Upload pipeline.py

pipeline.py

@@ -0,0 +1,16 @@
+from typing import Dict, List, Tuple
+import numpy as np
+import os
+import torch
+
+class PreTrainedPipeline():
+    def __init__(self, path=''):
+        self.model = torch.hub.load('sigsep/open-unmix-pytorch', 'umxhq')
+        self.sampling_rate = int(self.model.sample_rate.item())
+
+    def __call__(self, inputs): 
+        estimates = self.model(inputs.unsqueeze(0))
+        vocals = estimates[0][0].detach().numpy()
+        n = vocals.shape[0]
+        return vocals, self.sampling_rate, [f"label_{i}" for i in range(n)]
+