OpusLM: A Family of Open Unified Speech Language Models (2506.17611v1)

Abstract: This paper presents Open Unified Speech LLMs (OpusLMs), a family of open foundational speech LLMs (SpeechLMs) up to 7B. Initialized from decoder-only text LLMs, the OpusLMs are continuously pre-trained on 213K hours of speech-text pairs and 292B text-only tokens. We demonstrate our OpusLMs achieve comparable (or even superior) performance with existing SpeechLMs in speech recognition, speech synthesis, and text-only capabilities. Technically, this paper articulates our SpeechLM designs on tokenization, multi-stream LLMs, and multi-stage training strategies. We experimentally demonstrate the importance of model size scaling and the effect of annealing data selection. The OpusLMs are all built from publicly available materials and are fully transparent models. We release our code, data, checkpoints, and training logs to facilitate open SpeechLM research