Create app.py

app.py

@@ -0,0 +1,146 @@
+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"]
+
+    print(f"Sample rate: {sample_rate}, Sample size: {sample_size}")
+
+    model = model.to(device)
+    print("Model moved to device.")
+
+    # Set up text and timing conditioning
+    conditioning = [{
+        "prompt": prompt,
+        "seconds_start": 0,
+        "seconds_total": seconds_total
+    }]
+    print(f"Conditioning: {conditioning}")
+
+    # Generate stereo audio
+    print("Generating audio...")
+    output = generate_diffusion_cond(
+        model,
+        steps=steps,
+        cfg_scale=cfg_scale,
+        conditioning=conditioning,
+        sample_size=sample_size,
+        sigma_min=0.3,
+        sigma_max=500,
+        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
+    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()