Are you ready to take your StyleTTS2 training experience to a whole new level? With the newly open-sourced multilingual PL-BERT checkpoint developed by Papercup, you can enhance the capabilities of StyleTTS2 to support multiple languages. In this article, we’ll guide you through the process of integrating this exciting new feature.

Getting Started with Multilingual PL-BERT

The first step is to understand what has changed with this new checkpoint compared to the default PL-BERT model. Let’s highlight a few essentials:

• This model employs the bert-base-multilingual-cased tokenizer to cater to various languages.
• The PL-BERT model has been trained for 1.1M iterations utilizing data from the crowdsourced dataset found in StyleTTS2 community datasets.
• There are significant alterations in the token_maps.pkl and util.py files that are crucial for handling multi-language data.

Step-by-Step Training Process

Now, let’s dive into how to train your StyleTTS2 with this new multilingual checkpoint:

• Create a New Folder: Inside your StyleTTS2 repository, navigate to the `Utils` directory. Create a new folder by the name of PLBERT_all_languages.
• Copy Required Files: You’ll need to copy the following files into the PLBERT_all_languages folder:
  • config.yml
  • step_1100000.t7
  • util.py
• Update the Configuration: In your StyleTTS2 config file, modify the PLBERT_dir to:

  Utils/PLBERT_all_languages

• Change the Import Statement: Revamp the import line:

  from Utils.PLBERT.util import load_plbert

  to:

  from Utils.PLBERT_all_languages.util import load_plbert

• Alternatives: If you prefer not to change the code, you could simply replace the original files in `Utils/PLBERT`.
• Create Training Files: You need to craft training and validation files. Use espeak to generate files matching the format found in the Data folder of your StyleTTS2 repository. Don’t forget to alter the language argument when working with non-English text. The language codes can be referred to in this resource. For instance, for Latin American Spanish, you’ll use `es-419`.

The Big Picture: Analogy Time!

Think of training your StyleTTS2 model as baking a multi-layer cake. The original recipe (default PL-BERT) gave you a delicious vanilla layer, but the new multilingual PL-BERT is an upgrade that lets you add layers of rich chocolate, vibrant berry, and creamy caramel!

• The new folder is your separate baking area for the chocolate cake layer.
• Copying files is like gathering ingredients—config.yml, step_1100000.t7, and util.py are critical recipes for success.
• Updating configurations ensures your cake is baked in the right oven, specifically tuned for multi-flavored delights.
• Creating training and validation files is like mixing your batter correctly to ensure each layer rises equally.

And once everything is set, you’ll serve up a beautiful, multi-layered StyleTTS2 cake that can handle numerous languages, ready for your audience to enjoy!

Troubleshooting Tips

While training with the new checkpoint may seem straightforward, issues can sometimes arise. Here are a few common troubleshooting suggestions:

• File Not Found Error: Double-check the paths in your configuration. Ensure that all referenced files are correctly located in the PLBERT_all_languages folder.
• Tokenization Issues: If you face errors with tokenization, validate that you’re utilizing the right tokenizer and that the token_maps.pkl file is appropriately adjusted.
• Language Code Confusion: Always confirm that your language codes are correct when generating training files. Mislabeling can lead to perplexing output!
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.

Conclusion

With the multilingual PL-BERT checkpoint, you’re poised to expand the horizons of StyleTTS2. By following these steps, you’re not just integrating a new feature, but also preparing a rich, diverse experience for users across multiple languages!