AudioLM: a Language Modeling Approach to Audio Generation
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Cited by 64 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio generation as a language modeling task in this representation space. We show how existing audio tokenizers provide different trade-offs between reconstruction quality and long-term structure, and we propose a hybrid tokenization scheme to achieve both objectives. Namely, we leverage the discretized activations of a masked language model pre-trained on audio to capture long-term structure and the discrete codes produced by a neural audio codec to achieve high-quality synthesis. By training on large corpora of raw audio waveforms, AudioLM learns to generate natural and coherent continuations given short prompts. When trained on speech, and without any transcript or annotation, AudioLM generates syntactically and semantically plausible speech continuations while also maintaining speaker identity and prosody for unseen speakers. Furthermore, we demonstrate how our approach extends beyond speech by generating coherent piano music continuations, despite being trained without any symbolic representation of music.
Key facts. Cited by 611 papers in the map (data through 7 Oct 2026). Builds on 57 of its 68 references that are in the map. First posted on arXiv 7 Sep 2022.
Builds on
57 of 68 references.
References · 57
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- 2018Efficient Neural Audio Synthesiscited by 218
- 2018Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Datasetcited by 215
- 2017Parallel WaveNet: Fast High-Fidelity Speech Synthesiscited by 214
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- 2019High Fidelity Speech Synthesis with Adversarial Networkscited by 90
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- 2020Data Augmenting Contrastive Learning of Speech Representations in the Time Domaincited by 63
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- 2021The Zero Resource Speech Challenge 2021: Spoken language modellingcited by 47
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- 2022Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modelingcited by 25
50 most cited of 57 All 57 (JSON)
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Citing papers per year: 2022: 15; 2023: 116; 2024: 188; 2025: 172; 2026: 120.
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