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arXiv · 2102.01192

Generative Spoken Language Modeling from Raw Audio

Kushal Lakhotia·Evgeny Kharitonov·Wei-Ning Hsu·Yossi Adi·Adam Polyak·Benjamin Bolte·Tu-Anh Nguyen·Jade Copet·Alexei Baevski·Adelrahman Mohamed·Emmanuel Dupoux
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Abstract

We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. We set up baseline systems consisting of a discrete speech encoder (returning pseudo-text units), a generative language model (trained on pseudo-text), and a speech decoder (generating a waveform from pseudo-text) all trained without supervision and validate the proposed metrics with human evaluation. Across 3 speech encoders (CPC, wav2vec 2.0, HuBERT), we find that the number of discrete units (50, 100, or 200) matters in a task-dependent and encoder-dependent way, and that some combinations approach text-based systems.

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Kushal Lakhotia, Evgeny Kharitonov, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Benjamin Bolte, Tu-Anh Nguyen, Jade Copet, Alexei Baevski, Adelrahman Mohamed, Emmanuel Dupoux. 2021-02-01. Generative Spoken Language Modeling from Raw Audio. https://arxiv.org/abs/2102.01192

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