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README.md

@@ -1,10 +1,10 @@
 ---
-title: Audio
-emoji: 🐢
-colorFrom: blue
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.35.0
+title: Stable Audio Open Zero
+emoji: 🔥
+colorFrom: indigo
+colorTo: pink
+sdk: gradio
+sdk_version: 4.33.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -1,42 +1,110 @@
+import random
 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
 
-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",
-    "seconds_start": 0, 
-    "seconds_total": 30
-}]
-
-# Generate stereo audio
-output = generate_diffusion_cond(
-    model,
-    steps=100,
-    cfg_scale=7,
-    conditioning=conditioning,
-    sample_size=sample_size,
-    sigma_min=0.3,
-    sigma_max=500,
-    sampler_type="dpmpp-3m-sde",
-    device=device
+# Load the model outside of the GPU-decorated function
+def load_model():
+    print("Loading model...")
+    model, model_config = get_pretrained_model("chaowenguo/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}")
+
+    seed = random.randint(0, 2**63 - 1)
+    random.seed(seed)
+    torch.manual_seed(seed)
+    print(f"Using seed: {seed}")
+
+    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=5, label="Duration in Seconds"),
+        gr.Slider(10, 150, value=10, 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."
 )
 
-# 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)
+# Pre-load the model to avoid multiprocessing issues
+model, model_config = load_model()
 
+# Launch the Interface
+interface.launch()

requirements.txt

@@ -0,0 +1,3 @@
+torch
+torchaudio
+stable-audio-tools