fixed detector

src/detector.py

@@ -14,7 +14,7 @@ model_path = snapshot_download(repo_id="openai/whisper-base", cache_dir=cache_di
 asr = pipeline("automatic-speech-recognition", model=model_path)
 
 def detect_accent(audio_path: str):
-    result = asr(audio_path, return_timestamps=False)
+    result = asr(audio_path, return_timestamps=True)
     text = result["text"].lower()
 
     # Score dictionary for multiple accents
@@ -23,7 +23,6 @@ def detect_accent(audio_path: str):
         "British": 0,
         "Australian": 0,
         "Indian": 0,
-        "Other": 0,
     }
 
     # American patterns
@@ -39,7 +38,7 @@ def detect_accent(audio_path: str):
         accent_scores["British"] += 1
 
     # Australian patterns
-    if any(word in text for word in ["yeah nah", "arvo", "barbie", "brekkie", "mate"]):
+    if any(word in text for word in ["yeah nah", "arvo", "barbie", "brekkie"]):
         accent_scores["Australian"] += 2
     if "g’day" in text or "no worries" in text:
         accent_scores["Australian"] += 1
@@ -50,9 +49,10 @@ def detect_accent(audio_path: str):
     if any(word in text for word in ["co-brother", "timepass", "out of station"]):
         accent_scores["Indian"] += 1
 
-    # Determine best guess
+    # Determine best guess or fallback to "Unknown"
     top_accent = max(accent_scores, key=accent_scores.get)
-    confidence = accent_scores[top_accent] * 10 + 50  # basic mock confidence
-
-    return top_accent, min(confidence, 95), text
+    if accent_scores[top_accent] == 0:
+        return "Unknown", 50, text  # default fallback
 
+    confidence = accent_scores[top_accent] * 10 + 50  # simple mock scoring
+    return top_accent, min(confidence, 95), text