for the contributions that have made this possible!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.