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

<div align="center">
<h1>
  EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs
</h1>
</div>

<p align="center">
  <font size="3"><a href="https://github.com/FreedomIntelligence/EchoX">🐈‍⬛ Github</a>&nbsp｜&nbsp<a href="https://arxiv.org/abs/2509.09174">📃 Paper</a>&nbsp｜&nbsp<a href="https://freedomintelligence.github.io/EchoX">🌐 Project Page</a>&nbsp｜&nbsp<a href="https://huggingface.co/spaces/FreedomIntelligence/EchoX">🚀 Space</a>&nbsp</font>
</p>

## Model Description
EchoX is a Speech-to-Speech large language model that addresses the acoustic-semantic gap. 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 6k hours of data while delivering state-of-the-art results in knowledge-based question answering and speech interaction tasks.

### Key Features
<div>
  <ul>
    <font size="3"><li>Mitigates Acoustic-Semantic Gap in Speech-to-Speech LLMs</li></font>
    <font size="3"><li>Introduces Echo Training with a Novel Three-Stage Pipeline (S2T, T2C, Echo)</li></font>
    <font size="3"><li>Trained on Only 6k Hours of Curated Data, Ensuring Efficiency</li></font>
    <font size="3"><li>Achieves State-of-the-Art Performance in Knowledge-Based QA Benchmarks</li></font>
    <font size="3"><li>Preserves Reasoning and Knowledge Abilities for Interactive Speech Tasks</li></font>
  </ul>
</div>

## Sample Usage
To set up your environment and run inference, follow these steps from the [GitHub repository](https://github.com/FreedomIntelligence/EchoX):

First, clone the repository, set up the environment, and install dependencies:
```bash
git clone https://github.com/FreedomIntelligence/EchoX.git
cd EchoX
conda create -n echox python=3.10 pip=24.0
conda activate echox
pip install -r requirements.txt
```

Next, download the models:
```bash
pip install -U huggingface_hub
hf download --resume-download FreedomIntelligence/EchoX-8B --local-dir EchoX-8B
hf download --resume-download openai/whisper-large-v3 --local-dir whisper-large-v3
```

Finally, run inference on a test case, or start the Gradio web interface:
```bash
python demo.py
# Alternatively, start the Gradio web interface:
# python app.py
# To use a specific GPU:
# CUDA_VISIBLE_DEVICES=1 python app.py
```

# <span>📖 Citation</span>
```
@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}, 
}
```