Spark-TTS: An Efficient Text-to-Speech Tool Based on LLM | Single-Stream Decoupled Speech Coding Technology Analysis

Spark-TTSis an innovative text-to-speech (TTS) model developed by the SparkAudio team, with a core based on theBiCodec Architecturewith large-scale language modeling (LLM) technology, achieving a breakthrough in both efficiency and sound quality in the field of speech synthesis.

I. Technical architecture: single-stream decoupled speech coding

1. BiCodec Design Principles
   Spark-TTS has made this possible through the proposedBiCodec Encoderthat decomposes the speech signal into two complementary types of tokens:
   • Low bit rate semantic tokens: Focus on encoding linguistic content (e.g., phonemes, intonation)
   • Fixed-length global token: Extraction of speaker characteristics (timbre, pronunciation habits, etc.)
     This decoupled design reduces the model parameters by 301 TP3T, while maintaining 98.21 TP3T of sonic reproduction.
2. LLM and CoT Generation Framework
   combiningQwen2.5 Large Language ModelingWith the Chain-of-Thought (CoT) generation method, the system is able to dynamically optimize speech rhythms:
   • Real-time analysis of textual emotional coloring (e.g., questioning, emphasis)
   • Automatic adjustment of pause positions and speed changes

II. Core strengths: efficiency and quality go hand in hand

• Increased generation speed: 2.7 times faster inference compared to traditional TTS models (42.5 speech frames per second measured)¹
• Multi-language supportSupports mixed input and seamless switching between 12 languages, including Chinese, English, Japanese, and Korean.
• tone control: Only 3 seconds of reference audio is needed to clone the target tone, with a similarity of 93.61 TP3T²

III. Application scenarios

1. Intelligent Customer Service: Generate multilingual responses with emotional expressions in real time
2. Audio content creation: Batch generation of high quality audiobooks/podcasts with support for custom character timbre
3. Accessibility: Natural and smooth interactive voice for visually impaired users

Developers can access the full code with pre-trained models via the GitHub repository, project offerings:

• Out-of-the-box Python API interface
• Lightweight deployment options (minimum 2GB video memory GPU support)
• Multi-scenario configuration templates (live streaming, education, healthcare, etc.)

In their paper "Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens", the research team verified that the model achieves a score of 4.31 (out of 5) on the MOS (mean opinion score) test , while keeping the inference delay within 120ms. This breakthrough signifies that speech synthesis technology has officially entered a new era of "high efficiency and high fidelity".