Update app.py

app.py

@@ -8,56 +8,47 @@ from funasr.utils.postprocess_utils import rich_transcription_postprocess
 from funasr.auto.auto_model import AutoModel
 import os
 
-model_dir = "FunAudioLLM/SenseVoiceSmall"
-try:
-    model = AutoModel(model=model_dir, vad_model="fsmn-vad", device="cpu", hub="hf")
-    print("✅ Model loaded successfully!")
-except Exception as e:
-    print("❌ Model loading error:", str(e))
-
 
+app = FastAPI()
 
+# Load mô hình SenseVoiceSmall
+model_dir = "FunAudioLLM/SenseVoiceSmall"
 
-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}
+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}