Welcome to the fascinating world of voice synthesis! Today we will explore how to utilize the Yoruba text-to-speech (TTS) model checkpoint from Meta AI’s Massively Multilingual Speech (MMS) project. With just a few simple steps, you can turn written Yoruba text into spoken words, which can be useful for various applications such as digital assistants, educational tools, and much more.

What You Will Need

• Python installed on your system
• The latest version of 🤗 Transformers library (4.33 or higher)
• Basic knowledge of Python programming

Getting Started

First, you need to install the required libraries. You can do this by running the following command in your terminal:

pip install --upgrade transformers accelerate

Loading the MMS Yoruba Model

Once you’ve installed the necessary libraries, it’s time to load the Yoruba model. Think of this step like opening a book—the model is like a library, and the Yoruba text is your chosen story to be narrated.

Use the following Python code to load the model and prepare for text inference:

from transformers import VitsModel, AutoTokenizer
import torch

model = VitsModel.from_pretrained("facebook/mms-tts-yor")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-yor")
text = "your example text in the Yoruba language"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    output = model(**inputs).waveform

Saving Your Synthesized Speech

Now that you have your waveform output, you can save it as a .wav file. Consider this step as capturing the live performance of your favorite artist and preserving it for replay!

import scipy

scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)

Listening to Your Output

If you’re using a programming environment like Jupyter Notebook or Google Colab, you can listen to the audio directly.

from IPython.display import Audio

Audio(output, rate=model.config.sampling_rate)

Troubleshooting

While using the MMS Yoruba TTS model is quite straightforward, you might encounter some issues. Here are a few troubleshooting tips:

• Issue: The model fails to load.
• Solution: Ensure you’ve installed the latest library version as mentioned. Try re-updating with pip install --upgrade transformers accelerate.
• Issue: Audio waveform does not play.
• Solution: Check if your Jupyter Notebook/Colab supports audio playback. Make sure the path and filename are correctly set when saving the .wav file.
• Issue: No output or an error during inference.
• Solution: Check the input text for any encoding issues and ensure that it’s in the Yoruba language.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With the right setup, the Yoruba TTS model can efficiently make your text come alive. Remember, experimentation is key—don’t hesitate to try different texts and see how the model performs.

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.