Whisper-GPT: Hybrid Continuous-Discrete Model for Speech and Music Generation
Researchers have proposed Whisper-GPT, a large language model that combines continuous audio representations (spectrograms) with discrete acoustic tokens to improve speech and music generation. The hybrid approach addresses a key limitation of token-only models: excessive context length requirements that reduce efficiency for high-fidelity audio. The model demonstrates improved perplexity and negative log-likelihood scores, suggesting potential advances in generative audio systems.
Whisper-GPT is a generative language model designed to handle both speech and music by simultaneously processing continuous and discrete audio representations within a single architecture. Recent advances in generative audio have relied heavily on discrete tokens derived from neural compression algorithms like EnCodec, but this approach creates computational challenges when maintaining sufficient context for high-fidelity generation across multiple frequency bands. By integrating continuous spectrograms with discrete acoustic tokens, Whisper-GPT captures complete audio information at each time step while retaining the sampling and prediction benefits of discrete token spaces. The researchers report improvements in perplexity and negative log-likelihood metrics for next-token prediction compared to token-only baselines. The work was presented at the 50th International Conference on Acoustics, Speech and Signal Processing.
What's missing
The paper does not provide comparative evaluation against other hybrid or continuous-representation approaches, only against token-based baselines. Subjective audio quality assessments (listening tests) and computational efficiency metrics (inference time, memory usage) are not reported in the abstract. The specific architecture details, training data, and model scale are not disclosed in the available abstract.
What different sources said
- arXiv cs.LGCenter
Whisper-GPT -- Continuous Discrete Hybrid Representation Language Models For Speech And Music
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