done

app.py

@@ -1,87 +1,3 @@
-# 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
-
-
-# from huggingface_hub import login
-
-# hf_token = os.getenv('HF_TOKEN')
-# login(token=hf_token,add_to_git_credential=True)
-
-# # 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
-
-# # Define the function to generate audio
-# @spaces.GPU(duration=120)
-# def generate_audio(prompt, bpm, seconds_total):
-#     device = "cuda" if torch.cuda.is_available() else "cpu"
-
-#     # Download model
-#     model, model_config = load_model()
-#     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"{bpm} BPM {prompt}",
-#         "seconds_start": 0,
-#         "seconds_total": seconds_total
-#     }]
-
-#     # 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
-#     )
-
-#     # 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()
-    
-#     output_path = "output.wav"
-#     torchaudio.save(output_path, output, sample_rate)
-    
-#     return output_path
-
-# # Define the Gradio interface
-# iface = gr.Interface(
-#     fn=generate_audio,
-#     inputs=[
-#         gr.Textbox(label="Prompt", placeholder="Enter the description of the audio (e.g., tech house drum loop)"),
-#         gr.Number(label="BPM", value=128),
-#         gr.Number(label="Duration (seconds)", value=30)
-#     ],
-#     outputs=gr.Audio(label="Generated Audio"),
-#     title="Stable Audio Generation",
-#     description="Generate audio based on a text prompt using stable audio tools.",
-# )
-
-# # Launch the interface
-# iface.launch()
-
 import torch
 import torchaudio
 from einops import rearrange
@@ -96,9 +12,9 @@ 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.")
+    print("Loading model...Done")
     return model, model_config
 
 # Function to set up, generate, and process the audio