label

app.py

@@ -60,79 +60,6 @@
 
  
 
-# import os
-# import gradio as gr
-# from scipy.io.wavfile import write
-# import subprocess
-# import torch
-
-# # Assuming audio_separator is available in your environment
-# from audio_separator import Separator  
-
-# def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
-#     # Initially, show the loading GIF
-#     loading_gif_path = "7RwF.gif"
-#     gr.Image(loading_gif_path,visible=True) 
-
-#     os.makedirs("out", exist_ok=True)
-#     audio_path = 'test.wav'
-#     write(audio_path, audio[0], audio[1])
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     print(f"Using device: {device}")
-
-#     try:
-#         command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out"
-#         process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-#         print("Demucs script output:", process.stdout.decode())
-#     except subprocess.CalledProcessError as e:
-#         print("Error in Demucs script:", e.stderr.decode())
-#         return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
-
-#     try:
-#         separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device=='cuda', output_format='wav')
-#         primary_stem_path, secondary_stem_path = separator.separate()
-#     except Exception as e:
-#         print("Error in custom separation:", str(e))
-#         return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
-
-#     stem_paths = {
-#         "vocals": "./out/htdemucs_6s/test/vocals.wav" if vocals else None,
-#         "bass": "./out/htdemucs_6s/test/bass.wav" if bass else None,
-#         "drums": "./out/htdemucs_6s/test/drums.wav" if drums else None,
-#         "other": "./out/htdemucs_6s/test/other.wav" if other else None,
-#         "piano": "./out/htdemucs_6s/test/piano.wav" if piano else None,
-#         "guitar": "./out/htdemucs_6s/test/guitar.wav" if guitar else None,
-#         "lead_vocals": primary_stem_path if lead_vocals else None,
-#         "backing_vocals": secondary_stem_path if backing_vocals else None
-#     }
-
-#     # Once processing is done, hide the GIF by returning a transparent image
-#     gr.Image(visible=False)
-#     return [gr.Audio(stem_paths[stem], visible=bool(stem_paths[stem])) for stem in stem_paths]  
-
-# # Define checkboxes for each stem
-# checkbox_labels = ["Full Vocals", "Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals"]
-# checkboxes = [gr.components.Checkbox(label=label) for label in checkbox_labels]
-
-# # Gradio Interface
-# title = "Source Separation Demo"
-# description = "Music Source Separation in the Waveform Domain. Upload your audio to begin."
-# iface = gr.Interface(
-#     inference, 
-#     [gr.components.Audio(type="numpy", label="Input")] + checkboxes,
-#     [gr.Audio(label=label, visible=False) for label in checkbox_labels],
-#     title=title,
-#     description=description,
-# )
-
-# iface.launch()
-
-
-
-
-
-
-
 import os
 import gradio as gr
 from scipy.io.wavfile import write
@@ -142,37 +69,31 @@ import torch
 # Assuming audio_separator is available in your environment
 from audio_separator import Separator  
 
-# Global variable to track the display state of the GIF
-show_loading_gif = True
-
-def start_loading():
-    global show_loading_gif
-    show_loading_gif = True
+def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
+    status_message = "Processing..."
 
-def stop_loading():
-    global show_loading_gif
-    show_loading_gif = False
 
-def process_audio(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
-    # Audio processing logic
     os.makedirs("out", exist_ok=True)
     audio_path = 'test.wav'
     write(audio_path, audio[0], audio[1])
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print(f"Using device: {device}")
 
     try:
         command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out"
         process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        print("Demucs script output:", process.stdout.decode())
     except subprocess.CalledProcessError as e:
-        return None  # Handle the error appropriately
+        print("Error in Demucs script:", e.stderr.decode())
+        return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
 
     try:
         separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device=='cuda', output_format='wav')
         primary_stem_path, secondary_stem_path = separator.separate()
     except Exception as e:
-        return None  # Handle the error appropriately
+        print("Error in custom separation:", str(e))
+        return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
 
-    # Generate paths for the stems
     stem_paths = {
         "vocals": "./out/htdemucs_6s/test/vocals.wav" if vocals else None,
         "bass": "./out/htdemucs_6s/test/bass.wav" if bass else None,
@@ -184,23 +105,9 @@ def process_audio(audio, vocals, bass, drums, other, piano, guitar, lead_vocals,
         "backing_vocals": secondary_stem_path if backing_vocals else None
     }
 
-    return [stem_paths[stem] for stem in stem_paths]
+    # Once processing is done, hide the GIF by returning a transparent image
 
-def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
-    global show_loading_gif
-
-    # Start loading
-    start_loading()
-
-    # Call the main processing function
-    audio_outputs = process_audio(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals)
-
-    # Stop loading
-    stop_loading()
-
-    # Return the outputs along with the loading GIF state
-    loading_gif_path = "7RwF.gif" if show_loading_gif else ""
-    return loading_gif_path, *audio_outputs
+    return [gr.Audio(stem_paths[stem], visible=bool(stem_paths[stem])) for stem in stem_paths], "Done! Successfully processed."
 
 # Define checkboxes for each stem
 checkbox_labels = ["Full Vocals", "Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals"]
@@ -212,10 +119,11 @@ description = "Music Source Separation in the Waveform Domain. Upload your audio
 iface = gr.Interface(
     inference, 
     [gr.components.Audio(type="numpy", label="Input")] + checkboxes,
-    [gr.Image()] + [gr.Audio(label=label, visible=False) for label in checkbox_labels],
+    [gr.Audio(label=label, visible=False) for label in checkbox_labels] + [gr.Label()],
     title=title,
     description=description,
 )
 
 iface.launch()
 
+