app.py

app.py

@@ -1,59 +1,32 @@
+import gradio as gr
 import torch
 import torchaudio
 from einops import rearrange
-import gradio as gr
-import spaces
-import os
-import uuid
-
-# Importing the model-related functions
 from stable_audio_tools import get_pretrained_model
 from stable_audio_tools.inference.generation import generate_diffusion_cond
 
-# Load the model outside of the GPU-decorated function
-def load_model():
-    print("Loading model...")
-    model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
-    print("Model loaded successfully.")
-    return model, model_config
-
-# Function to set up, generate, and process the audio
-@spaces.GPU(duration=120)  # Allocate GPU only when this function is called
-def generate_audio(prompt, seconds_total=30, steps=100, cfg_scale=7):
-    print(f"Prompt received: {prompt}")
-    print(f"Settings: Duration={seconds_total}s, Steps={steps}, CFG Scale={cfg_scale}")
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print(f"Using device: {device}")
-
-    # Fetch the Hugging Face token from the environment variable
-    hf_token = os.getenv('HF_TOKEN')
-    print(f"Hugging Face token: {hf_token}")
-
-    # Use pre-loaded model and configuration
-    model, model_config = load_model()
-    sample_rate = model_config["sample_rate"]
-    sample_size = model_config["sample_size"]
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-    print(f"Sample rate: {sample_rate}, Sample size: {sample_size}")
+# 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)
-    print("Model moved to device.")
+model = model.to(device)
 
+def generate_audio(prompt, bpm, duration):
     # Set up text and timing conditioning
     conditioning = [{
-        "prompt": prompt,
-        "seconds_start": 0,
-        "seconds_total": seconds_total
+        "prompt": f"{bpm} BPM {prompt}",
+        "seconds_start": 0, 
+        "seconds_total": duration
     }]
-    print(f"Conditioning: {conditioning}")
 
     # Generate stereo audio
-    print("Generating audio...")
     output = generate_diffusion_cond(
         model,
-        steps=steps,
-        cfg_scale=cfg_scale,
+        steps=100,
+        cfg_scale=7,
         conditioning=conditioning,
         sample_size=sample_size,
         sigma_min=0.3,
@@ -61,86 +34,27 @@ def generate_audio(prompt, seconds_total=30, steps=100, cfg_scale=7):
         sampler_type="dpmpp-3m-sde",
         device=device
     )
-    print("Audio generated.")
 
     # Rearrange audio batch to a single sequence
     output = rearrange(output, "b d n -> d (b n)")
-    print("Audio rearranged.")
 
-    # Peak normalize, clip, convert to int16
+    # 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()
-    print("Audio normalized and converted.")
-
-    # Generate a unique filename for the output
-    unique_filename = f"output_{uuid.uuid4().hex}.wav"
-    print(f"Saving audio to file: {unique_filename}")
-
-    # Save to file
-    torchaudio.save(unique_filename, output, sample_rate)
-    print(f"Audio saved: {unique_filename}")
-
-    # Return the path to the generated audio file
-    return unique_filename
-
-# Setting up the Gradio Interface
-interface = gr.Interface(
-    fn=generate_audio,
-    inputs=[
-        gr.Textbox(label="Prompt", placeholder="Enter your text prompt here"),
-        gr.Slider(0, 47, value=30, label="Duration in Seconds"),
-        gr.Slider(10, 150, value=100, step=10, label="Number of Diffusion Steps"),
-        gr.Slider(1, 15, value=7, step=0.1, label="CFG Scale")
-    ],
-    outputs=gr.Audio(type="filepath", label="Generated Audio"),
-    title="Stable Audio Generator",
-    description="Generate variable-length stereo audio at 44.1kHz from text prompts using Stable Audio Open 1.0.",
-    examples=[
-    [
-        "Create a serene soundscape of a quiet beach at sunset.",  # Text prompt
- 
-        45,  # Duration in Seconds
-        100,  # Number of Diffusion Steps
-        10,  # CFG Scale
-    ],
-    [
-        "Generate an energetic and bustling city street scene with distant traffic and close conversations.",  # Text prompt
-        
-        30,  # Duration in Seconds
-        120,  # Number of Diffusion Steps
-        5,  # CFG Scale
-    ],
-    [
-        "Simulate a forest ambiance with birds chirping and wind rustling through the leaves.",  # Text prompt
-        60,  # Duration in Seconds
-        140,  # Number of Diffusion Steps
-        7.5,  # CFG Scale
-    ],
-    [
-        "Recreate a gentle rainfall with distant thunder.",  # Text prompt
-       
-        35,  # Duration in Seconds
-        110,  # Number of Diffusion Steps
-        8,  # CFG Scale
-        
-    ],
-    [
-        "Imagine a jazz cafe environment with soft music and ambient chatter.",  # Text prompt
-        25,  # Duration in Seconds
-        90,  # Number of Diffusion Steps
-        6,  # CFG Scale
-  
-    ],
-    ["Rock beat played in a treated studio, session drumming on an acoustic kit.",
-        30,  # Duration in Seconds
-        100,  # Number of Diffusion Steps
-        7,  # CFG Scale
-     
-    ]
-])
-
-
-# Pre-load the model to avoid multiprocessing issues
-model, model_config = load_model()
-
-# Launch the Interface
-interface.launch()
+    
+    return sample_rate, output
+
+inputs = [
+    gr.inputs.Textbox(label="Prompt"),
+    gr.inputs.Number(label="BPM", default=128),
+    gr.inputs.Number(label="Duration (seconds)", default=30)
+]
+
+output = gr.outputs.Audio(type="numpy", label="Generated Audio")
+
+gr.Interface(
+    fn=generate_audio, 
+    inputs=inputs,
+    outputs=output,
+    title="Stable Audio Generation",
+    description="Generate audio using Stable Audio Open 1.0"
+).launch()