Seed-TTS: A Family of High-Quality Versatile Speech Generation Models

Seed-TTS is a family of high-quality, versatile speech generation models developed by the Seed Team at ByteDance. This innovative model is designed to produce speech that is virtually indistinguishable from human speech. It excels in speech in-context learning, achieving remarkable performance in speaker similarity and naturalness, comparable to ground truth human speech in both objective and subjective evaluations. With fine-tuning, Seed-TTS can achieve even higher subjective scores across these metrics.

Customers

Seed-TTS caters to a diverse range of customers, including:

• Voice Application Developers: Developers looking for high-quality, natural-sounding TTS models for their applications.
• Content Creators: Individuals or organizations creating audio content such as audiobooks, podcasts, and voiceovers.
• Language Learning Platforms: Platforms that require accurate and natural speech synthesis for language learning purposes.
• Accessibility Solutions Providers: Companies developing solutions for visually impaired users who rely on screen readers and other assistive technologies.

Problems and Solution

Problems

Seed-TTS addresses several key issues in the field of text-to-speech synthesis:

• Lack of Naturalness: Many existing TTS models produce speech that sounds robotic and unnatural.
• Limited Controllability: Difficulty in controlling various speech attributes such as emotion and expressiveness.
• Dependence on Pre-estimated Phoneme Durations: Non-autoregressive models often require pre-estimated phoneme durations, limiting their flexibility.

Solution

Seed-TTS solves these problems by introducing a large-scale autoregressive TTS model capable of generating highly expressive and diverse speech. It offers superior controllability over various speech attributes and employs a self-distillation method for speech factorization. Additionally, a reinforcement learning approach is used to enhance model robustness, speaker similarity, and controllability. The non-autoregressive variant, Seed-TTSDiT, utilizes a fully diffusion-based architecture, eliminating the need for pre-estimated phoneme durations and enabling end-to-end speech generation.

Use Case

Seed-TTS can be used in various applications, including voice assistants, audiobooks, language learning tools, and accessibility solutions. For instance, an audiobook producer can use Seed-TTS to create highly natural and expressive narrations, enhancing the listening experience for users. Similarly, a language learning platform can leverage Seed-TTS to provide accurate and natural-sounding pronunciations, aiding learners in their language acquisition process.

Frequently Asked Questions

1. What is Seed-TTS?

   Seed-TTS is a family of high-quality, versatile speech generation models developed by ByteDance, capable of generating speech that is virtually indistinguishable from human speech.

2. Is there any plan to open source inference or fine-tuning part of Seedtts?

   Due to considerations for AI safety, ByteDance will not be releasing the source code and model weights of Seed-TTS. However, BytedanceSpeech/seed-tts-eval repository contains the objective test set as proposed in their project, along with the scripts for metric calculations. They invite users to experience the speech generation feature within ByteDance products.

3. How does Seed-TTS achieve natural-sounding speech?

   Seed-TTS achieves natural-sounding speech through a large-scale autoregressive model, fine-tuning for higher subjective scores, and a self-distillation method for speech factorization.

4. What are the applications of Seed-TTS?

   Seed-TTS can be used in voice assistants, audiobooks, language learning tools, and accessibility solutions, among other applications.

5. What is the difference between the autoregressive and non-autoregressive variants of Seed-TTS?

   The autoregressive variant generates speech tokens based on condition text and speech, while the non-autoregressive variant, Seed-TTSDiT, uses a fully diffusion-based architecture for end-to-end speech generation without relying on pre-estimated phoneme durations.

6. Who can benefit from Seed-TTS?

   Researchers and developers in speech synthesis, businesses looking to integrate advanced TTS capabilities, and content creators involved in producing audio content can all benefit from Seed-TTS.

7. What is Seed-TTSDiT?

   Seed-TTSDiT is a non-autoregressive variant of Seed-TTS that utilizes a fully diffusion-based architecture, achieving comparable performance to the autoregressive variant with added flexibility.

8. How does Seed-TTS handle multilingual support?

   Seed-TTS supports multilingual speech generation with high quality and naturalness, and the Seed-TTSDiT variant further enhances its robustness and flexibility in handling multiple languages.

9. What is in-context learning in Seed-TTS?

   In-context learning allows Seed-TTS to generalize from limited examples, providing high-quality speech output in new contexts without extensive retraining.

10. How does Seed-TTS ensure speaker similarity?

Seed-TTS incorporates advanced speaker modeling techniques to ensure that the generated speech closely matches the characteristics of the target speaker, achieving high similarity scores.

11. Can Seed-TTS be fine-tuned for specific use cases?

    Yes, Seed-TTS offers fine-tuning capabilities, allowing users to tailor the model to specific use cases and achieve even higher subjective scores in naturalness and speaker similarity.