Create app.py

app.py

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+from fastapi import FastAPI, File, UploadFile
+import librosa
+import numpy as np
+import shutil
+from funasr import AutoModel
+
+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="cpu", 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}