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from fastapi import FastAPI, File, UploadFile
import librosa
import numpy as np
import shutil
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
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}
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