AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes an enhanced VITS2 architecture, UNet-VITS, to address these long-standing challenges through three synergistic technical improvements—a unique combination not seen in prior research.
StellarTTS is introduced, a novel mobile-optimized NAR TTS framework based on a sparse temporal embedding strategy, enabling granular control of phoneme duration, pronunciation, and prosody and a semantic-aware codec that facilitates efficient single-stage decoding.
A receptive-field-scaling study shows that expanding self-attention beyond 15 phonemes provides no consistent gains in pitch, energy, or duration prediction, and introduces a fixed-receptive-field convolutional encoder that reduces the respective prediction errors.
Qwen-Audio-3.0-Gen-Preview is presented, a unified non-autoregressive framework that uses a Diffusion Transformer and a shared variational autoencoder (VAE) to generate the complete mixed waveform and demonstrates the potential of unified generation for temporally structured audio without task-specific branches.
The proposed DiTAR+, a dual-optimization framework for continuous-latent Autoregressive Diffusion Transformer models, introduces Dilated Context Sampling and Hierarchical Acoustic Masking to prevent shallow layers from attending to acoustic pre-context, explicitly decoupling semantic alignment from acoustic detail reco...
A novel E-Branchformer-based architecture that effectively leverages self-supervised speech representations for audio deepfake detection and employs parallel branches to simultaneously capture global contextual dependencies through multi-head self-attention and local temporal patterns through convolutional processing.
MiDashengLM-Gen is an end-to-end framework that couples a pre-trained Large Language Model (LLM) with per-token conditional flow matching for autoregressive, variable-length mixed-audio scene generation and drastically improves speech intelligibility over existing unified models.
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