Create pipeline.py

pipeline.py

@@ -0,0 +1,35 @@
+from typing import Any, List, Tuple
+import numpy as np
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        # IMPLEMENT_THIS
+        # Preload all the elements you are going to need at inference.
+        # For instance your model, processors, tokenizer that might be needed.
+        # This function is only called once, so do all the heavy processing I/O here"""
+
+        self.sampling_rate = # IMPLEMENT THIS
+        
+        raise NotImplementedError(
+            "Please implement PreTrainedPipeline __init__ function"
+        )
+
+    def __call__(self, inputs: np.array) -> Tuple[np.array, int, List[str]]:
+        """
+        Args:
+            inputs (:obj:`np.array`):
+                The raw waveform of audio received. By default sampled at `self.sampling_rate`.
+                The shape of this array is `T`, where `T` is the time axis
+        Return:
+            A :obj:`tuple` containing:
+              - :obj:`np.array`:
+                 The return shape of the array must be `C'`x`T'`
+              - a :obj:`int`: the sampling rate as an int in Hz.
+              - a :obj:`List[str]`: the annotation for each out channel.
+                    This can be the name of the instruments for audio source separation
+                    or some annotation for speech enhancement. The length must be `C'`.
+        """
+        # IMPLEMENT_THIS
+        raise NotImplementedError(
+            "Please implement PreTrainedPipeline __call__ function"
+        )