461 citations · 1.5k across the 14 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…