F3-Tokenizer: Taming Audio Autoencoder Latents for Understanding and Generation
Abstract
Continuous audio autoencoders reconstruct waveforms well but often produce latents with weak structure for understanding, while self-supervised audio encoders capture semantics but are not directly decodable. This mismatch complicates a single audio tokenizer that must support both understanding and generation. We adapt continuous autoencoder latents to this setting with two components: a noise-regularized autoencoder bottleneck and a latent-side representation encoder. The bottleneck uses channel normalization and stochastic perturbation instead of KL-based variational training, yielding scale-controlled continuous latents for reconstruction and autoregressive generation. The representation encoder is trained on frozen autoencoder latents with RQ-MTP and frozen-LLM supervision. The resulting tokenizer provides high-dimensional representations for understanding while preserving normalized continuous latents as generation targets
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the F3-Tokenizer, which effectively adapts audio autoencoder latents for both understanding and generation tasks, thereby enhancing the capabilities of audio representation learning. The innovative approach and comprehensive evaluation position this work as a valuable addition to the field of machine learning in audio processing.
Methodology
The paper introduces the F3-Tokenizer, which innovatively combines a noise-regularized autoencoder bottleneck with a latent-side representation encoder to address the dual challenges of audio understanding and generation. The methodology is well-structured, employing a normalized continuous latent space that avoids traditional KL divergence constraints, thus enhancing robustness and decodability. The use of RQ-MTP and frozen LLM supervision for training the representation encoder is a notable strength, as it effectively bridges the gap between acoustic fidelity and semantic understanding.
Results & evaluation
The experiments are comprehensive, evaluating the proposed tokenizer across three dimensions: reconstruction quality, audio understanding, and generation capabilities. The probing tasks and downstream applications, including TTS and text-to-audio generation, demonstrate the effectiveness of the proposed methods. However, the paper lacks detailed quantitative results in some areas, which could strengthen the evaluation.
Reproducibility
The paper provides a clear description of the methodology and training pipeline, which aids reproducibility. However, the absence of a public code repository or demo limits the ability for others to replicate the results fully. Including specific hyperparameters and dataset details would further enhance reproducibility.
Limitations
The paper acknowledges limitations, such as the dependency on external objectives and teacher models, which may affect performance based on the quality and coverage of training data. Additionally, the separation of latent spaces for understanding and generation may not be optimal for all applications, suggesting a need for future work to explore unified representations.
Broader impact
The F3-Tokenizer has significant potential applications in various audio processing tasks, including speech recognition, music generation, and sound event classification. Its ability to provide high-dimensional representations while maintaining acoustic fidelity could lead to advancements in multimodal systems and improve user experiences in audio-related applications. The main contribution of this paper is the introduction of the F3-Tokenizer, which effectively adapts audio autoencoder latents for both understanding and generation tasks, thereby enhancing the capabilities of audio representation learning. The innovative approach and comprehensive evaluation position this work as a valuable addition to the field of machine learning in audio processing.
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