793 citations · 1.5k across the 17 of their papers we have counts for
19 papers
Voxtral TTS
Mistral-AI, :, Alexander H. Liu +186
We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid…
IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages
Tahir Javed, Janki Atul Nawale, Eldho Ittan George +18
We present INDICVOICES, a dataset of natural and spontaneous speech containing a total of 7348 hours of read (9%), extempore (74%) and conversational (17%) audio from 16237 speaker…
Pushing the performances of ASR models on English and Spanish accents
Pooja Chitkara, Morgane Riviere, Jade Copet +2
Speech to text models tend to be trained and evaluated against a single target accent. This is especially true for English for which native speakers from the United States became t…
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation
Marvin Lavechin, Marianne Métais, Hadrien Titeux +7
Most automatic speech processing systems register degraded performance when applied to noisy or reverberant speech. But how can one tell whether speech is noisy or reverberant? We…
Learned Force Fields Are Ready For Ground State Catalyst Discovery
Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose +6
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery. Our key finding is that relaxation using forces fro…
Textless Speech Emotion Conversion using Discrete and Decomposed Representations
Felix Kreuk, Adam Polyak, Jade Copet +7
Speech emotion conversion is the task of modifying the perceived emotion of a speech utterance while preserving the lexical content and speaker identity. In this study, we cast the…
ASR4REAL: An extended benchmark for speech models
Morgane Riviere, Jade Copet, Gabriel Synnaeve
Popular ASR benchmarks such as Librispeech and Switchboard are limited in the diversity of settings and speakers they represent. We introduce a set of benchmarks matching real-life…
Text-Free Prosody-Aware Generative Spoken Language Modeling
Eugene Kharitonov, Ann Lee, Adam Polyak +8
Speech pre-training has primarily demonstrated efficacy on classification tasks, while its capability of generating novel speech, similar to how GPT-2 can generate coherent paragra…
The Zero Resource Speech Challenge 2021: Spoken language modelling
Ewan Dunbar, Mathieu Bernard, Nicolas Hamilakis +6
We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the…
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
Changhan Wang, Morgane Rivière, Ann Lee +6
We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised repre…
The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling
Tu Anh Nguyen, Maureen de Seyssel, Patricia Rozé +5
We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource S…
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das +14
Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reli…
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal +14
Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer produc…
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
Eugene Kharitonov, Morgane Rivière, Gabriel Synnaeve +4
Contrastive Predictive Coding (CPC), based on predicting future segments of speech based on past segments is emerging as a powerful algorithm for representation learning of speech…
Unsupervised pretraining transfers well across languages
Morgane Rivière, Armand Joulin, Pierre-Emmanuel Mazaré +1
Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting. This assumes the existence of a parallel…
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…
Fully Parallel Hyperparameter Search: Reshaped Space-Filling
M. -L. Cauwet, C. Couprie, J. Dehos +5
Space-filling designs such as scrambled-Hammersley, Latin Hypercube Sampling and Jittered Sampling have been proposed for fully parallel hyperparameter search, and were shown to be…
Inspirational Adversarial Image Generation
Baptiste Rozière, Morgane Riviere, Olivier Teytaud +3
The task of image generation started to receive some attention from artists and designers to inspire them in new creations. However, exploiting the results of deep generative model…
GDPP: Learning Diverse Generations Using Determinantal Point Process
Mohamed Elfeki, Camille Couprie, Morgane Riviere +1
Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images. An essential characteri…