Researchers at the Institute of Computing Technology, Chinese Academy of Sciences, have introduced LLaMA-Omni2, a family of speech-capable large language models (SpeechLMs) now available on Hugging Face. This research introduces a modular framework that enables real-time spoken dialogue by integrating speech perception and synthesis with language understanding. Unlike earlier cascaded systems, LLaMA-Omni2 operates in an end-to-end pipeline while retaining modular interpretability and low training cost.
Overview of the LLaMA-Omni2 Architecture
LLaMA-Omni2 encompasses models ranging from 0.5B to 14B parameters, each built atop the Qwen2.5-Instruct series. The architecture consists of:
- Speech Encoder: Utilizes Whisper-large-v3 to transform input speech into token-level acoustic representations.
- Speech Adapter: Processes encoder outputs using a downsampling layer and a feed-forward network to align with the language model’s input space.
- Core LLM: The Qwen2.5 models serve as the main reasoning engine.
- Streaming TTS Decoder: Converts LLM outputs into speech tokens using an autoregressive Transformer and then generates mel spectrograms through a causal flow matching model inspired by CosyVoice2.
A gating mechanism fuses LLM hidden states with textual embeddings before speech synthesis, enhancing contextual fidelity in the generated audio.
Streaming Generation with Read-Write Scheduling
The model adopts a read-write strategy to facilitate streaming output. Specifically, for every R tokens produced by the LLM, W speech tokens are generated. This enables synchronized textual and acoustic generation, minimizing latency without compromising fluency.
Empirical findings suggest that setting R = 3 and W = 10 provides a favorable trade-off between latency (~583 ms), alignment (ASR-WER: 3.26), and perceptual quality (UTMOS: 4.19).
Training Approach
Despite achieving competitive performance, LLaMA-Omni2 is trained on a relatively compact corpus—200K multi-turn speech-to-speech dialogue samples. These samples are synthesized from instruction-following text datasets (Alpaca, UltraChat), with diverse input voices and a consistent output voice generated using FishSpeech and CosyVoice2 models.
Training is executed in two stages:
- Stage I: Independently optimizes the speech-to-text and text-to-speech modules.
- Stage II: Fine-tunes the speech-to-speech generation path, including the gating and autoregressive decoding components.
Benchmark Results
The models are evaluated on spoken question answering and speech instruction following tasks using both speech-to-text (S2T) and speech-to-speech (S2S) modes.
| Model | Llama Q (S2S) | Web Q (S2S) | GPT-4o Score | ASR-WER | Latency (ms) |
|---|---|---|---|---|---|
| GLM-4-Voice (9B) | 50.7 | 15.9 | 4.09 | 3.48 | 1562.8 |
| LLaMA-Omni (8B) | 49.0 | 23.7 | 3.52 | 3.67 | 346.7 |
| LLaMA-Omni2-7B | 60.7 | 31.3 | 4.15 | 3.26 | 582.9 |
The performance scales consistently with model size. Notably, LLaMA-Omni2-14B outperforms all baselines across tasks, even with substantially less training data than native SpeechLMs such as GLM-4-Voice.
Component Analyses
- Gate Fusion Module: Removing the gating mechanism increases ASR-WER and reduces speech quality, confirming its role in aligning textual and contextual signals.
- TTS Pretraining: Initializing the TTS model from Qwen2.5 and fine-tuning in a streaming setup yields the best performance. Training from scratch fails to converge effectively.
- Read/Write Strategies: Adjusting the R:W ratio impacts latency and quality. Larger W improves UTMOS but at the cost of response delay.
Additionally, the study demonstrates that multi-turn dialogue data is more effective than single-turn data in training speech interaction capabilities, and that performance plateaus around 200K samples.
Conclusion
LLaMA-Omni2 demonstrates that high-quality, low-latency spoken interaction with LLMs is feasible without the need for extensive pretraining on massive speech corpora. By combining modular architecture with autoregressive streaming synthesis, the system offers a practical pathway for real-time speech applications.
Check out the Paper, Model on Hugging Face and GitHub Page. Also, don’t forget to follow us on Twitter.
Here’s a brief overview of what we’re building at Marktechpost:
ML News Community – r/machinelearningnews (92k+ members)
Newsletter– airesearchinsights.com/(30k+ subscribers)
miniCON AI Events – minicon.marktechpost.com
AI Reports & Magazines – magazine.marktechpost.com
AI Dev & Research News – marktechpost.com (1M+ monthly readers)
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
