Update app.py

app.py

@@ -5,46 +5,51 @@ import shutil
 from funasr import AutoModel
 from funasr.utils.postprocess_utils import rich_transcription_postprocess
 
+from funasr import list_models
+
+print(list_models())  # Xem danh sách model hỗ trợ
+
+
 app = FastAPI()
 
-# Load mô hình SenseVoiceSmall
-model_dir = "FunAudioLLM/SenseVoiceSmall"
-
-
-model = AutoModel(
-    model=model_dir,
-    vad_model="fsmn-vad",
-    vad_kwargs={"max_single_segment_time": 30000},
-    device="cuda:0",
-    hub="hf",
-)
-
-# Hàm tính RMS energy
-def calculate_rms_energy(audio_path):
-    y, sr = librosa.load(audio_path)
-    rms = librosa.feature.rms(y=y)[0]
-    return np.mean(rms)
-
-# Hàm phát hiện tiếng ồn
-def detect_noise(audio_path):
-    rms_energy = calculate_rms_energy(audio_path)
-    res = model.generate(input=audio_path, language="auto", audio_event_detection=True)
-    audio_events = res[0].get("audio_event_detection", {})
-
-    if rms_energy > 0.02:
-        return "ồn ào"
-    elif rms_energy > 0.01:
-        for event_label, event_score in audio_events.items():
-            if event_score > 0.7 and event_label in ["laughter", "applause", "crying", "coughing"]:
-                return f"ồn ào ({event_label})"
-    return "yên tĩnh"
-
-# API nhận file âm thanh từ Flutter
-@app.post("/detect-noise/")
-async def detect_noise_api(file: UploadFile = File(...)):
-    file_path = f"temp/{file.filename}"
-    with open(file_path, "wb") as buffer:
-        shutil.copyfileobj(file.file, buffer)
-
-    result = detect_noise(file_path)
-    return {"noise_level": result}
+# # Load mô hình SenseVoiceSmall
+# model_dir = "FunAudioLLM/SenseVoiceSmall"
+
+
+# model = AutoModel(
+#     model=model_dir,
+#     vad_model="fsmn-vad",
+#     vad_kwargs={"max_single_segment_time": 30000},
+#     device="cuda:0",
+#     hub="hf",
+# )
+
+# # Hàm tính RMS energy
+# def calculate_rms_energy(audio_path):
+#     y, sr = librosa.load(audio_path)
+#     rms = librosa.feature.rms(y=y)[0]
+#     return np.mean(rms)
+
+# # Hàm phát hiện tiếng ồn
+# def detect_noise(audio_path):
+#     rms_energy = calculate_rms_energy(audio_path)
+#     res = model.generate(input=audio_path, language="auto", audio_event_detection=True)
+#     audio_events = res[0].get("audio_event_detection", {})
+
+#     if rms_energy > 0.02:
+#         return "ồn ào"
+#     elif rms_energy > 0.01:
+#         for event_label, event_score in audio_events.items():
+#             if event_score > 0.7 and event_label in ["laughter", "applause", "crying", "coughing"]:
+#                 return f"ồn ào ({event_label})"
+#     return "yên tĩnh"
+
+# # API nhận file âm thanh từ Flutter
+# @app.post("/detect-noise/")
+# async def detect_noise_api(file: UploadFile = File(...)):
+#     file_path = f"temp/{file.filename}"
+#     with open(file_path, "wb") as buffer:
+#         shutil.copyfileobj(file.file, buffer)
+
+#     result = detect_noise(file_path)
+#     return {"noise_level": result}