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f317798 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import os
import sys
import numpy as np
import pandas as pd
import librosa
import soundfile as sf
from tensorflow.keras.models import load_model
import random
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utils.hear_extractor import HeARExtractor
from utils.audio_preprocessor import advanced_preprocess
# --- Config ---
MODEL_PATH = r"c:\Users\ASUS\lung_ai_project\models\hear_classifier_advanced.h5"
CLASSES_PATH = r"c:\Users\ASUS\lung_ai_project\models\hear_classes_advanced.npy"
RESP_BASE = r"c:\Users\ASUS\lung_ai_project\data\extracted_cough\Respiratory_Sound_Dataset-main"
COS_BASE = r"c:\Users\ASUS\lung_ai_project\data\coswara"
def run_debug_test():
print("DEBUG: Initializing...")
extractor = HeARExtractor()
print("DEBUG: Loading Model...")
model = load_model(MODEL_PATH, compile=False)
classes = np.load(CLASSES_PATH)
print(f"DEBUG: Classes are {classes}")
# Pick one known sample
sample_path = r"c:\Users\ASUS\lung_ai_project\data\extracted_cough\Respiratory_Sound_Dataset-main\audio_and_txt_files\104_1b1_Al_sc_Litt3200.wav"
true_label = "sick"
print(f"DEBUG: Testing on {sample_path}")
if not os.path.exists(sample_path):
print("DEBUG: Sample path not found!")
return
# 1. Load Audio
y, sr = librosa.load(sample_path, sr=16000, duration=5.0)
print(f"DEBUG: Loaded audio, shape {y.shape}")
# 2. Preprocess
y_clean = advanced_preprocess(y, sr)
print(f"DEBUG: Preprocessed audio, length {len(y_clean)}")
# 3. Save to Temp
temp_path = "debug_temp.wav"
sf.write(temp_path, y_clean, 16000)
print(f"DEBUG: Saved temp file")
# 4. Extract
embedding = extractor.extract(temp_path)
if embedding is not None:
print(f"DEBUG: Extracted embedding, shape {embedding.shape}")
X = embedding[np.newaxis, ...]
preds = model.predict(X, verbose=0)
print(f"DEBUG: Raw predictions: {preds}")
pred_idx = np.argmax(preds[0])
pred_label = classes[pred_idx]
print(f"DEBUG: Predicted label: {pred_label}")
status = "OK" if pred_label == true_label else "MIS"
print(f"DEBUG: Result: {status}")
else:
print("DEBUG: Embedding extraction FAILED")
if __name__ == "__main__":
run_debug_test()
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