added function to controll paths to pyannote models

autotranscript/misc.py

@@ -6,15 +6,15 @@ import glob
 from warnings import warn
 import yaml
 
-WHISPER_DEFAULT_PATH = os.path.join(os.path.dirname(__file__),
+WHISPER_DEFAULT_PATH = os.path.relpath(os.path.join(os.path.dirname(__file__),
-                                     "models", "whisper")
+                                     "models", "whisper"))
 
-PYANNOTE_DEFAULT_PATH = os.path.join(os.path.dirname(__file__), 
+PYANNOTE_DEFAULT_PATH = os.path.relpath(os.path.join(os.path.dirname(__file__), 
                                      "models", "pyannote", 
-                                     "speaker_diarization", "config.yaml")
+                                     "speaker_diarization", "config.yaml"))
 
 
-def config_diarization_yaml(file):
+def config_diarization_yaml(file, path_to_segmentation = None, path_to_embedding = None):
     """
     Configure diarization pipeline from yaml file to use the model offline
     and avoid manuel file manipulation.
@@ -22,4 +22,36 @@ def config_diarization_yaml(file):
     :param file: yaml file
     :type file: yaml
     """
+    with open(file, "r") as stream:
+            yml = yaml.safe_load(stream)
+            stream.close()
+    if path_to_segmentation:
+        yml["pipeline"]["params"]["segmentation"] = path_to_segmentation
+    else:
+        yml["pipeline"]["params"]["segmentation"] = os.path.relpath(os.path.join(
+                                                                    os.path.dirname(__file__),
+                                                                    "models", "pyannote",
+                                                                    "segmentation",
+                                                                    "pytorch_model.bin"))
+                                                 
+    if path_to_embedding:
+        yml["pipeline"]["params"]["embedding"] = path_to_embedding
+    else:
+        yml["pipeline"]["params"]["embedding"] = os.path.relpath(
+                                                            os.path.join(
+                                                            os.path.dirname(__file__),
+                                                            "models", "pyannote",
+                                                            "speechbrain",
+                                                            "spkrec-ecapa-voxceleb",
+                                                            "embedding_model.ckpt"))
+    
+    if not os.path.exists(yml["pipeline"]["params"]["segmentation"]):
+        raise FileNotFoundError(f"Segmentation model not found at {yml['pipeline']['params']['segmentation']}")
+    
+    if not os.path.exists(yml["pipeline"]["params"]["embedding"]):
+        raise FileNotFoundError(f"Embedding model not found at {yml['pipeline']['params']['embedding']}")
+    
+    with open(file, "w") as stream:
+        yaml.dump(yml, stream)
+        stream.close()