LlamaVoice is an innovative, Llama-based model for large-scale voice generation. It takes a novel approach by predicting continuous features directly, offering a more streamlined and efficient process compared to traditional models that rely on vector quantization for discrete speech code prediction.

The following diagram illustrates the architecture of LlamaVoice:

flowchart TD
    text(["Text"]) --> bbpe("Tokenizer")
    bbpe --> model("Llama Model")
    Voice_prompt(["Voice Prompt"]) --> model
    model --> linear("Linear") & dist1(["LLM distribution"])
    linear --> stop(["Stop"])
    dist1 <-- KL --> flow("Flow")
    flow <--> dist2(["VAE distribution"])
    target_wav(["Target Voice"]) --> speech_encoder["speech_encoder"]
    speech_encoder --> dist2
    dist2 --> v("Voice Decoder")
    v --> generated_wav(["Generated/Reconstructed Voice"])
    style text fill:#FFF9C4
    style model fill:#FFCDD2
    style linear fill:#FFCDD2
    style speech_encoder fill:#FFCDD2
    style flow fill:#FFCDD2
    style v fill:#FFCDD2
    style Voice_prompt fill:#FFF9C4
    style stop fill:#FFF9C4
    style target_wav fill:#FFF9C4
    style generated_wav fill:#FFF9C4

• Continuous Feature Prediction: LlamaVoice predicts continuous features directly, bypassing the need for vector quantization and resulting in a more efficient process.
• VAE Latent Feature Prediction: Unlike models that predict mel-spectrograms, LlamaVoice predicts Variational Autoencoder (VAE) latent features, enabling more flexible and expressive voice generation.
• Joint Training: The VAE and Large Language Model (LLM) are trained together, simplifying the training procedure and enhancing overall performance.
• Advanced Sampling Strategy: LlamaVoice implements a novel sampling strategy on the predicted distribution, resulting in more diverse latent representations.
• Flow-based Enhancement: Utilizes flow-based models to make the latent space more amenable to prediction by the LLM, improving the quality and consistency of generated voices.

To get started with LlamaVoice:

1. Clone the repository:

   git clone https://github.com/OpenT2S/LlamaVoice.git

2. Navigate to the project directory:

   cd LlamaVoice

3. Install the required dependencies:

   pip install -r requirements.txt

Generate voice samples with LlamaVoice using the following command:

python bin/generate_voice.py --input_text "Your text here"

For comprehensive usage instructions and additional options, please refer to our detailed documentation.

We welcome contributions to LlamaVoice! Whether you have suggestions, bug reports, or feature requests, please don't hesitate to open an issue or submit a pull request.