Update app.py

app.py

@@ -1,3 +1,5 @@
+
+"""
 import streamlit as st
 from transformers import pipeline
 
@@ -6,4 +8,44 @@ text = st.text_area("some text")
 
 if text:
     out = pipe(text)
-    st.json(out)
+    st.json(out)
+
+"""
+
+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
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# 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": "128 BPM tech house drum loop",
+}]
+
+# 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)
+
+
+