The jaywalnut310/vits repository is a PyTorch implementation of the VITS (Variational Inference for Text-to-Speech) model, a state-of-the-art text-to-speech (TTS) system that can generate high-quality, natural-sounding speech from text inputs.

Pros

• High-Quality Speech Generation: VITS is capable of generating highly natural-sounding speech, with a level of quality that rivals professional voice recordings.
• Versatile and Customizable: The model can be fine-tuned on various datasets, allowing for the creation of custom voices and speaking styles.
• Efficient and Fast Inference: The model is designed for efficient inference, enabling real-time speech generation on modern hardware.
• Open-Source and Actively Maintained: The project is open-source and actively maintained, with regular updates and improvements.

Cons

• Complexity: Implementing and training the VITS model can be a complex and computationally intensive process, requiring significant expertise in machine learning and TTS systems.
• Limited Pre-Trained Models: The repository currently provides a limited number of pre-trained models, which may not cover all desired languages or speaking styles.
• Dependency on PyTorch: The project is built using PyTorch, which may be a limitation for users who prefer other deep learning frameworks.
• Potential Performance Issues: Depending on the hardware and system configuration, the model may experience performance issues during inference or training.

Code Examples

from vits.model import VITSModel
from vits.utils import load_checkpoint

# Load a pre-trained VITS model
model = VITSModel.from_pretrained("jaywalnut310/vits-ljspeech")

# Generate speech from text
text = "Hello, this is a sample text-to-speech output."
audio = model.generate_speech(text)

This code demonstrates how to load a pre-trained VITS model and use it to generate speech from a given text input.

from vits.dataset import LJSpeechDataset
from vits.trainer import VITSTrainer

# Load the LJSpeech dataset
dataset = LJSpeechDataset("path/to/ljspeech")

# Create a VITS trainer and fine-tune the model
trainer = VITSTrainer(dataset, model_path="path/to/save/model")
trainer.train(num_epochs=100)

This code shows how to fine-tune the VITS model on a custom dataset, such as the LJSpeech dataset, using the provided VITSTrainer class.

from vits.inference import VITSInference

# Create an inference interface
inference = VITSInference("path/to/vits/model")

# Generate speech from text
text = "This is another sample text-to-speech output."
audio = inference.generate_speech(text)

This code demonstrates how to use the VITSInference class to generate speech from text using a pre-trained VITS model.

Getting Started

To get started with the jaywalnut310/vits project, follow these steps:

1. Clone the repository:

git clone https://github.com/jaywalnut310/vits.git

2. Install the required dependencies:

cd vits
pip install -r requirements.txt

3. Download a pre-trained VITS model:

from vits.model import VITSModel
model = VITSModel.from_pretrained("jaywalnut310/vits-ljspeech")

4. Generate speech from text:

text = "Hello, this is a sample text-to-speech output."
audio = model.generate_speech(text)

5. (Optional) Fine-tune the VITS model on a custom dataset:

from vits.dataset import LJSpeechDataset
from vits.trainer import VITSTrainer

dataset = LJSpeechDataset("path/to/ljspeech")
trainer = VITSTrainer(dataset, model_path="path/to/save/model")
trainer