Update app.py

app.py

@@ -25,6 +25,8 @@ print("Loading DeepFilterNet model...", flush=True)
 df_model, df_state, _ = init_df()
 print("DeepFilterNet model ready!")
 
+USE_DEEPFILTERNET = True  # برای تست بدون DeepFilterNet، این رو موقتاً False کن
+
 @app.get("/health")
 async def health():
     return {"status": "ok", "message": "SubFlow backend is running"}
@@ -50,24 +52,32 @@ async def transcribe(websocket: WebSocket):
         await websocket.send_json({"status": "cleaning"})
         print("Step 3.5: cleaning audio with DeepFilterNet")
 
-        audio_data, _ = load_audio(tmp_path, sr=df_state.sr())
-        enhanced_audio = enhance(df_model, df_state, audio_data, atten_lim_db=20)
-        enhanced_path = tmp_path.replace(".wav", "_enhanced.wav")
-        save_audio(enhanced_path, enhanced_audio, df_state.sr())
-        print(f"Step 3.6: cleaned audio saved to {enhanced_path}")
+        audio_to_transcribe = tmp_path
+
+        if USE_DEEPFILTERNET:
+            await websocket.send_json({"status": "cleaning"})
+            print("Step 3.5: cleaning audio with DeepFilterNet")
+
+            audio_data, _ = load_audio(tmp_path, sr=df_state.sr())
+            enhanced_audio = enhance(df_model, df_state, audio_data, atten_lim_db=20)
+            enhanced_path = tmp_path.replace(".wav", "_enhanced.wav")
+            save_audio(enhanced_path, enhanced_audio, df_state.sr())
+            audio_to_transcribe = enhanced_path
+            print(f"Step 3.6: cleaned audio saved to {enhanced_path}")
+        else:
+            print("Step 3.5: skipping DeepFilterNet (disabled for A/B test)")
 
         await websocket.send_json({"status": "transcribing"})
         print("Step 4: starting transcription")
 
         segments_raw, info = model.transcribe(
-            enhanced_path,
+            audio_to_transcribe,
             language="en",
             word_timestamps=True,
             vad_filter=True,
             vad_parameters=dict(
                 min_silence_duration_ms=500,
                 speech_pad_ms=200,
-                threshold=0.3,
             ),
         )