updated

backend/services/interview_engine.py

@@ -175,31 +175,36 @@ def convert_webm_to_wav(webm_path, wav_path):
         logging.error(f"Error converting audio: {e}")
         return None
 
+import subprocess  # top of the file if not already imported
+
 def whisper_stt(audio_path):
+    """Speech-to-text using Faster-Whisper"""
     try:
-        if not audio_path or not os.path.exists(audio_path):
-            logging.error(f"Audio file does not exist: {audio_path}")
-            return ""
-
-        if os.path.getsize(audio_path) == 0:
-            logging.error(f"Audio file is empty: {audio_path}")
+        if not os.path.exists(audio_path) or os.path.getsize(audio_path) == 0:
+            logging.error(f"Audio file is empty or missing: {audio_path}")
             return ""
 
-        # Convert WebM to WAV using ffmpeg (ensure ffmpeg is available)
-        converted_path = audio_path.replace(".webm", ".wav")
-        subprocess.run([
-            "ffmpeg", "-y", "-i", audio_path, "-ar", "16000", "-ac", "1", converted_path
-        ], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+        # Convert webm to wav using ffmpeg
+        wav_path = audio_path.replace(".webm", ".wav")
+        cmd = [
+            "ffmpeg",
+            "-y",  # overwrite
+            "-i", audio_path,
+            "-ar", "16000",
+            "-ac", "1",
+            "-f", "wav",
+            wav_path
+        ]
+        subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
 
-        if not os.path.exists(converted_path) or os.path.getsize(converted_path) == 0:
-            logging.error(f"Conversion failed or produced empty file: {converted_path}")
+        if not os.path.exists(wav_path) or os.path.getsize(wav_path) == 0:
+            logging.error(f"FFmpeg conversion failed or produced empty file: {wav_path}")
             return ""
 
         model = load_whisper_model()
-        segments, _ = model.transcribe(converted_path)
+        segments, _ = model.transcribe(wav_path)
         transcript = " ".join(segment.text for segment in segments)
         return transcript.strip()
-
     except Exception as e:
         logging.error(f"Error in STT: {e}")
         return ""