Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model

Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically for native, joint audio-video generation. Leveraging a dual-branch Diffusion Transformer architecture, the model integrates a cross-modal joint module with a specialized multi-stage data pipeline, achieving exceptional audio-visual synchronization and superior generation quality. To ensure practical utility, we implement meticulous post-training optimizations, including Supervised Fine-Tuning (SFT) on high-quality datasets and Reinforcement Learning from Human Feedback (RLHF) with multi-dimensional reward models. Furthermore, we introduce an acceleration framework that boosts inference speed by over 10X. Seedance 1.5 pro distinguishes itself through precise multilingual and dialect lip-syncing, dynamic cinematic camera control, and enhanced narrative coherence, positioning it as a robust engine for professional-grade content creation. Seedance 1.5 pro is now accessible on Volcano Engine at https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?type=GenVideo.

Transfer Learning with Pseudo Multi-Label Birdcall Classification for DS@GT BirdCLEF 2024

We present working notes for the DS@GT team on transfer learning with pseudo multi-label birdcall classification for the BirdCLEF 2024 competition, focused on identifying Indian bird species in recorded soundscapes. Our approach utilizes production-grade models such as the Google Bird Vocalization Classifier, BirdNET, and EnCodec to address representation and labeling challenges in the competition. We explore the distributional shift between this year's edition of unlabeled soundscapes representative of the hidden test set and propose a pseudo multi-label classification strategy to leverage the unlabeled data. Our highest post-competition public leaderboard score is 0.63 using BirdNET embeddings with Bird Vocalization pseudo-labels. Our code is available at https://github.com/dsgt-kaggle-clef/birdclef-2024