feat: add streaming segmentation task, model, and inference

pyannote/audio/__init__.py

@@ -27,8 +27,9 @@
 
 
 from .core.inference import Inference
+from .core.streaming_inference import StreamingInference
 from .core.io import Audio
 from .core.model import Model
 from .core.pipeline import Pipeline
 
-__all__ = ["Audio", "Model", "Inference", "Pipeline"]
+__all__ = ["Audio", "Model", "Inference", "Pipeline", "StreamingInference"]

pyannote/audio/core/inference.py

@@ -225,6 +225,7 @@ def infer(self, chunks: torch.Tensor) -> Union[np.ndarray, Tuple[np.ndarray]]:
         def __convert(output: torch.Tensor, conversion: nn.Module, **kwargs):
             return conversion(output).cpu().numpy()
 
+
         return map_with_specifications(
             self.model.specifications, __convert, outputs, self.conversion
         )
@@ -549,7 +550,7 @@ def aggregate(
         aggregated_scores : SlidingWindowFeature
             Aggregated scores. Shape is (num_frames, num_classes)
         """
-
+        print("aggregate")
         num_chunks, num_frames_per_chunk, num_classes = scores.data.shape
 
         chunks = scores.sliding_window
@@ -596,6 +597,7 @@ def aggregate(
             )
             + 1
         )
+
         aggregated_output: np.ndarray = np.zeros(
             (num_frames, num_classes), dtype=np.float32
         )
@@ -611,7 +613,6 @@ def aggregate(
         aggregated_mask: np.ndarray = np.zeros(
             (num_frames, num_classes), dtype=np.float32
         )
-
         # loop on the scores of sliding chunks
         for (chunk, score), (_, mask) in zip(scores, masks):
             # chunk ~ Segment
@@ -620,6 +621,7 @@ def aggregate(
 
             start_frame = frames.closest_frame(chunk.start + 0.5 * frames.duration)
 
+
             aggregated_output[start_frame : start_frame + num_frames_per_chunk] += (
                 score * mask * hamming_window * warm_up_window
             )
@@ -644,6 +646,7 @@ def aggregate(
 
         return SlidingWindowFeature(average, frames)
 
+
     @staticmethod
     def trim(
         scores: SlidingWindowFeature,