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OpusLM: A Family of Open Unified Speech Language Models

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arxiv 2506.17611 v1 pith:S76DLTVV submitted 2025-06-21 cs.CL cs.SDeess.AS

Jinchuan Tian , William Chen , Yifan Peng , Jiatong Shi , Siddhant Arora , Shikhar Bharadwaj , Takashi Maekaku , Yusuke Shinohara
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Keita Goto Xiang Yue Huck Yang Shinji Watanabe
This is my paper
classification cs.CLcs.SDeess.AS
keywords modelslanguagespeechopenopuslmsdatademonstratefamily
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This paper presents Open Unified Speech Language Models (OpusLMs), a family of open foundational speech language models (SpeechLMs) up to 7B. Initialized from decoder-only text language models, 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 language models, 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

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Bagpiper-TTS: Natural Language Guided Universal Speech Synthesis

    cs.CL 2026-06 unverdicted novelty 7.0 of 10

    Bagpiper-TTS uses natural language prompts and intent reasoning to derive rich captions that guide a single model for universal speech synthesis across classical TTS, multi-talker, singing, and role-play tasks.

  2. An Efficient vLLM-Based Inference Pipeline for Unified Audio Understanding and Generation

    eess.AS 2026-07 unverdicted novelty 6.0 of 10

    Extends vLLM with delay-pattern de-interleaving, multi-stream sampling, and co-scheduled CFG to achieve 80% of non-CFG throughput for unified audio tasks while open-sourcing the pipeline.

  3. UniVoice: Unifying Autoregressive ASR and Flow-Matching based TTS with Large Language Models

    eess.AS 2025-10 conditional novelty 6.0 of 10

    A single LLM can do ASR and zero-shot TTS on continuous speech features by switching between causal and bidirectional attention, reaching competitive but not state-of-the-art results.

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