Update app.py

app.py

@@ -18,6 +18,7 @@ import soundfile as sf
 import librosa
 import noisereduce as nr
 import gradio as gr
+import huggingface_hub
 
 # Lazy imports (heavy models) will be done inside the worker function
 # to keep the app responsive on startup.
@@ -133,6 +134,18 @@ def pipeline_worker(video_file_path: str, keywords: List[str]):
         yield from emit(f"ERROR loading audio: {e}")
         return
 
+
+    if "use_auth_token" in inspect.signature(huggingface_hub.hf_hub_download).parameters:
+        pass  # old version, fine
+    else:
+    # patch for backward compatibility
+    def hf_hub_download_patch(*args, use_auth_token=None, **kwargs):
+        if use_auth_token is not None:
+            kwargs["token"] = use_auth_token
+        return huggingface_hub.hf_hub_download(*args, **kwargs)
+    huggingface_hub.hf_hub_download = hf_hub_download_patch
+
+
     # Lazy-load heavy models
     yield from emit("Loading diarization & embedding models (this can take a while)...")
     HF_TOKEN = os.environ.get("HF_TOKEN_1")
@@ -140,8 +153,8 @@ def pipeline_worker(video_file_path: str, keywords: List[str]):
     try:
         from pyannote.audio import Pipeline, Model
         # diarize_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2022.07", use_auth_token=HF_TOKEN)
-        diarize_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
-        embedding_model = Model.from_pretrained("pyannote/embedding")
+        diarize_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token =HF_TOKEN_E)
+        embedding_model = Model.from_pretrained("pyannote/embedding", use_auth_token = HF_TOKEN_E)
         
         yield from emit("pyannote models loaded.")
     except Exception as e: