With the development of automatic speech recognition (ASR) and text-to-speech (TTS) technology, high-quality voice conversion (VC) can be achieved by extracting source content information and target speaker information to reconstruct waveforms. However, current methods still require improvement in terms of inference speed. In this study, we propose a lightweight VITS-based VC model that uses the HuBERT-Soft model to extract content information features without speaker information. Through subjective and objective experiments on synthesized speech, the proposed model demonstrates competitive results in terms of naturalness and similarity. Importantly, unlike the original VITS model, we use the inverse short-time Fourier transform (iSTFT) to replace the most computationally expensive part. Experimental results show that our model can generate samples at over 5000 kHz on the 3090 GPU and over 250 kHz on the i9-10900K CPU, achieving competitive speed for the same hardware configuration.

翻译：摘要：随着自动语音识别（ASR）和文本转语音（TTS）技术的发展，可通过提取源语言内容信息与目标说话人信息来重构波形，从而实现高质量的语音转换（VC）。然而，现有方法在推理速度方面仍有改进空间。本研究提出一种轻量级基于VITS的语音转换模型，该模型采用HuBERT-Soft模型提取不含说话人信息的内容特征。通过对合成语音进行主观与客观实验，所提模型在自然度和相似度方面展现出具有竞争力的性能。重要的是，与原始VITS模型不同，我们使用逆短时傅里叶变换（iSTFT）替代了计算最密集的部分。实验结果表明，在3090 GPU上，我们的模型能以超过5000 kHz的频率生成样本；在i9-10900K CPU上，则能以超过250 kHz的频率生成样本，在相同硬件配置下实现了具有竞争力的速度。