27 citations · 44 across the 16 of their papers we have counts for
17 papers
JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions
Leying Zhang, Bowen Shi, Haibin Wu +2
The rapid advancement of generative audio models has outpaced the development of robust evaluation methodologies. Existing objective metrics and general multimodal large language m…
SAM Audio Judge: A Unified Multimodal Framework for Perceptual Evaluation of Audio Separation
Helin Wang, Bowen Shi, Andros Tjandra +6
The performance evaluation remains a complex challenge in audio separation, and existing evaluation metrics are often misaligned with human perception, course-grained, relying on g…
Mosaic: Unlocking Long-Context Inference for Diffusion LLMs via Global Memory Planning and Dynamic Peak Taming
Liang Zheng, Bowen Shi, Yitao Hu +5
Diffusion-based large language models (dLLMs) have emerged as a promising paradigm, utilizing simultaneous denoising to enable global planning and iterative refinement. While these…
SAM Audio: Segment Anything in Audio
Bowen Shi, Andros Tjandra, John Hoffman +11
General audio source separation is a key capability for multimodal AI systems that can perceive and reason about sound. Despite substantial progress in recent years, existing separ…
MR-FlowDPO: Multi-Reward Direct Preference Optimization for Flow-Matching Text-to-Music Generation
Alon Ziv, Sanyuan Chen, Andros Tjandra +3
A key challenge in music generation models is their lack of direct alignment with human preferences, as music evaluation is inherently subjective and varies widely across individua…
Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound
Andros Tjandra, Yi-Chiao Wu, Baishan Guo +10
The quantification of audio aesthetics remains a complex challenge in audio processing, primarily due to its subjective nature, which is influenced by human perception and cultural…
Audiobox TTA-RAG: Improving Zero-Shot and Few-Shot Text-To-Audio with Retrieval-Augmented Generation
Mu Yang, Bowen Shi, Matthew Le +2
This work focuses on improving Text-To-Audio (TTA) generation on zero-shot and few-shot settings (i.e. generating unseen or uncommon audio events). Inspired by the success of Retri…
MusicFlow: Cascaded Flow Matching for Text Guided Music Generation
K R Prajwal, Bowen Shi, Matthew Lee +8
We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music aud…
MDCure: A Scalable Pipeline for Multi-Document Instruction-Following
Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu +2
Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. While LLMs have improved…
Movie Gen: A Cast of Media Foundation Models
Adam Polyak, Amit Zohar, Andrew Brown +85
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…
High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching
Gael Le Lan, Bowen Shi, Zhaoheng Ni +9
We introduce MelodyFlow, an efficient text-controllable high-fidelity music generation and editing model. It operates on continuous latent representations from a low frame rate 48…
Learning Fine-Grained Controllability on Speech Generation via Efficient Fine-Tuning
Chung-Ming Chien, Andros Tjandra, Apoorv Vyas +3
As the scale of generative models continues to grow, efficient reuse and adaptation of pre-trained models have become crucial considerations. In this work, we propose Voicebox Adap…
XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception
HyoJung Han, Mohamed Anwar, Juan Pino +4
Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments. Augmenting these systems with visual signals has the potent…
Towards Privacy-Aware Sign Language Translation at Scale
Phillip Rust, Bowen Shi, Skyler Wang +2
A major impediment to the advancement of sign language translation (SLT) is data scarcity. Much of the sign language data currently available on the web cannot be used for training…
Audiobox: Unified Audio Generation with Natural Language Prompts
Apoorv Vyas, Bowen Shi, Matthew Le +21
Audio is an essential part of our life, but creating it often requires expertise and is time-consuming. Research communities have made great progress over the past year advancing t…
Generative Pre-training for Speech with Flow Matching
Alexander H. Liu, Matt Le, Apoorv Vyas +3
Generative models have gained more and more attention in recent years for their remarkable success in tasks that required estimating and sampling data distribution to generate high…
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
Lili Yu, Bowen Shi, Ramakanth Pasunuru +24
We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. C…