Update app.py

app.py

@@ -1,10 +1,5 @@
 import torch
 import torchaudio
-import numpy as np
-import scipy
-import stempeg
-import os
-from openunmix import predict
 import gradio as gr
 import stempeg
 
@@ -13,36 +8,32 @@ torch.hub.download_url_to_file('https://github.com/AK391/open-unmix-pytorch/blob
 use_cuda = torch.cuda.is_available()
 device = torch.device("cuda" if use_cuda else "cpu")
 
+# loading umxhq four target separator
+separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxhq')
 
 def inference(audio):
-    start = 0 
-    stop = 7 
     audio, rate = stempeg.read_stems(
-        audio.name,
-        sample_rate=44100,
-        start=start,
-        duration=stop-start,
-    )
-    estimates = predict.separate(
-        audio=torch.as_tensor(audio).float(),
-        rate=44100,
-        device=device,
+        audio,
+        sample_rate=44100
     )
-    target_path = str("target.wav")
+    audio = torch.as_tensor(audio).float().T
+    audio = audio[None]
+    estimates = separator(audio)
+    estimates = separator.to_dict(estimates)
 
     estimates_numpy = {}
     for target, estimate in estimates.items():
         estimates_numpy[target] = torch.squeeze(estimate).detach().cpu().numpy().T
-
+    target_path = str("target.wav")
     stempeg.write_stems(
         target_path,
         estimates_numpy,
         sample_rate=rate,
         writer=stempeg.FilesWriter(multiprocess=True, output_sample_rate=44100),
     )
-    return 'vocals.wav', 'drums.wav', 'bass.wav', 'other.wav'
+    return 'vocals.wav', 'drums.wav', 'bass.wav', 'other.wav'   
     
-inputs = gr.inputs.Audio(label="Input Audio", type="file")
+inputs = gr.inputs.Audio(label="Input Audio", type="filepath")
 outputs =  [gr.outputs.Audio(label="Vocals", type="file"),
             gr.outputs.Audio(label="Drums", type="file"),
             gr.outputs.Audio(label="Bass", type="file"),
@@ -55,4 +46,4 @@ article = "<p style='text-align: center'><a href='https://joss.theoj.org/papers/
 
 examples = [['test.wav']]
 
-gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples).launch(enable_queue=True)
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(debug=True)