Title: Real-Time Streaming Mel Vocoding with Generative Flow Matching Show Chinese title

Title: 基于生成流匹配的实时流式梅尔声码器

Abstract: The task of Mel vocoding, i.e., the inversion of a Mel magnitude spectrogram to an audio waveform, is still a key component in many text-to-speech (TTS) systems today. Based on generative flow matching, our prior work on generative STFT phase retrieval (DiffPhase), and the pseudoinverse operator of the Mel filterbank, we develop MelFlow, a streaming-capable generative Mel vocoder for speech sampled at 16 kHz with an algorithmic latency of only 32 ms and a total latency of 48 ms. We show real-time streaming capability at this latency not only in theory, but in practice on a consumer laptop GPU. Furthermore, we show that our model achieves substantially better PESQ and SI-SDR values compared to well-established not streaming-capable baselines for Mel vocoding including HiFi-GAN. Show Chinese abstract

Abstract: Mel声码器的任务，即从Mel幅度频谱图逆变音频波形，仍然是许多文本到语音（TTS）系统中的关键组件。 基于生成流匹配，我们之前在生成STFT相位恢复（DiffPhase）的工作，以及Mel滤波器组的伪逆算子，我们开发了MelFlow，一种适用于16 kHz采样语音的流式生成Mel声码器，算法延迟仅为32毫秒，总延迟为48毫秒。我们不仅在理论上，而且在消费级笔记本电脑GPU上实际展示了这种延迟下的实时流式能力。 此外，我们表明，与一些已建立的非流式基线相比，我们的模型在PESQ和SI-SDR值方面取得了显著更好的结果，这些基线包括HiFi-GAN。