MOSS-Audio Technical Report
Abstract
MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped transcription, and audio-grounded reasoning. MOSS-Audio couples a dedicated audio encoder with a modality adapter and a large language model: the encoder produces 12.5 Hz temporal representations, the adapter projects them into the decoder space, and the decoder generates autoregressive text outputs. Two design choices are central to the system: \textbf{DeepStack cross-layer feature injection}, which exposes the decoder to acoustic information from multiple encoder depths, and \textbf{time markers}, which provide explicit temporal cues by inserting timestamp markers into the audio-token stream. At the data level, we design an event-preserving audio annotation pipeline that segments raw audio at coherent event boundaries, applies branch-specific annotation to speech, music, and general audio, and merges the results into unified captions for pretraining. The intermediate branch-specific captions are further retained to support the construction of task-oriented SFT data. The model is pretrained on large-scale audio-language data, with time-aware objectives incorporated to support temporal grounding, and then undergoes multi-stage post-training to enhance instruction following and audio-grounded reasoning. We release 4B and 8B variants in both Instruct and Thinking configurations. MOSS-Audio achieves strong performance across general audio understanding, speech captioning, ASR, and timestamped ASR, positioning it as a promising understanding foundation for future voice agents.
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
MOSS-Audio presents a unified audio-language model that achieves state-of-the-art performance across various audio understanding tasks, leveraging innovative architectural choices and a comprehensive training methodology. The technical contributions, particularly in temporal grounding and feature injection, position it as a significant advancement in the field of audio processing and multimodal AI systems.
Methodology
The methodology presented in MOSS-Audio is robust and innovative, employing a modular architecture that integrates a dedicated audio encoder, modality adapter, and a large language model. The introduction of DeepStack cross-layer feature injection is particularly notable, as it enhances the model's ability to capture multi-level acoustic features, which is crucial for understanding complex audio inputs. Additionally, the incorporation of explicit time markers for temporal grounding is a significant advancement, allowing the model to handle timestamped transcription and time-aware question answering more effectively. The event-preserving audio annotation pipeline is also a strong methodological contribution, ensuring that the model is trained on coherent audio segments rather than arbitrary cuts, which is essential for real-world audio understanding tasks.
Results & evaluation
The empirical results demonstrate that MOSS-Audio achieves state-of-the-art performance across a variety of benchmarks, including general audio understanding, speech captioning, ASR, and timestamped ASR. The model's performance is particularly impressive given its relatively compact size compared to other state-of-the-art models, indicating efficient scaling. The evaluation methodology is thorough, utilizing a range of benchmarks and metrics that provide a comprehensive assessment of the model's capabilities. However, the paper could benefit from more detailed comparisons against a wider array of existing models to further contextualize its performance.
Reproducibility
The paper provides a detailed description of the architecture, training pipeline, and evaluation metrics, which supports reproducibility. However, the absence of publicly available code or model weights limits the practical reproducibility of the results. Future work should consider releasing the model and training code to facilitate independent validation and experimentation by the research community.
Limitations
While MOSS-Audio shows strong performance, the paper does not address potential limitations related to the model's generalization to unseen audio types or its performance in noisy environments. Additionally, the reliance on large-scale annotated datasets may raise concerns about the model's applicability in low-resource settings. The paper could also explore the computational costs associated with training and deploying such models, which can be significant.
Broader impact
MOSS-Audio has the potential to significantly impact various applications, including voice assistants, automated transcription services, and audio analysis tools in diverse fields such as healthcare, entertainment, and security. By providing a unified framework for audio understanding, the model can enhance user interactions with technology and improve accessibility for individuals with hearing impairments. The advancements in temporal reasoning and audio-grounded reasoning could also lead to more sophisticated AI systems capable of understanding and responding to complex audio cues in real-time. MOSS-Audio presents a unified audio-language model that achieves state-of-the-art performance across various audio understanding tasks, leveraging innovative architectural choices and a comprehensive training methodology. The technical contributions, particularly in temporal grounding and feature injection, position it as a significant advancement in the field of audio processing and multimodal AI systems.
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