QuarkAudio Technical Report
Abstract
Many existing audio processing and generation models rely on task-specific architectures, resulting in fragmented development efforts and limited extensibility. It is therefore promising to design a unified framework capable of handling multiple tasks, while providing robust instruction and audio understanding and high-quality audio generation. This requires a compatible paradigm design, a powerful backbone, and a high-fidelity audio reconstruction module. To meet these requirements, this technical report introduces QuarkAudio, a decoder-only autoregressive (AR) LM-based generative framework that unifies multiple tasks. The framework includes a unified discrete audio tokenizer, H-Codec, which incorporates self-supervised learning (SSL) representations into the tokenization and reconstruction process. We further propose several improvements to H-Codec, such as a dynamic frame-rate mechanism and extending the audio sampling rate to 48 kHz. QuarkAudio unifies tasks by using task-specific conditional information as the conditioning sequence of the decoder-only LM, and predicting discrete target audio tokens in an AR manner. The framework supports a wide range of audio processing and generation tasks, including speech restoration (SR), target speaker extraction (TSE), speech separation (SS), voice conversion (VC), and language-queried audio source separation (LASS). In addition, we extend downstream tasks to universal free-form audio editing guided by natural language instructions (including speech semantic editing and audio event editing). Experimental results show that H-Codec achieves high-quality audio reconstruction with a low frame rate, improving both the efficiency and performance of downstream audio generation, and that QuarkAudio delivers competitive or comparable performance to state-of-the-art task-specific or multi-task systems across multiple tasks.
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 paper presents QuarkAudio, a unified framework for audio processing and generation that effectively combines multiple tasks into a single architecture, showcasing innovative methodologies and promising experimental results. The comprehensive approach to audio understanding and generation positions it as a significant advancement in the field of machine learning for audio applications.
Methodology
The paper introduces QuarkAudio, a unified framework for audio processing and generation that employs a decoder-only autoregressive model. The methodology is sound, integrating a novel discrete audio tokenizer (H-Codec) that utilizes self-supervised learning for improved audio representation. The dual-stream codec design, which separates acoustic and semantic features, is innovative and addresses the limitations of existing models. The dynamic frame-rate mechanism and the extension of the audio sampling rate to 48 kHz are notable enhancements that contribute to the framework's robustness and efficiency.
Results & evaluation
The experiments are comprehensive, utilizing a diverse set of datasets for training and evaluation across multiple audio tasks. The results demonstrate competitive performance against state-of-the-art models, with detailed metrics provided for various tasks, including speech restoration and audio editing. The use of extensive datasets and the two-stage training strategy further bolster the credibility of the findings.
Reproducibility
The paper provides detailed implementation specifics, including architecture choices, training procedures, and evaluation metrics. However, the absence of a clear mention of the code's reproducibility status could hinder independent validation. The URLs provided for the demo and project repository are helpful for those interested in replicating the results.
Limitations
While the framework shows promise, the paper acknowledges challenges in semantic alignment and the potential for improved performance in speech semantic editing tasks. Additionally, the reliance on large datasets may limit accessibility for smaller research groups.
Broader impact
The QuarkAudio framework has the potential to significantly advance the field of audio processing by providing a unified model capable of handling various tasks, which could streamline development efforts and enhance the quality of audio generation applications. Its implications extend to areas such as speech synthesis, audio editing, and interactive applications, making it a valuable contribution to the field. The paper presents QuarkAudio, a unified framework for audio processing and generation that effectively combines multiple tasks into a single architecture, showcasing innovative methodologies and promising experimental results. The comprehensive approach to audio understanding and generation positions it as a significant advancement in the field of machine learning for audio applications.
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