Update app.py

app.py

@@ -102,7 +102,14 @@ def gradio_generate(prompt, steps, guidance):
 # Using this ChatGPT-generated description of the sound, TANGO provides superior results.
 # <p/>
 # """
-description_text = ""
+description_text = """
+<p><a href="https://huggingface.co/spaces/declare-lab/tango2/blob/main/app.py?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings. <br/><br/>
+Generate audio using Tango2 by providing a text prompt. Tango2 was built from Tango and was trained on <a href="https://huggingface.co/datasets/declare-lab/audio-alpaca">Audio-alpaca</a>
+<br/><br/> This is the demo for Tango2 for text to audio generation: <a href="https://arxiv.org/abs/2404.09956">Read our paper.</a>
+<br>
+This version is trained on an extended version of Audio-alpaca and results are better.
+<p/>
+"""
 # Gradio input and output components
 input_text = gr.Textbox(lines=2, label="Prompt")
 output_audio = gr.Audio(label="Generated Audio", type="filepath")
@@ -114,7 +121,7 @@ gr_interface = gr.Interface(
     fn=gradio_generate,
     inputs=[input_text, denoising_steps, guidance_scale],
     outputs=[output_audio],
-    title="TANGO2: Aligning Diffusion-based Text-to-Audio Generative Models through Direct Preference Optimization",
+    title="Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization",
     description=description_text,
     allow_flagging=False,
     examples=[