Upload app.py

app.py

@@ -2,23 +2,30 @@ import gradio as gr
 import torchaudio
 from audiocraft.models import AudioGen
 from audiocraft.data.audio import audio_write
+
 model = AudioGen.get_pretrained('facebook/audiogen-medium')
-def infer(prompt, duration):    
+
+def infer(prompt, duration):
+    
     model.set_generation_params(duration=duration)  # generate 5 seconds.
     descriptions = [prompt]
     wav = model.generate(descriptions)  # generates n samples (referring to the number n of prompts in descriptions)
-    for idx, one_wav in enumerate(wav):        
-# Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
+
+    for idx, one_wav in enumerate(wav):
+        # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
         audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
+
     return "0.wav"
+
 css="""
 #col-container{
     margin: 0 auto;
     max-width: 640px;
 }
 """
-with gr.Blocks(css=css) as demo:    
-with gr.Column(elem_id="col-container"):
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
         gr.HTML("""
         <h2 style="text-align: center;">
             AudioGen: Textually-guided audio generation
@@ -26,14 +33,17 @@ with gr.Column(elem_id="col-container"):
         <p style="text-align: center;">   
         </p>
         """)
-        prompt_in = gr.Textbox(label="audio prompt")        
-with gr.Row():
+
+        prompt_in = gr.Textbox(label="audio prompt")
+        with gr.Row():
             duration = gr.Slider(label="Duration", minimum=5, maximum=120, step=5, value=5)
             submit_btn = gr.Button("Submit")
         audio_o = gr.Audio(label="AudioGen result")
+
     submit_btn.click(
         fn=infer,
         inputs=[prompt_in, duration],
         outputs=[audio_o]
     )
+
 demo.queue().launch(debug=True)