MOSS-Audio-4B-Instruct

모델 소개

MOSS-Audio is an open-source **audio understanding model** from MOSI.AI, the OpenMOSS team, and Shanghai Innovation Institute. It performs unified modeling over complex real-world audio, supporting **speech understanding, environmental sound understanding, music understanding, audio captioning, time-aware QA, and complex reasoning**. In this release, we provide **four models**: **MOSS-Audio-4B-Instruct**, **MOSS-Audio-4B-Thinking**, **MOSS-Audio-8B-Instruct**, and **MOSS-Audio-8B-Thinking**. The Instruct variants are optimized for direct instruction following, while the Thinking variants provide stronger chain-of-thought reasoning capabilities. Understanding audio requires more than simply transcribing words — it demands the ability to perceive acoustic cues, recognize speakers and emotions, interpret environmental sounds, reason over temporal context, and handle complex multi-step inference. **MOSS-Audio** is built to unify these capabilities within a single model. MOSS-Audio follows a modular design comprising three components: an audio encoder, a modality adapter, and a large language model. Raw audio is first encoded by **MOSS-Audio-Encoder** into continuous temporal representations at **12.5 Hz**, which are then projected into the language model's embedding space through the adapter and finally consumed by the LLM for auto-regressive text generation.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。