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Alexandre Defossez

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4 papers

1 author row

NeurIPS Conference 2023 Conference Paper

From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion

Robin San Roman
Yossi Adi
Antoine Deleforge
Romain Serizel
Gabriel Synnaeve
Alexandre Defossez

Deep generative models can generate high-fidelity audio conditioned on varioustypes of representations (e. g. , mel-spectrograms, Mel-frequency Cepstral Coefficients(MFCC)). Recently, such models have been used to synthesize audiowaveforms conditioned on highly compressed representations. Although suchmethods produce impressive results, they are prone to generate audible artifactswhen the conditioning is flawed or imperfect. An alternative modeling approach isto use diffusion models. However, these have mainly been used as speech vocoders(i. e. , conditioned on mel-spectrograms) or generating relatively low samplingrate signals. In this work, we propose a high-fidelity multi-band diffusion-basedframework that generates any type of audio modality (e. g. , speech, music, environmentalsounds) from low-bitrate discrete representations. At equal bit rate, the proposed approach outperforms state-of-the-art generative techniques in termsof perceptual quality. Training and evaluation code are available on the facebookresearch/audiocraft github project. Samples are available on the followinglink (https: //ai. honu. io/papers/mbd/).

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NeurIPS Conference 2023 Conference Paper

Simple and Controllable Music Generation

Jade Copet
Felix Kreuk
Itai Gat
Tal Remez
David Kant
Gabriel Synnaeve
Yossi Adi
Alexandre Defossez

We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i. e. , tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models, e. g. , hierarchically or upsampling. Following this approach, we demonstrate how MusicGen can generate high-quality samples, both mono and stereo, while being conditioned on textual description or melodic features, allowing better controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark. Through ablation studies, we shed light over the importance of each of the components comprising MusicGen. Music samples, code, and models are available at https: //github. com/facebookresearch/audiocraft

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NeurIPS Conference 2023 Conference Paper

Textually Pretrained Speech Language Models

Michael Hassid
Tal Remez
Tu Anh Nguyen
Itai Gat
Alexis Conneau
Felix Kreuk
Jade Copet
Alexandre Defossez

Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different model design choices such as the speech tokenizer, the pretrained textual model, and the dataset size. We find that model and dataset scale both play an important role in constructing better-performing SpeechLMs. Based on our observations, we present the largest (to the best of our knowledge) SpeechLM both in terms of number of parameters and training data. We additionally introduce two spoken versions of the StoryCloze textual benchmark to further improve model evaluation and advance future research in the field. We make speech samples, code and models publicly available.

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NeurIPS Conference 2018 Conference Paper

SING: Symbol-to-Instrument Neural Generator

Alexandre Defossez
Neil Zeghidour
Nicolas Usunier
Leon Bottou
Francis Bach

Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers for speech or music generation. Despite their successes, current state-of-the-art neural audio synthesizers such as WaveNet and SampleRNN suffer from prohibitive training and inference times because they are based on autoregressive models that generate audio samples one at a time at a rate of 16kHz. In this work, we study the more computationally efficient alternative of generating the waveform frame-by-frame with large strides. We present a lightweight neural audio synthesizer for the original task of generating musical notes given desired instrument, pitch and velocity. Our model is trained end-to-end to generate notes from nearly 1000 instruments with a single decoder, thanks to a new loss function that minimizes the distances between the log spectrograms of the generated and target waveforms. On the generalization task of synthesizing notes for pairs of pitch and instrument not seen during training, SING produces audio with significantly improved perceptual quality compared to a state-of-the-art autoencoder based on WaveNet as measured by a Mean Opinion Score (MOS), and is about 32 times faster for training and 2, 500 times faster for inference.

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