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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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | import os
import sys
import numpy as np
import librosa
import soundfile as sf
import pandas as pd
from tqdm import tqdm
import gc
import tensorflow as tf
import time
# Add project root to path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utils.hear_extractor import HeARExtractor
# --- Config ---
OUTPUT_DIR = r"c:\Users\ASUS\lung_ai_project\data\hear_embeddings_optimized"
TEMP_AUDIO_DIR = r"c:\Users\ASUS\lung_ai_project\data\temp_aug_audio"
ORIG_HEAR_DIR = r"c:\Users\ASUS\lung_ai_project\data\hear_embeddings"
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"
CHECKPOINT_INTERVAL = 50
# --- Augmentations ---
def add_noise(data, noise_factor=0.005):
noise = np.random.randn(len(data))
augmented_data = data + noise_factor * noise
return augmented_data
def speed_change(data, speed_factor=0.9):
# Resample is much faster than time_stretch/pitch_shift (FFT based)
# This changes both pitch and speed, which is a valid augmentation
new_len = int(len(data) / speed_factor)
return librosa.resample(data, orig_sr=16000, target_sr=int(16000*speed_factor))
# --- Data Collection ---
def get_sick_files():
files = []
# Coswara
csv_dir = os.path.join(COS_BASE, "csvs")
data_dir = os.path.join(COS_BASE, "coswara_data", "kaggle_data")
status_map = {}
if os.path.exists(csv_dir):
for csv_file in os.listdir(csv_dir):
if csv_file.endswith(".csv"):
df = pd.read_csv(os.path.join(csv_dir, csv_file))
if 'id' in df.columns and 'covid_status' in df.columns:
for _, row in df.iterrows():
status_map[row['id']] = row['covid_status']
if os.path.exists(data_dir):
for pid in os.listdir(data_dir):
status = status_map.get(pid)
if status and status.lower() != "healthy":
pid_dir = os.path.join(data_dir, pid)
for af in ["cough.wav", "cough-heavy.wav", "cough-shallow.wav"]:
path = os.path.join(pid_dir, af)
if os.path.exists(path):
files.append(path)
break
# Respiratory
resp_audio = os.path.join(RESP_BASE, "audio_and_txt_files")
resp_csv = os.path.join(RESP_BASE, "patient_diagnosis.csv")
if os.path.exists(resp_csv):
df = pd.read_csv(resp_csv)
diag_map = dict(zip(df['Patient_ID'], df['DIAGNOSIS']))
for f in os.listdir(resp_audio):
if f.endswith(".wav"):
try:
pid = int(f.split('_')[0])
diag = diag_map.get(pid)
if diag and diag.lower() != "healthy":
files.append(os.path.join(resp_audio, f))
except: continue
return files
def main():
# Setup directories
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
if not os.path.exists(TEMP_AUDIO_DIR):
os.makedirs(TEMP_AUDIO_DIR)
print("Identifying Sick Files...")
sick_files = get_sick_files()
print(f"Found {len(sick_files)} sick files.")
# Load feature lists
features = []
labels = []
# Check for existing checkpoint
checkpoint_path = os.path.join(OUTPUT_DIR, "checkpoint_indices.npy")
start_idx = 0
if os.path.exists(checkpoint_path):
start_idx = np.load(checkpoint_path).item()
print(f"Resuming from index {start_idx}")
features = list(np.load(os.path.join(OUTPUT_DIR, "X_checkpoint.npy")))
labels = list(np.load(os.path.join(OUTPUT_DIR, "y_checkpoint.npy")))
print("Loading HeAR Extractor...")
extractor = HeARExtractor()
# Processing Loop
processed_count = 0
for i in tqdm(range(start_idx, len(sick_files))):
file_path = sick_files[i]
try:
# Memory Cleanup
if processed_count % CHECKPOINT_INTERVAL == 0 and processed_count > 0:
gc.collect()
tf.keras.backend.clear_session()
# Save Checkpoint
np.save(os.path.join(OUTPUT_DIR, "X_checkpoint.npy"), np.array(features))
np.save(os.path.join(OUTPUT_DIR, "y_checkpoint.npy"), np.array(labels))
np.save(checkpoint_path, i)
# Load Audio (limit duration to 5s to save speed)
y, sr = librosa.load(file_path, sr=16000, duration=5.0)
if len(y) < 2000: # Skip empty/too short
continue
# Aug 1: Noise (Fast)
y_noise = add_noise(y)
temp_path_1 = os.path.join(TEMP_AUDIO_DIR, "temp_noise.wav")
sf.write(temp_path_1, y_noise, 16000)
emb1 = extractor.extract(temp_path_1)
if emb1 is not None:
features.append(emb1)
labels.append("sick")
# Aug 2: Speed/Pitch Change (Resampling - Fast)
y_speed = speed_change(y, speed_factor=0.9) # Slightly slower/deeper
temp_path_2 = os.path.join(TEMP_AUDIO_DIR, "temp_speed.wav")
sf.write(temp_path_2, y_speed, 16000)
emb2 = extractor.extract(temp_path_2)
if emb2 is not None:
features.append(emb2)
labels.append("sick")
except Exception as e:
print(f"Error on {file_path}: {e}")
continue
processed_count += 1
# Merge
print("Merging with Original Data...")
if os.path.exists(os.path.join(ORIG_HEAR_DIR, "X_hear.npy")):
X_orig = np.load(os.path.join(ORIG_HEAR_DIR, "X_hear.npy"))
y_orig = np.load(os.path.join(ORIG_HEAR_DIR, "y_hear.npy"))
X_final = np.concatenate([X_orig, np.array(features)])
y_final = np.concatenate([y_orig, np.array(labels)])
else:
X_final = np.array(features)
y_final = np.array(labels)
np.save(os.path.join(OUTPUT_DIR, "X_hear_opt_merged.npy"), X_final)
np.save(os.path.join(OUTPUT_DIR, "y_hear_opt_merged.npy"), y_final)
print(f"DONE. Saved {len(X_final)} total samples to {OUTPUT_DIR}")
# Cleanup Temp
try:
import shutil
shutil.rmtree(TEMP_AUDIO_DIR)
except: pass
if __name__ == "__main__":
main()
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