Have you ever wanted to convert written text into spoken words in Kinyarwanda effortlessly? Welcome to the realm of Text-to-Speech (TTS) technology! In this article, we will explore an innovative Kinyarwanda TTS model that harnesses the power of deep learning, making it as easy as a walk in the park.

Model Description

This model is an end-to-end deep-learning-based solution designed specifically for Kinyarwanda. Imagine a talented actor who can mimic various voices with just one minute of audio as training. That’s what the zero-shot learning capabilities of this model achieve! Trained using the Coquis TTS library and the YourTTS architecture, it draws on 67 hours of Kinyarwanda Bible data and completed 100 epochs of training. This means it can generate convincing voice outputs, even with limited resources.

Data Sources

To understand how our TTS model was developed, let’s take a closer look at the data sources.

• Audio data: www.faithcomesbyhearing.com, Common Language Version audio Old Testament
• Text data: www.bible.com, Bibiliya Ijambo ryimana (BIR) – only the Old Testament was used

How to Use the Kinyarwanda TTS Model

Ready to start using this model? Follow these steps for a smooth setup!

1. First, install the Coquis TTS library by executing the following command in your terminal:

pip install TTS

2. Next, download the necessary files from this repository.
3. Now, run the TTS command as shown below:

tts --text text --model_path best_model.pth --encoder_path SE_checkpoint.pth.tar --encoder_config_path config_se.json --config_path config.json --speakers_file_path speakers.pth --speaker_wav conditioning_audio.wav --out_path out.wav

In this command: – Replace “text” with the Kinyarwanda text you want to convert. – The “conditioning audio” refers to the wav file(s) used to condition a multi-speaker TTS model. You can include multiple file paths as needed. – “d_vectors” will be computed as the average of the provided audio files.

Troubleshooting Tips

Encountering some bumps along the way? Here’s how to tackle a few common issues:

• Installation Errors: Ensure you have the latest version of Python and the necessary packages installed. If installation fails, try running the command in an administrator mode or updating your pip installer.
• Audio Quality Issues: Ensure your conditioning audio files are clear and of good quality; poor audio can lead to subpar output.
• Model Not Working: Double-check that all file paths in your command are correct. Missing or incorrect paths can lead to the model not executing as desired.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.