Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model (2408.17175v3)

Abstract: Recent advancements in audio generation have been significantly propelled by the capabilities of LLMs. The existing research on audio LLM has primarily focused on enhancing the architecture and scale of audio LLMs, as well as leveraging larger datasets, and generally, acoustic codecs, such as EnCodec, are used for audio tokenization. However, these codecs were originally designed for audio compression, which may lead to suboptimal performance in the context of audio LLM. Our research aims to address the shortcomings of current audio LLM codecs, particularly their challenges in maintaining semantic integrity in generated audio. For instance, existing methods like VALL-E, which condition acoustic token generation on text transcriptions, often suffer from content inaccuracies and elevated word error rates (WER) due to semantic misinterpretations of acoustic tokens, resulting in word skipping and errors. To overcome these issues, we propose a straightforward yet effective approach called X-Codec. X-Codec incorporates semantic features from a pre-trained semantic encoder before the Residual Vector Quantization (RVQ) stage and introduces a semantic reconstruction loss after RVQ. By enhancing the semantic ability of the codec, X-Codec significantly reduces WER in speech synthesis tasks and extends these benefits to non-speech applications, including music and sound generation. Our experiments in text-to-speech, music continuation, and text-to-sound tasks demonstrate that integrating semantic information substantially improves the overall performance of LLMs in audio generation. Our code and demo are available (Demo: https://x-codec-audio.github.io Code: https://github.com/zhenye234/xcodec)

• The paper identifies that conventional audio codecs lack semantic fidelity, leading to misinterpretations in tasks like speech synthesis.
• It introduces X-Codec, which integrates semantic features with acoustic representations using semantic reconstruction loss.
• Experimental results demonstrate significant reductions in word error rates and improved performance metrics in diverse audio generation tasks.

An Exploration of Audio Codec Optimization for Audio LLMs

The research paper titled "Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio LLM" provides a detailed investigation into the limitations of current audio codecs when applied to audio LLMs. Historically, audio codecs have been developed with a primary focus on compression and reconstruction efficiency, as demonstrated by widely used technologies like EnCodec. However, these codecs are not inherently optimized for the semantic intricacies required by audio LLMs.

Overview and Motivation

The primary motivation of this research stems from the observation that current audio LLMs suffer from significant limitations in semantic tokenization, leading to content inaccuracies during audio generation tasks, such as speech synthesis. For instance, methods like VALL-E, which condition audio generation on text transcriptions, exhibit high word error rates due to semantic misinterpretations caused by these inadequacies in acoustic token representations.

Proposed Solution: X-Codec

To address these challenges, the authors propose a novel approach named X-Codec. This codec is designed to incorporate semantic features alongside traditional acoustic representations in the audio generation pipeline. Building upon a pre-trained semantic encoder, X-Codec integrates these semantic features before the Residual Vector Quantization (RVQ) stage and implements a semantic reconstruction loss thereafter. This dual-layer architecture aims to retain semantic integrity and substantially enhance the performance of audio LLMs across varied applications.

Experimental Evaluation

The researchers conducted extensive experiments on tasks including text-to-speech (TTS), music continuation, and text-to-sound synthesis. The results indicate a significant reduction in word error rates (WER) for speech synthesis tasks, validating the codec's improved semantic encoding abilities. The paper also extends its findings to non-speech audio applications—music and sound generation—where integrating semantic information was shown to improve performance metrics such as Frechet Distance (FD) and Frechet Audio Distance (FAD) compared to solely acoustically oriented codecs.

Implications and Future Directions

The findings from this paper have both practical and theoretical implications. From a practical standpoint, the development of a codec that can effectively support the semantic demands of audio generation opens opportunities for more accurate and contextually relevant audio LLMs, enhancing applications in areas from voice synthesis to music creation. Theoretically, this research lays the groundwork for future explorations into the integration of semantic understanding within traditionally compression-focused domains. This denotes a possible shift in how codecs are designed and optimized, emphasizing semantic comprehension alongside acoustic fidelity.

In future work, further exploration into the adaptability of this method across diverse languages and audio forms will be critical to understanding the universality and scalability of such approaches. Moreover, integrating such semantic-aware codecs into real-time applications could significantly influence the naturalness and accuracy of interactive audio systems.

Conclusion

The research convincingly argues that optimizing audio codecs for semantic fidelity can lead to substantial improvements in audio LLMs, particularly in tasks requiring nuanced understanding and generation of spoken content. The introduction of the X-Codec is a promising development, potentially steering the discourse on audio compression technologies towards a more semantically harmonious approach with significant implications for AI-driven audio applications.