If you’re venturing into the realm of transforming text to audio and eager to harness the power of AI-generated speech, the Chat TTS library is the key to unlocking this innovation. In this guide, we’ll walk you through the entire process from cloning the repository to generating your first audio output. So let’s dive in!

Step 1: Clone the Repository

The first step is to get your hands on the code by cloning the Git repository. Open your terminal and run the following command:

git clone https://github.com/2noise/ChatTTS.git

Step 2: Model Inference Setup

Now that you have the repository cloned, it’s time to set up the model for inference. Here’s a breakdown of the necessary code:

import torch
import torchaudio

torch._dynamo.config.cache_size_limit = 64
torch._dynamo.config.suppress_errors = True
torch.set_float32_matmul_precision(high)

import ChatTTS
from IPython.display import Audio

chat = ChatTTS.Chat()
chat.load_models(compile=False) # Set to True for better performance

Think of this step as prepping your kitchen before cooking a new recipe. You gather all the essential ingredients (libraries) you need to make your dish (generate the audio). The initial configurations ensure everything is ready for your main course.

Step 3: Defining Text for Inference

Next, we will set the text input for the model to synthesize. Here’s how you can do that:

texts = [
    "So we found being competitive and collaborative was a huge way of staying motivated towards our goals, so one person to call when you fall off, one person who gets you back on then one person to actually do the activity with."
]

This text input serves as your recipe instruction. The model will use it to generate the corresponding audio output.

Step 4: Generating Audio

With everything in place, you can now perform inference to generate and play the audio. Here’s the code to take action:

wavs = chat.infer(texts)
Audio(wavs[0], rate=24000, autoplay=True)

Once executed, this code brings your text to life, creating an audio output as if you just cooked a delicious meal ready to be served. The generated audio is played automatically, immersing you in your creation!

Step 5: Saving the Generated Audio

You can also save the generated audio to a file for later use. Here’s the code snippet:

torchaudio.save('output.wav', torch.from_numpy(wavs[0]), 24000)

This step is like putting your finished dish in a nice container so you can enjoy it again later or share it with friends.

Troubleshooting Common Issues

If you encounter any issues while setting up or using the Chat TTS library, here are a few troubleshooting suggestions:

• Model Loading Issues: Ensure you have the right permissions for loading the models. Check if your Python environment is correctly set up.
• Audio Playback Problems: Make sure your device’s audio settings are properly configured and the volume is turned up.
• Installation Errors: Always verify that you have all necessary dependencies installed, including PyTorch and Torchaudio.

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

Additional Resources

For further examples and finer control over the generated speech, you can refer to the documentation notebook available at: example notebook.

Disclaimer

The information provided in this document is intended for educational and research purposes and should not be used for any commercial or legal applications. The authors do not guarantee the accuracy, completeness, or reliability of any information provided.

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.