ChatTTS

Github.com: A generative speech model for everyday conversations. Contribute to the ChatTTS repository development by 2noise on GitHub.

GitHub Repo: ChatTTS Code from 2noise

ChatTTS -Introduction

ChatTTS is a text-to-speech model specially crafted for conversational scenarios like LLM assistant. It provides conversational TTS tailored for interactive dialogues involving multiple speakers, ensuring a natural and expressive speech synthesis. The model stands out in predicting and managing detailed prosodic elements such as laughter, pauses, and interjections, outperforming many open-source TTS models in prosody aspects. Leveraging a core model trained on 100,000+ hours of Chinese and English audio data, ChatTTS facilitates further research and development through pretrained models. The platform's future plans encompass releasing base models as open source, enabling streaming audio generation, and introducing versions with multi-emotion control. It is crucial to highlight that ChatTTS is meant for academic and research purposes exclusively, and users are advised to use the technology responsibly and ethically. For queries regarding the model and future plans, users can reach out to the team at [email protected].

ChatTTS -Features

Product Characteristics of ChatTTS:

Overview:

  • ChatTTS is a conversational speech model tailored for daily dialogues.
  • It offers multilingual support, including English and Chinese.
  • The model is fine-tuned for dialogue tasks, ensuring natural and expressive speech generation.

Core Objective and Target Audience:

  • Core Objective: ChatTTS is crafted for dialogue applications like LLM assistant, specializing in conversational text-to-speech functions.
  • Target Audience: Users seeking a text-to-speech model optimized for dialogues, allowing precise control over prosodic elements.

Feature Specifics and Functions:

  • Interactive TTS: ChatTTS facilitates interactive dialogues with multi-speaker support.
  • Precise Control: Users can anticipate and manage prosodic elements such as laughter, pauses, and interjections.
  • Enhanced Prosody: ChatTTS outperforms many open-source TTS models in prosody, offering pretrained models for further exploration.

User Advantages:

  • Natural and Expressive Speech Generation: ChatTTS ensures natural and expressive speech output for immersive dialogues.
  • Fine-grained Prosodic Control: Users can finely adjust prosodic elements to enhance speech quality.
  • Multilingual Support: Trained on Chinese and English audio data, ChatTTS caters to diverse language requirements.

Compatibility and Incorporation:

  • ChatTTS is compatible with various platforms and can be seamlessly integrated into diverse text-to-speech applications.
  • Integration with Hugging Face enhances ChatTTS with additional functionalities.

Client Testimonials and Use Cases:

  • Positive user feedback emphasizes ChatTTS' effectiveness in producing high-quality dialogue speech.
  • Use cases showcase ChatTTS' practical applications in enriching user interactions through natural speech synthesis.

Access and Activation Procedure:

  • Users can access ChatTTS via the GitHub repository provided by 2noise.
  • Activation entails cloning the repository, installing necessary dependencies, and following guidelines for usage and customization.

ChatTTS -Frequently Asked Questions

常见问题

  1. ChatTTS 需要多少 VRAM?推理速度如何?

    • 对于 30 秒的音频剪辑,至少需要 4GB 的 GPU 内存。该模型在 4090 GPU 上可以生成大约每秒约 7 个语义标记对应的音频。实时因子(RTF)约为 0.3。
  2. 我遇到了模型稳定性问题,比如多说话者问题或音频质量差的情况。有什么建议吗?

    • 这些问题在像 ChatTTS 这样的自回归模型中很常见。完全避免它们可能有挑战性。您可以尝试生成多个样本以找到合适的结果。
  3. 除了控制笑声,还有其他可以控制的元素吗?我们可以管理其他情绪吗?

    • 在当前发布的模型中,唯一的令牌级控制单元是 [laugh]、[uv_break] 和 [lbreak]。未来版本可能会包括具有额外情绪控制功能的模型。

ChatTTS -Data Analysis

Latest Traffic Information

  • Monthly Visits

    437.914238M

  • Bounce Rate

    38.34%

  • Pages Per Visit

    6.50

  • Visit Duration

    00:07:17

  • Global Rank

    78

  • Country Rank

    111

Visits Over Time

Traffic Sources

  • direct:
    51.33%
  • referrals:
    11.05%
  • social:
    6.66%
  • mail:
    0.86%
  • search:
    30.08%
  • paidReferrals:
    0.03%
More data

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