Bagpiper: Solving Open-Ended Audio Tasks via Rich Captions
Abstract
Current audio foundation models typically rely on rigid, task-specific supervision, addressing isolated factors of audio rather than the whole. In contrast, human intelligence processes audio holistically, seamlessly bridging physical signals with abstract cognitive concepts to execute complex tasks. Grounded in this philosophy, we introduce Bagpiper, an 8B audio foundation model that interprets physical audio via rich captions, i.e., comprehensive natural language descriptions that encapsulate the critical cognitive concepts inherent in the signal (e.g., transcription, audio events). By pre-training on a massive corpus of 600B tokens, the model establishes a robust bidirectional mapping between raw audio and this high-level conceptual space. During fine-tuning, Bagpiper adopts a caption-then-process workflow, simulating an intermediate cognitive reasoning step to solve diverse tasks without task-specific priors. Experimentally, Bagpiper outperforms Qwen-2.5-Omni on MMAU and AIRBench for audio understanding and surpasses CosyVoice3 and TangoFlux in generation quality, capable of synthesizing arbitrary compositions of speech, music, and sound effects. To the best of our knowledge, Bagpiper is among the first works that achieve unified understanding generation for general audio. Model, data, and code are available at Bagpiper Home Page.
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
Bagpiper represents a significant advancement in audio foundation models by utilizing rich captions to unify understanding and generation tasks. This innovative approach not only enhances performance across diverse audio tasks but also sets a new standard for holistic audio processing in machine learning.
Methodology
The methodology of Bagpiper is innovative, leveraging rich captions as a bridge between audio signals and cognitive concepts. The model employs a caption-then-process workflow, which simulates cognitive reasoning, allowing it to handle diverse audio tasks without task-specific supervision. The architecture integrates a decoder-only LLM with an acoustic encoder, establishing a robust bidirectional mapping through extensive pre-training on a large dataset of audio-text pairs. This approach is a significant departure from traditional task-specific models, promoting a more holistic understanding of audio processing.
Results & evaluation
The experiments are comprehensive, comparing Bagpiper against various state-of-the-art models in both audio understanding and generation. The results demonstrate superior performance in multiple benchmarks, including MMAU and AIR-Bench, validating the effectiveness of the rich captioning approach. The evaluation metrics are well-defined, and the use of qualitative assessments alongside quantitative results adds depth to the findings.
Reproducibility
The authors commit to releasing their model, data, and code, which is crucial for reproducibility. The paper provides detailed descriptions of the training process, data curation, and evaluation protocols, enhancing the likelihood that other researchers can replicate the results.
Limitations
While Bagpiper shows promise, it has limitations in handling highly compositional prompts and may exhibit higher inference latency due to the computational overhead of generating rich captions. Additionally, the model's performance may vary across different demographic groups, raising concerns about bias.
Broader impact
The potential applications of Bagpiper are vast, ranging from improving accessibility through detailed audio descriptions to enhancing creative workflows in sound production. However, the risks associated with misuse for deception or disinformation, as well as environmental concerns related to large-scale training, must be carefully managed. Bagpiper represents a significant advancement in audio foundation models by utilizing rich captions to unify understanding and generation tasks. This innovative approach not only enhances performance across diverse audio tasks but also sets a new standard for holistic audio processing in machine learning.
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