461 citations · 1.5k across the 14 of their papers we have counts for
17 papers
On the Tool Manipulation Capability of Open-source Large Language Models
Qiantong Xu, Fenglu Hong, Bo Li +3
Recent studies on software tool manipulation with large language models (LLMs) mostly rely on closed model APIs. The industrial adoption of these models is substantially constraine…
Flashlight: Enabling Innovation in Tools for Machine Learning
Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko +11
As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become chall…
MLS: A Large-Scale Multilingual Dataset for Speech Research
Vineel Pratap, Qiantong Xu, Anuroop Sriram +2
This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox an…
Self-training and Pre-training are Complementary for Speech Recognition
Qiantong Xu, Alexei Baevski, Tatiana Likhomanenko +5
Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they l…
Rethinking Evaluation in ASR: Are Our Models Robust Enough?
Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap +5
Is pushing numbers on a single benchmark valuable in automatic speech recognition? Research results in acoustic modeling are typically evaluated based on performance on a single da…
SlimIPL: Language-Model-Free Iterative Pseudo-Labeling
Tatiana Likhomanenko, Qiantong Xu, Jacob Kahn +2
Recent results in end-to-end automatic speech recognition have demonstrated the efficacy of pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Clas…
Self-Training for End-to-End Speech Translation
Juan Pino, Qiantong Xu, Xutai Ma +2
One of the main challenges for end-to-end speech translation is data scarcity. We leverage pseudo-labels generated from unlabeled audio by a cascade and an end-to-end speech transl…
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Lijun Ding, Yingjie Fei, Qiantong Xu +1
We develop a novel variant of the classical Frank-Wolfe algorithm, which we call spectral Frank-Wolfe, for convex optimization over a spectrahedron. The spectral Frank-Wolfe algori…
Iterative Pseudo-Labeling for Speech Recognition
Qiantong Xu, Tatiana Likhomanenko, Jacob Kahn +3
Pseudo-labeling has recently shown promise in end-to-end automatic speech recognition (ASR). We study Iterative Pseudo-Labeling (IPL), a semi-supervised algorithm which efficiently…
Scaling Up Online Speech Recognition Using ConvNets
Vineel Pratap, Qiantong Xu, Jacob Kahn +6
We design an online end-to-end speech recognition system based on Time-Depth Separable (TDS) convolutions and Connectionist Temporal Classification (CTC). We improve the core TDS a…
Libri-Light: A Benchmark for ASR with Limited or No Supervision
Jacob Kahn, Morgane Rivière, Weiyi Zheng +12
We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books f…
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures
Gabriel Synnaeve, Qiantong Xu, Jacob Kahn +6
We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functio…
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions
Awni Hannun, Ann Lee, Qiantong Xu +1
We propose a fully convolutional sequence-to-sequence encoder architecture with a simple and efficient decoder. Our model improves WER on LibriSpeech while being an order of magnit…
wav2letter++: The Fastest Open-source Speech Recognition System
Vineel Pratap, Awni Hannun, Qiantong Xu +5
This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor libra…
Fully Convolutional Speech Recognition
Neil Zeghidour, Qiantong Xu, Vitaliy Liptchinsky +3
Current state-of-the-art speech recognition systems build on recurrent neural networks for acoustic and/or language modeling, and rely on feature extraction pipelines to extract me…
An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yang Yuan +4
Evaluating generative adversarial networks (GANs) is inherently challenging. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address…
Learning a Repression Network for Precise Vehicle Search
Qiantong Xu, Ke Yan, Yonghong Tian
The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from large-scale image databases. Precise vehicle search, ai…