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Towards audio language modeling -- an overview (2402.13236v1)

Published 20 Feb 2024 in eess.AS and cs.SD
Towards audio language modeling -- an overview

Abstract: Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as suitable tokenizers for converting continuous audio into discrete codes, which can be employed to develop audio LLMs (LMs). Numerous high-performance neural audio codecs and codec-based LMs have been developed. The paper aims to provide a thorough and systematic overview of the neural audio codec models and codec-based LMs.

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Comprehensive Overview on Neural Audio Codec Models and Codec-Based LLMs

Introduction to Neural Audio Codec Models

The landscape of neural audio codecs has advanced considerably, initially emerging to compress audio data for efficient transmission. These codecs, through sophisticated encoding and decoding mechanisms, significantly reduce data size while aiming to retain the original quality of the audio. The inception of neural audio codecs provided a basis for further innovation, particularly in their application as tokenizers. This crucial development allowed for the transformation of continuous audio signals into discrete codes, paving the way for the application of these codecs in the development of audio LLMs (LMs). An audio LM aims to understand and generate audio content, taking into account not only the textual or linguistic content but also the speaker's identity, emotions, and other paralinguistic features embedded in the audio signal.

Analysis of Neural Codec Models

The paper explores an extensive comparison of six leading open-source neural codec models, focusing on their training methodologies, settings, and the data used for training. Key insights from the comparison include:

  • Methodological Overview: Models like SoundStream and Encodec highlight the evolution of neural codecs, integrating components such as quantizers and encoder-decoder architectures tailored for audio processing. Techniques like Residual Vector Quantization (RVQ) have been instrumental in these developments.
  • Innovative Training Techniques: Beyond traditional training approaches, models have employed advanced mechanisms such as adversarial and reconstruction loss optimization, showcasing the dynamic adaptations within the field to improve audio quality and efficiency.
  • Design and Discriminator Use: The paper further compares discriminators across models, noting the varied approaches and their impacts on audio quality. For instance, the integration of Multi-scale-STFT Discriminator (MS-STFTD) and the innovative application of multi-band STFT discriminators have been pivotal in refining audio output.
  • Semantic Integration and Activation Functions: Another intriguing aspect is the embedding of semantic information and the use of unique activation functions, which both serve to enhance the fidelity and applicability of codec models across diverse audio types.

Codec-Based LLMs (CLMs)

The paper provides a systematic overview of the evolving sphere of codec-based LMs, spotlighting their methodologies, input-output handling, and the diverse array of tasks they are designed to address. Noteworthy models include:

  • AudioLM and VALL-E: Pioneers in demonstrating the potential of codec codes for LLMing, leveraging hierarchical processes to intertwine semantic and acoustic tokens for generating high-quality audio outputs.
  • ViaLA, AudioPaLM, and LauraGPT: Models that signify the confluence of audio and textual processing, capable of handling dual modality inputs and outputs, and setting the stage for tasks like speech recognition, synthesis, translation, and even speech enhancement.

Insights and Future Directions

This comprehensive review sheds light on the rapid advancements and the nuanced differences between various neural codec models and codec-based LMs. The insightful analysis elucidates the strengths and potential areas of improvement for each model, providing a valuable resource for researchers aiming to navigate or contribute to the field. The implications of this research span both theoretical and practical realms, suggesting a promising trajectory for future developments in AI, particularly in enhancing the generative capabilities and efficiency of neural audio codecs and LLMs. The anticipated advancements could revolutionize audio processing applications, including more nuanced speech synthesis, improved speech-to-text translation, and innovative audio content generation.

In conclusion, the exploration of neural audio codecs and codec-based LLMs illuminates a path towards more intricate and efficient audio processing capabilities. As the field continues to evolve, the insights from this paper underscore the importance of continued research and collaboration, fostering an enriching environment for innovation in AI-driven audio applications.

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Authors (7)
  1. Haibin Wu (84 papers)
  2. Xuanjun Chen (17 papers)
  3. Yi-Cheng Lin (23 papers)
  4. Ho-Lam Chung (13 papers)
  5. Alexander H. Liu (32 papers)
  6. Hung-yi Lee (325 papers)
  7. Kai-Wei Chang (292 papers)
Citations (16)
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