Baichuan-Omni-1.5 Technical Report
원문 초록
We introduce Baichuan-Omni-1.5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. To achieve fluent and high-quality interaction across modalities without compromising the capabilities of any modality, we prioritized optimizing three key aspects. First, we establish a comprehensive data cleaning and synthesis pipeline for multimodal data, obtaining about 500B high-quality data (text, audio, and vision). Second, an audio-tokenizer (Baichuan-Audio-Tokenizer) has been designed to capture both semantic and acoustic information from audio, enabling seamless integration and enhanced compatibility with MLLM. Lastly, we designed a multi-stage training strategy that progressively integrates multimodal alignment and multitask fine-tuning, ensuring effective synergy across all modalities. Baichuan-Omni-1.5 leads contemporary models (including GPT4o-mini and MiniCPM-o 2.6) in terms of comprehensive omni-modal capabilities. Notably, it achieves results comparable to leading models such as Qwen2-VL-72B across various multimodal medical benchmarks.
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BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance
Fast Text-to-Audio Generation with Adversarial Post-Training
OmniAudio: Generating Spatial Audio from 360-Degree Video
Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception
토론
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