ChatTTS

What is the project about?

ChatTTS is a text-to-speech (TTS) model specifically designed for conversational AI, like chatbots or virtual assistants powered by Large Language Models (LLMs). It focuses on generating natural-sounding speech suitable for dialogue.

What problem does it solve?

It addresses the need for more natural and expressive speech synthesis in conversational settings. Traditional TTS systems can sound robotic and lack the nuances of human conversation. ChatTTS aims to create a more engaging and realistic interaction by providing fine-grained control over prosody.

What are the features of the project?

• Conversational TTS: Optimized for dialogue-based tasks.
• Multi-speaker Support: Can generate speech for multiple speakers, enabling interactive conversations.
• Fine-grained Prosodic Control: Allows control over prosodic features like laughter, pauses, and interjections (e.g., "um," "uh").
• High-Quality Prosody: Claims to surpass most open-source TTS models in prosody (natural rhythm, stress, and intonation).
• Streaming Audio Generation: Supports generating audio in a streaming fashion.
• Zero-Shot Inference: Capable of generating speech for unseen speakers (with the DVAE encoder).
• Word Level Control: It is possible to control parameters at word level.

What are the technologies used in the project?

• Deep Learning: It's a generative speech model, implying the use of deep neural networks.
• PyTorch: The examples use torch and torchaudio.
• Hugging Face Transformers: The model is available on Hugging Face Hub, suggesting integration with the Transformers library.
• vLLM (optional): For faster inference on Linux.
• TransformerEngine (optional, under development): For NVIDIA GPUs.
• FlashAttention-2 (optional, experimental): For potential speed improvements on supported hardware.
• Vocos: Used as a pretrained vocoder.
• DVAE encoder

What are the benefits of the project?

• More Natural Conversations: Improves the user experience in applications involving spoken dialogue with AI.
• Expressive Speech: Allows for more engaging and realistic interactions.
• Research Platform: Provides a pretrained model and codebase for further research and development in conversational TTS.
• Open Source: The code (AGPLv3+) and a pre-trained model (CC BY-NC 4.0) are available, fostering community contributions and use in research/education.

What are the use cases of the project?

• LLM Assistants: Providing voice output for chatbots and virtual assistants.
• Interactive Voice Response (IVR) Systems: Creating more natural-sounding automated phone systems.
• Gaming: Generating realistic character dialogue.
• Accessibility Tools: Creating more expressive screen readers or communication aids.
• Audiobook Creation: Although not the primary focus, it could potentially be used for generating audiobooks with multiple characters.
• Education and Research: Studying and developing conversational AI and speech synthesis techniques.