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We trained HierSpeech++ with a batch size of 160 for 1,000k steps on eight NVIDIA A6000 GPUs.

For voice conversion, we first extract the semantic representation by MMS from the audio at 16 kHz, and F0 using the YAPPT algorithm.

The content encoder of the TTV consists of 16 layers of noncausal WaveNet with a hidden size of 256 and a kernel size of five.

In this stage, we simply upsample a low-resolution speech waveform to a high-resolution speech waveform from 16 kHz to 48 kHz as illustrated in Fig 5.

Following VITS [35], we utilize a variational autoencoder and a monotonic alignment search (MAS) to align the text and speech internally

We propose a hierarchical speech synthesizer as the backbone speech synthesizer for HierSpeech++

In this study, we propose HierSpeech++, a human-level zeroshot speech synthesis model in terms of naturalness and voice similarity.

Diffusion models have also demonstrated their powerful generative performances in speech synthesis.

Recently, neural audio codec model, have replaced conventional acoustic representations with a high-compressed audio codec.

Mamba excels in DNA modeling, outperforming HyenaDNA and Transformer++ in scaling laws for model size and context length.