README.md

metadata

language:
  - en
tags:
  - audio-text-to-audio-text
  - speech-understanding
  - audio
  - chat
license: apache-2.0
datasets:
  - custom
metrics:
  - wer
  - bleu
  - AIR-Bench

EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs

🐈‍⬛ Github ｜  📃 Paper ｜  🚀 Space (8B) ｜  📊 EchoX-Dialougues ｜  📊 EchoX-Dialogues-Plus

Model Description

EchoX is a Speech-to-Speech large language model that addresses the acoustic-semantic gap. This is the 3B version. By introducing Echo Training, EchoX integrates semantic and acoustic learning, mitigating the degradation of reasoning ability observed in existing speech-based LLMs. It is trained on only 10k hours of data while delivering state-of-the-art results in knowledge-based question answering and speech interaction tasks.

Key Features

• Mitigates Acoustic-Semantic Gap in Speech-to-Speech LLMs
• Introduces Echo Training with a Novel Three-Stage Pipeline (S2T, T2C, Echo)
• Trained on Only 10k Hours of Curated Data, Ensuring Efficiency
• Achieves State-of-the-Art Performance in Knowledge-Based QA Benchmarks
• Preserves Reasoning and Knowledge Abilities for Interactive Speech Tasks

Usage

Load the EchoX model and run inference with your audio files as shown in the GitHub repository.

📖 Citation

@misc{zhang2025echoxmitigatingacousticsemanticgap,
      title={EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs}, 
      author={Yuhao Zhang and Yuhao Du and Zhanchen Dai and Xiangnan Ma and Kaiqi Kou and Benyou Wang and Haizhou Li},
      year={2025},
      eprint={2509.09174},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.09174}, 
}