Added simple versioning

sonogram.py

@@ -5,10 +5,13 @@ import torch
 import soundfile as sf
 import numpy as np
 from pyannote.core import Segment
+from pyannote.audio.models.segmentation import PyanNet
+from pyannote.audio import Inference
+from pyannote.pipeline.parameter import ParamDict
 
 class Sonogram():
 
-    def __init__(self):
+    def __init__(self,version='speaker-diarization-3.1'):
         self.earlyCleanup = True
         
         self.isTPU = False
@@ -26,8 +29,29 @@ class Sonogram():
                 print(f"GPU is not available")
             self.device = torch.device("cuda" if self.isGPU else "cpu")
             print(f"Using {self.device} instead.")
-    
-        self.pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
+
+        self.version = version
+        if version == 'speaker-diarization-3.1':
+            self.pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
+        elif version == '1.0':
+            baselinePipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
+            newSpecs = baselinePipeline._segmentation.model.specifications
+            segModel = PyanNet.from_pretrained('20251208_Sonogram_Segmentation.ckpt')
+            segModel.specifications = newSpecs
+            
+            segmentation_duration = segModel.specifications.duration
+            baselinePipeline._segmentation = Inference(
+                segModel,
+                duration=segmentation_duration,
+                step=baselinePipeline.segmentation_step * segmentation_duration,
+                skip_aggregation=True,
+                batch_size=1,
+            )
+            
+            baselinePipeline.segmentation = ParamDict(min_duration_off=0.0,)
+            self.pipeline = baselinePipeline
+        
+        
         self.pipeline.to(self.device)
 
         # Load SVM classifier