Create app.py

app.py

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+import torch
+import torchaudio
+import numpy as np
+import scipy
+import stempeg
+import os
+from openunmix import predict
+import gradio as gr
+import stempeg
+
+torch.hub.download_url_to_file('https://github.com/AK391/open-unmix-pytorch/blob/master/test.wav?raw=true', 'test.wav')
+
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda" if use_cuda else "cpu")
+
+
+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,
+    )
+    target_path = str("target.wav")
+
+    estimates_numpy = {}
+    for target, estimate in estimates.items():
+        estimates_numpy[target] = torch.squeeze(estimate).detach().cpu().numpy().T
+
+    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'
+    
+inputs = gr.inputs.Audio(label="Input Audio", type="file")
+outputs =  [gr.outputs.Audio(label="Vocals", type="file"),
+            gr.outputs.Audio(label="Drums", type="file"),
+            gr.outputs.Audio(label="Bass", type="file"),
+            gr.outputs.Audio(label="Other Audio", type="file")]
+
+
+title = "OPEN-UNMIX"
+description = "gradio demo for OPEN-UNMIX, reference implementation for music source separation. To use it, simply add your audio, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://joss.theoj.org/papers/10.21105/joss.01667'>Open-Unmix - A Reference Implementation for Music Source Separation</a> | <a href='https://github.com/sigsep/open-unmix-pytorch'>Github Repo</a></p>"
+
+examples = [['test.wav']]
+
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()