The Massively Multilingual Speech (MMS) project from Facebook aims to expand speech technology across a diverse range of languages. If you’re looking to create speech synthesis for Persian using the VITS model, you’re in the right place! In this blog, I’ll guide you on how to set up and utilize the MMS model for Persian speech synthesis.

What is VITS?

VITS, which stands for Variational Inference with adversarial learning for end-to-end Text-to-Speech, enables you to generate a speech waveform based on input text sequences. Think of VITS as a talented voice actor who can interpret a script with a variety of emotions and intonations, bringing the text to life in unique ways.

Getting Started: Installation

Before diving into the speech synthesis process, you need to make sure that you have the necessary libraries installed. Ensure you install the latest version of the 🤗 Transformers library, specifically version 4.33 and above.

• Open your terminal or command line interface.
• Run the following command to install the required library:

pip install --upgrade transformers accelerate

Using the Model for Persian Speech Synthesis

Now that your setup is ready, let’s proceed with the implementation. Use the following code snippet to run inference.

python
from transformers import VitsModel, AutoTokenizer
import torch

model = VitsModel.from_pretrained("SeyedAliPersian-Speech-synthesis")
tokenizer = AutoTokenizer.from_pretrained("SeyedAliPersian-Speech-synthesis")

text = "your example text in Persian language"
inputs = tokenizer(text, return_tensors='pt')

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

The resulting waveform can then be saved as a .wav file or displayed directly in Jupyter Notebook or Google Colab, as shown below.

python
import scipy

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

# Or display in a notebook
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)

Troubleshooting

While implementing the MMS model for speech synthesis, you may encounter a few issues. Here are some common troubleshooting tips:

• If you receive an error related to library versions, ensure you upgraded the Transformers package to the latest version.
• If the audio output is not as expected, experiment with different texts, as the VITS model generates varied outputs for the same input text.
• For consistency in generated speech, remember to set a fixed seed.
• If you run into installation issues, verify your Python environment is compatible with the libraries you are using.

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

Conclusion

By following these steps, you can effectively leverage the MMS model for generating Persian speech synthesis. Remember, each text input may yield different speech outputs, allowing for flexibility in expression. The advanced architecture of VITS enhances the expressiveness, making it perfect for projects requiring nuanced speech synthesis.

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.