5 papers
MOSS Transcribe Diarize Technical Report
MOSI. AI, :, Donghua Yu +23
Speaker-Attributed, Time-Stamped Transcription (SATS) aims to transcribe what is said and to precisely determine the timing of each speaker, which is particularly valuable for meet…
MOSS-Audio Technical Report
Chen Yang, Chufan Yu, Hanfu Chen +27
MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped trans…
Rethinking Retrieval-Augmentation as Synthesis: A Query-Aware Context Merging Approach
Jiarui Guo, Yuemeng Xu, Zongwei Lv +6
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to extend their existing knowledge by dynamically incorporating external information. However, practical d…
MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models
Yitian Gong, Kuangwei Chen, Zhaoye Fei +9
Discrete audio tokenizers are fundamental to empowering large language models with native audio processing and generation capabilities. Despite recent progress, existing approaches…
MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance
Xingjian Zhao, Zhe Xu, Qinyuan Cheng +20
Spoken dialogue systems often rely on cascaded pipelines that transcribe, process, and resynthesize speech. While effective, this design discards paralinguistic cues and limits exp…