In the realm of artificial intelligence, text-to-speech (TTS) has revolutionized how text is transformed into spoken words. Harnessing the power of models like VITS, we can create realistic and engaging voice outputs. In this guide, we’ll dive into setting up a Russian TTS system using the VITS model, allowing you to seamlessly convert text into voice. Let’s embark on this journey!

Step-by-Step Guide

• Step 1: Install the necessary libraries.
• Step 2: Import the libraries and load the VITS model.
• Step 3: Prepare your input text.
• Step 4: Generate the audio output.
• Step 5: Play the audio output.

Code Example

We’ll explain the process in detail through Python code that utilizes the Hugging Face Transformers library:

python
from transformers import VitsModel, AutoTokenizer
import torch
import scipy

# Load the VITS model and tokenizer
model = VitsModel.from_pretrained('joefoxtts_vits_ru_hf')
tokenizer = AutoTokenizer.from_pretrained('joefoxtts_vits_ru_hf')

# Text input
text = "Привет, как дел+а? Всё +очень хорош+о! А у тебя как?"

# Convert to lowercase, as required
text = text.lower()

# Prepare inputs for the model
inputs = tokenizer(text, return_tensors='pt')
inputs['speaker_id'] = 3  # Set speaker ID

# Generate audio output
with torch.no_grad():
    output = model(**inputs).waveform
    scipy.io.wavfile.write('techno.wav', rate=model.config.sampling_rate, data=output[0].cpu().numpy())

# For displaying in a Jupyter Notebook / Google Colab
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)

Explaining the Code: An Analogy

Think of the VITS model as a sophisticated chef, proficient in cooking delicious meals from text-based recipes. Here’s how the cooking process unfolds:

• Ingredients (Libraries): Just as a chef needs utensils and ingredients, we need libraries like Transformers and Torch to prepare our meal.
• Recipe Book (Model and Tokenizer): The chef refers to a recipe book, which in our case is represented by the VITS model and tokenizer. This helps the chef understand how to process the given ingredients (text).
• Preparation (Inputs): The chef prepares the ingredients—lowering case and organizing them for cooking. Similarly, we preprocess our text to ensure it meets the model’s requirements.
• Cooking (Generating Output): The chef combines everything and lets it simmer, which parallels the model generating the spoken audio waveform.
• Serving (Playing Audio): Finally, just like serving the meal on a plate, we play the audio output for enjoyment.

Troubleshooting

If you encounter issues while following this guide, consider the following troubleshooting tips:

• Make sure all required libraries are installed and compatible versions are being used.
• Verify that the input text adheres to the expected format, including being in lowercase.
• Check if the correct model and tokenizer names are used when calling the from_pretrained method.
• If audio output isn’t playing properly, ensure that your environment supports audio playback.

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Conclusion

In this blog, we explored how to create a Russian text-to-speech system using the VITS model. As we integrate technology into our everyday lives, such advancements in TTS can pave the way for inclusive communication. 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.