310 citations · 498 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
Matthew Le, Apoorv Vyas, Bowen Shi +8
Large-scale generative models such as GPT and DALL-E have revolutionized the research community. These models not only generate high fidelity outputs, but are also generalists whic…
Pkwrap: a PyTorch Package for LF-MMI Training of Acoustic Models
Srikanth Madikeri, Sibo Tong, Juan Zuluaga-Gomez +3
We present a simple wrapper that is useful to train acoustic models in PyTorch using Kaldi's LF-MMI training framework. The wrapper, called pkwrap (short form of PyTorch kaldi wrap…