Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+import torch
+import torchaudio
+from einops import rearrange
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.inference.generation import generate_diffusion_cond
+
+def gen_music(description):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    st.title("Generate Music with Stability Audio!!")
+    
+    # Download model
+    model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
+    sample_rate = model_config["sample_rate"]
+    sample_size = model_config["sample_size"]
+    
+    model = model.to(device)
+    
+    # Set up text and timing conditioning
+    conditioning = [{
+    	"prompt": f"{description.placeholder}",
+    }]
+    
+    # Generate stereo audio
+    output = generate_diffusion_cond(
+    	model,
+    	conditioning=conditioning,
+    	sample_size=sample_size,
+    	device=device
+    )
+    
+    # Rearrange audio batch to a single sequence
+    output = rearrange(output, "b d n -> d (b n)")
+    
+    # Peak normalize, clip, convert to int16, and save to file
+    output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    torchaudio.save("output.wav", output, sample_rate)
+
+    return "output.wav"
+
+
+# Define a interface Gradio
+description = gr.Textbox(label="Description", placeholder="128 BPM tech house drum loop")
+output_path = gr.Audio(label="Generated Music", type="filepath")
+
+gr.Interface(
+    fn=gen_music,
+    inputs=[description],
+    outputs=output_path,
+    title="StableAudio Music Generation Demo",
+).launch()