Search Results for author: Jacob Kahn

Found 17 papers, 12 papers with code

Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

3 code implementations2 Apr 2021 Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli

On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%.

Self-Supervised Learning

Iterative Pseudo-Labeling for Speech Recognition

1 code implementation19 May 2020 Qiantong Xu, Tatiana Likhomanenko, Jacob Kahn, Awni Hannun, Gabriel Synnaeve, Ronan Collobert

In particular, IPL fine-tunes an existing model at each iteration using both labeled data and a subset of unlabeled data.

Ranked #11 on Speech Recognition on LibriSpeech test-other (using extra training data)

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Rethinking Evaluation in ASR: Are Our Models Robust Enough?

1 code implementation22 Oct 2020 Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Paden Tomasello, Jacob Kahn, Gilad Avidov, Ronan Collobert, Gabriel Synnaeve

Finally, we show that training a single acoustic model on the most widely-used datasets - combined - reaches competitive performance on both research and real-world benchmarks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures

1 code implementation19 Nov 2019 Gabriel Synnaeve, Qiantong Xu, Jacob Kahn, Tatiana Likhomanenko, Edouard Grave, Vineel Pratap, Anuroop Sriram, Vitaliy Liptchinsky, Ronan Collobert

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 functions.

Ranked #16 on Speech Recognition on LibriSpeech test-other (using extra training data)

Language Modelling speech-recognition +1

Libri-Light: A Benchmark for ASR with Limited or No Supervision

2 code implementations17 Dec 2019 Jacob Kahn, Morgane Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdel-rahman Mohamed, Emmanuel Dupoux

Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER).

 Ranked #1 on Speech Recognition on Libri-Light test-other (ABX-within metric)

speech-recognition Speech Recognition

Differentiable Weighted Finite-State Transducers

1 code implementation2 Oct 2020 Awni Hannun, Vineel Pratap, Jacob Kahn, Wei-Ning Hsu

We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time.

Handwriting Recognition speech-recognition +1

OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks

1 code implementation24 Oct 2022 Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty

We present OLLA, an algorithm that optimizes the lifetime and memory location of the tensors used to train neural networks.

Reasoning over Public and Private Data in Retrieval-Based Systems

1 code implementation14 Mar 2022 Simran Arora, Patrick Lewis, Angela Fan, Jacob Kahn, Christopher Ré

We first define the PUBLIC-PRIVATE AUTOREGRESSIVE INFORMATION RETRIEVAL (PAIR) privacy framework for the novel retrieval setting over multiple privacy scopes.

Fact Checking Information Retrieval +3

The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment

1 code implementation13 Feb 2023 Jared Fernandez, Jacob Kahn, Clara Na, Yonatan Bisk, Emma Strubell

In this work, we examine this phenomenon through a series of case studies analyzing the effects of model design decisions, framework paradigms, and hardware platforms on total model latency.

Computational Efficiency

Scaling Up Online Speech Recognition Using ConvNets

no code implementations27 Jan 2020 Vineel Pratap, Qiantong Xu, Jacob Kahn, Gilad Avidov, Tatiana Likhomanenko, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

We design an online end-to-end speech recognition system based on Time-Depth Separable (TDS) convolutions and Connectionist Temporal Classification (CTC).

speech-recognition Speech Recognition

SlimIPL: Language-Model-Free Iterative Pseudo-Labeling

no code implementations22 Oct 2020 Tatiana Likhomanenko, Qiantong Xu, Jacob Kahn, Gabriel Synnaeve, Ronan Collobert

We improve upon the IPL algorithm: as the model learns, we propose to iteratively re-generate transcriptions with hard labels (the most probable tokens), that is, without a language model.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

RA-DIT: Retrieval-Augmented Dual Instruction Tuning

no code implementations2 Oct 2023 Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, Luke Zettlemoyer, Scott Yih

Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build.

Few-Shot Learning Open-Domain Question Answering +1

Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

no code implementations12 Mar 2024 Sainbayar Sukhbaatar, Olga Golovneva, Vasu Sharma, Hu Xu, Xi Victoria Lin, Baptiste Rozière, Jacob Kahn, Daniel Li, Wen-tau Yih, Jason Weston, Xian Li

We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge.

Arithmetic Reasoning Code Generation +6

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