[2406.17159] Exploring compressibility of transformer based text-to-music (TTM) models
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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2406.17159 (eess)
[Submitted on 24 Jun 2024]

Title:Exploring compressibility of transformer based text-to-music (TTM) models

Authors:Vasileios Moschopoulos, Thanasis Kotsiopoulos, Pablo Peso Parada, Konstantinos Nikiforidis, Alexandros Stergiadis, Gerasimos Papakostas, Md Asif Jalal, Jisi Zhang, Anastasios Drosou, Karthikeyan Saravanan
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Abstract:State-of-the art Text-To-Music (TTM) generative AI models are large and require desktop or server class compute, making them infeasible for deployment on mobile phones. This paper presents an analysis of trade-offs between model compression and generation performance of TTM models. We study compression through knowledge distillation and specific modifications that enable applicability over the various components of the TTM model (encoder, generative model and the decoder). Leveraging these methods we create TinyTTM (89.2M params) that achieves a FAD of 3.66 and KL of 1.32 on MusicBench dataset, better than MusicGen-Small (557.6M params) but not lower than MusicGen-small fine-tuned on MusicBench.
Comments: Proceedings of INTERSPEECH 2024
Subjects: Audio and Speech Processing (eess.AS); Multimedia (cs.MM); Sound (cs.SD)
Cite as: arXiv:2406.17159 [eess.AS]
  (or arXiv:2406.17159v1 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2406.17159

Submission history

From: Pablo Peso Parada [view email]
[v1] Mon, 24 Jun 2024 22:17:32 UTC (407 KB)
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