MSAVBench is the first comprehensive benchmark for multi-shot audio-video generation, spanning video, audio, shot, and reference dimensions with an adaptive evaluation framework that reaches 91.5% Spearman correlation with human judgments.
W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training
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TextPro-SLM reduces the speech-text modality gap by feeding an LLM backbone with synchronized text tokens and prosody embeddings from WhisperPro, achieving lowest gap scores at 3B/7B scales with roughly 1,000 hours of audio.
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MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation
MSAVBench is the first comprehensive benchmark for multi-shot audio-video generation, spanning video, audio, shot, and reference dimensions with an adaptive evaluation framework that reaches 91.5% Spearman correlation with human judgments.
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Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM
TextPro-SLM reduces the speech-text modality gap by feeding an LLM backbone with synchronized text tokens and prosody embeddings from WhisperPro, achieving lowest gap scores at 3B/7B scales with roughly 1,000 hours of audio.