超轻量语音合成模型，4400万参数媲美主流零样本模型
SupertonicTTS: Towards Highly Efficient and Streamlined Text-to-Speech System
- 用低维隐空间+卷积块实现极简架构
- 4400万参数达成与主流模型相当的音质
- 无需音素转换模块，直接处理字符级输入
我们提出SupertonicTTS，一种高效简洁的文本到语音合成系统。该系统由语音自编码器（生成连续隐表示）、基于流匹配的文本到隐向量映射模块以及话语级时长预测器组成。为实现轻量化，采用低维隐空间、隐向量时间压缩及ConvNeXt块。通过直接处理原始字符级文本并使用交叉注意力对齐，省去音素转换（G2P）模块和外部对齐器。此外，提出上下文共享批扩展方法，在极低内存与I/O开销下加速损失收敛并稳定对齐。实验表明，仅需4400万参数即可达到与当前零样本TTS模型相当的性能，显著降低架构复杂度与计算成本。音频样例见：https://supertonictts.github.io/
原文摘要 · Abstract (English)
We introduce SupertonicTTS, a novel text-to-speech (TTS) system designed for efficient and streamlined speech synthesis. SupertonicTTS comprises three components: a speech autoencoder for continuous latent representation, a text-to-latent module leveraging flow-matching for text-to-latent mapping, and an utterance-level duration predictor. To enable a lightweight architecture, we employ a low-dimensional latent space, temporal compression of latents, and ConvNeXt blocks. The TTS pipeline is further simplified by operating directly on raw character-level text and employing cross-attention for text-speech alignment, thus eliminating the need for grapheme-to-phoneme (G2P) modules and external aligners. In addition, we propose context-sharing batch expansion that accelerates loss convergence and stabilizes text-speech alignment with minimal memory and I/O overhead. Experimental results demonstrate that SupertonicTTS delivers performance comparable to contemporary zero-shot TTS models with only 44M parameters, while significantly reducing architectural complexity and computational cost. Audio samples are available at: https://supertonictts.github.io/.
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