Spark-TTS：一种基于LLM的高效单流解耦语音Token文本转语音模型
Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens
AI总结：
提出基于LLM的单流解耦语音编解码模型Spark-TTS，结合Qwen2.5与思维链实现粗细粒度双重语音控制，在零样本语音克隆与定制化生成上达到SOTA。
摘要：
大型语言模型（LLMs）的最新进展推动了零样本文本转语音（TTS）合成的显著进步。然而，现有的基础模型依赖多阶段处理或复杂架构来预测多个码本，限制了效率和集成灵活性。为克服这些挑战，我们引入了Spark-TTS，这是一个由BiCodec驱动的新系统。BiCodec是一种单流语音编解码器，将语音分解为两种互补的token类型：用于语言内容的低比特率语义token，以及用于说话人属性的固定长度全局token。这种解耦表示结合Qwen2.5 LLM和思维链生成方法，实现了粗粒度控制（如性别、说话风格）和细粒度调整（如精确音高值、语速）。为促进可控TTS研究，我们引入了VoxBox，这是一个精心构建的包含100,000小时数据且带有全面属性标注的数据集。大量实验表明，Spark-TTS不仅实现了最先进的零样本语音克隆，还能生成高度可定制的语音，超越了基于参考合成的限制。
英文摘要：
Recent advancements in large language models (LLMs) have driven significant progress in zero-shot text-to-speech (TTS) synthesis. However, existing foundation models rely on multi-stage processing or complex architectures for predicting multiple codebooks, limiting efficiency and integration flexibility. To overcome these challenges, we introduce Spark-TTS, a novel system powered by BiCodec, a single-stream speech codec that decomposes speech into two complementary token types: low-bitrate semantic tokens for linguistic content and fixed-length global tokens for speaker attributes. This disentangled representation, combined with the Qwen2.5 LLM and a chain-of-thought (CoT) generation approach, enables both coarse-grained control (e.g., gender, speaking style) and fine-grained adjustments (e.g., precise pitch values, speaking rate). To facilitate research in controllable TTS, we introduce VoxBox, a meticulously curated 100,000-hour dataset with comprehensive attribute annotations. Extensive experiments demonstrate that Spark-TTS not only achieves state-of-the-art zero-shot voice cloning but also generates highly customizable voices that surpass the limitations of reference-based synthesis. Source code, pre-trained models, and audio samples are available at https://github.com/SparkAudio/Spark-TTS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
