Continuous latent representations underlie recent diffusion-based lyrics-to-song and text-to-music systems, yet aligning lyric conditions with these latents remains difficult: existing systems resort to generator-side machinery such as sentence-level timestamps, auxiliary alignment objectives, or semi-autoregressive de...
Guo-Bin Ma, Yue-Peng Jiang, Da-Ke Guo et al.· 0 citations
StreamTN is proposed, a lightweight LLM-based Chinese streaming TN model that employs a dual-track streaming framework in which input tokens and output tokens are processed on two parallel tracks, enabling low-latency real-time inference without complex prompting.
Wen-Hao Li, Jin-Rui Liang, Hao-Yu Zhang et al.· 0 citations
Discrete audio tokenization has become the critical interface between raw waveforms and autoregressive modeling in recent music generation. As a result, music tokenizers must simultaneously support high-fidelity reconstruction and produce discrete sequences that remain amenable to language modeling. Existing reconstruc...
Hua-Kang Chen, Guo-Bin Ma, Yue-Peng Jiang et al.· 0 citations
SphereVAE is proposed, which constrains the VAE latent space to the unit hypersphere and regularizes the latent distribution toward a uniform prior, so that information is encoded mainly by directional variation, providing a bounded geometric target for autoregressive prediction and reducing the risk of norm drift.
Hao-Yu Zhang, Jing-Bin Hu, Han-Ke Xie et al.· 0 citations
X-Pred MeanFlow is proposed, a few-step streaming token-to-mel decoder that reparameterizes MeanFlow with mel-space prediction and introduces layer-selective block-wise attention to enable continuous chunk-wise generation with bounded context.
Han-Ke Xie, Xia-Ming Ren, Qi-Rui Zhan et al.· 0 citations
SemBridge is proposed, a training-only semantic-token anchoring framework for continuous-latent autoregressive speech generation and demonstrates that explicit semantic-token supervision for autoregressive state learning is an effective and general direction for continuous speech generation.
Han-Ke Xie, Haopeng Lin, J. Qian et al.· 0 citations
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