Improve model card: Add pipeline tag, library name, project page link, and sample usage

README.md

@@ -1,19 +1,22 @@
 ---
+datasets:
+- custom
 language:
 - en
-tags:
-- audio-text-to-audio-text
-- speech-understanding
-- audio
-- chat
 license: apache-2.0
-datasets:
-- custom
 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
@@ -21,25 +24,50 @@ metrics:
 </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://huggingface.co/spaces/FreedomIntelligence/EchoX">🚀 Space</a>&nbsp</font>
+  <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 10k hours of data while delivering state-of-the-art results in knowledge-based question answering and speech interaction tasks.
+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 10k Hours of Curated Data, Ensuring Efficiency</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>
 
-## Usage
-Load the EchoX model and run inference with your audio files as shown in the <a href="https://github.com/FreedomIntelligence/EchoX">GitHub repository</a>.
+## 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>
 ```