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from fastapi import FastAPI, File, UploadFile
import librosa
import numpy as np
import shutil
import uvicorn
import os
from funasr import AutoModel
from starlette.middleware import Middleware
from starlette.middleware.cors import CORSMiddleware
app = FastAPI(
middleware=[
Middleware(
CORSMiddleware,
allow_origins=["*"], # Cho phép tất cả các origin
allow_credentials=True,
allow_methods=["*"], # Cho phép tất cả các phương thức
allow_headers=["*"], # Cho phép tất cả các header
)
]
)
# Tạo thư mục temp nếu chưa có
if not os.path.exists("temp"):
os.makedirs("temp")
# Load mô hình SenseVoiceSmall từ Hugging Face
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"
@app.get("/")
def read_root():
return {"message": "Hello, World!"}
print(app.routes)
# 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}
# Chạy FastAPI trên Hugging Face Spaces
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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