Scaling Speech Tokenizers with Diffusion Autoencoders
Abstract
Speech tokenizers are foundational to speech language models, yet existing approaches face two major challenges: (1) balancing trade-offs between encoding semantics for understanding and acoustics for reconstruction, and (2) achieving low bit rates and low token rates. We propose Speech Diffusion Tokenizer (SiTok), a diffusion autoencoder that jointly learns semantic-rich representations through supervised learning and enables high-fidelity audio reconstruction with diffusion. We scale SiTok to 1.6B parameters and train it on 2 million hours of speech. Experiments show that SiTok outperforms strong baselines on understanding, reconstruction and generation tasks, at an extremely low token rate of $12.5$ Hz and a bit-rate of 200 bits-per-second.
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 SiTok, a novel speech tokenizer that utilizes a diffusion autoencoder to achieve high-quality speech representation and reconstruction while maintaining low bit and token rates. This work significantly advances the field of speech processing by addressing key challenges in existing methodologies and providing a robust framework for future research and applications.
Methodology
The proposed methodology of the Speech Diffusion Tokenizer (SiTok) is innovative, leveraging a diffusion autoencoder to jointly optimize quantization and reconstruction. The introduction of semantic regularization through a CTC decoder is a significant advancement, allowing the model to maintain semantic integrity while achieving high compression rates. The architecture effectively combines the strengths of diffusion models with the need for efficient speech tokenization, addressing the limitations of previous approaches that often relied on heuristic compromises. The design choices, such as the use of mel-spectrograms and the focus on low token rates, are well-justified and align with the objectives of scalable language modeling.
Results & evaluation
The experiments conducted are extensive, utilizing a large dataset of 2 million hours of speech, which enhances the robustness of the findings. The paper provides a comprehensive evaluation across various tasks, including speech reconstruction, emotion recognition, and automatic speech recognition, demonstrating that SiTok outperforms existing baselines significantly. The results are well-presented, with clear metrics for comparison, and the ablation studies effectively highlight the contributions of different components of the model.
Reproducibility
The paper includes detailed descriptions of the model architecture, training settings, and evaluation protocols, which are crucial for reproducibility. The authors have made efforts to ensure that their work can be replicated, which is commendable. However, the absence of a publicly available code repository limits the ease of reproducibility for practitioners in the field.
Limitations
While the proposed model shows promising results, it may still face challenges in real-world applications, such as the potential for overfitting due to the large number of parameters (1.6B) and the reliance on extensive training data. Additionally, the computational efficiency during inference, although improved with shortcut fine-tuning, may still be a concern for deployment in resource-constrained environments. The paper does not address the ethical implications of misuse in generating synthetic speech, which is an important consideration in today's landscape.
Broader impact
The development of SiTok has significant implications for speech technology, particularly in applications such as automatic speech recognition, text-to-speech systems, and conversational agents. By enabling high-fidelity audio reconstruction at low bit rates, this work could enhance accessibility and usability in various domains, including assistive technologies and real-time communication systems. The potential for misuse, such as generating deceptive synthetic speech, highlights the need for responsible deployment and monitoring of such technologies. The main contribution of this paper is the introduction of SiTok, a novel speech tokenizer that utilizes a diffusion autoencoder to achieve high-quality speech representation and reconstruction while maintaining low bit and token rates. This work significantly advances the field of speech processing by addressing key challenges in existing methodologies and providing a robust framework for future research and applications.
Author of this paper? Send a correction.