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{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"id": "9b5d89c1",
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: tensorflow in c:\\users\\msi\\appdata\\roaming\\python\\python39\\site-packages (2.17.0)\n",
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"Requirement already satisfied: werkzeug>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.18,>=2.17->tensorflow-intel==2.17.0->tensorflow) (2.0.3)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install tensorflow librosa numpy pandas matplotlib scikit-learn resampy xgboost"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2317d7f3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MSI\\AppData\\Local\\Temp\\ipykernel_2700\\1497878668.py:24: UserWarning: PySoundFile failed. Trying audioread instead.\n",
" audio, sample_rate = librosa.load(file_name, res_type='kaiser_fast')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error encountered while parsing file: soundclips\\discomfort\\Minta Gendong AUD-20150509-WA0000.wav, \n",
"Feature extraction failed for file: soundclips\\discomfort\\Minta Gendong AUD-20150509-WA0000.wav\n",
"Error encountered while parsing file: soundclips\\discomfort\\recordgntipopok.wav, \n",
"Feature extraction failed for file: soundclips\\discomfort\\recordgntipopok.wav\n",
"Error encountered while parsing file: soundclips\\hungry\\Lapar AUD-20150509-WA0001.wav, \n",
"Feature extraction failed for file: soundclips\\hungry\\Lapar AUD-20150509-WA0001.wav\n",
"Error encountered while parsing file: soundclips\\hungry\\record-baby-1 cari puting.wav, \n",
"Feature extraction failed for file: soundclips\\hungry\\record-baby-1 cari puting.wav\n",
"Error encountered while parsing file: soundclips\\hungry\\record-baby2 puting dilepas.wav, \n",
"Feature extraction failed for file: soundclips\\hungry\\record-baby2 puting dilepas.wav\n",
"Error encountered while parsing file: soundclips\\tired\\Bangun Tidur AUD-20150509-WA0002.wav, \n",
"Feature extraction failed for file: soundclips\\tired\\Bangun Tidur AUD-20150509-WA0002.wav\n"
]
}
],
"source": [
"import os\n",
"import numpy as np\n",
"import librosa\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from tensorflow.keras.utils import to_categorical\n",
"import xgboost as xgb\n",
"from sklearn.metrics import accuracy_score\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout, Activation\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.callbacks import ModelCheckpoint\n",
"\n",
"# Path to the dataset\n",
"dataset_path = 'soundclips'\n",
"\n",
"# List of categories\n",
"categories = ['belly_pain', 'burping', 'discomfort', 'hungry', 'tired']\n",
"\n",
"# Function to extract features from audio files\n",
"def extract_features(file_name):\n",
" try:\n",
" audio, sample_rate = librosa.load(file_name, res_type='kaiser_fast')\n",
" mfccs = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)\n",
" mfccs_scaled = np.mean(mfccs.T, axis=0)\n",
" \n",
" return mfccs_scaled\n",
" except Exception as e:\n",
" print(f\"Error encountered while parsing file: {file_name}, {e}\")\n",
" return None\n",
"\n",
"# Create DataFrame to hold features and labels\n",
"features = []\n",
"labels = []\n",
"\n",
"# Iterate through each category\n",
"for category in categories:\n",
" category_path = os.path.join(dataset_path, category)\n",
" if not os.path.exists(category_path):\n",
" print(f\"Directory does not exist: {category_path}\")\n",
" continue\n",
" \n",
" for file in os.listdir(category_path):\n",
" file_path = os.path.join(category_path, file)\n",
" data = extract_features(file_path)\n",
" if data is not None and len(data) > 0:\n",
" features.append(data)\n",
" labels.append(category)\n",
" else:\n",
" print(f\"Feature extraction failed for file: {file_path}\")\n",
"\n",
"# Convert to numpy arrays\n",
"features = np.array(features)\n",
"labels = np.array(labels)\n",
"\n",
"# Check if features array is empty\n",
"if features.size == 0:\n",
" raise ValueError(\"No features extracted. Please check the dataset and ensure audio files are present and readable.\")\n",
"\n",
"# Encode the labels\n",
"le = LabelEncoder()\n",
"labels_encoded = le.fit_transform(labels)\n",
"labels_categorical = to_categorical(labels_encoded)\n",
"\n",
"# Split the dataset\n",
"X_train, X_test, y_train, y_test = train_test_split(features, labels_categorical, test_size=0.2, random_state=42)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "955d889d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MSI\\AppData\\Roaming\\Python\\Python39\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m9s\u001b[0m 770ms/step - accuracy: 0.1250 - loss: 92.2737\n",
"Epoch 1: val_loss improved from inf to 11.10916, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.2303 - loss: 55.0404 - val_accuracy: 0.7282 - val_loss: 11.1092\n",
"Epoch 2/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.7500 - loss: 10.0197\n",
"Epoch 2: val_loss improved from 11.10916 to 8.67415, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7144 - loss: 12.5967 - val_accuracy: 0.7282 - val_loss: 8.6742\n",
"Epoch 3/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.6875 - loss: 17.6288\n",
"Epoch 3: val_loss improved from 8.67415 to 4.57907, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6595 - loss: 14.6687 - val_accuracy: 0.7282 - val_loss: 4.5791\n",
"Epoch 4/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.6875 - loss: 7.3426\n",
"Epoch 4: val_loss improved from 4.57907 to 3.37468, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6161 - loss: 8.2100 - val_accuracy: 0.7282 - val_loss: 3.3747\n",
"Epoch 5/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.5938 - loss: 6.5427\n",
"Epoch 5: val_loss improved from 3.37468 to 2.81896, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6420 - loss: 6.6541 - val_accuracy: 0.7282 - val_loss: 2.8190\n",
"Epoch 6/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.7500 - loss: 4.3348\n",
"Epoch 6: val_loss improved from 2.81896 to 2.11174, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6473 - loss: 5.6300 - val_accuracy: 0.7282 - val_loss: 2.1117\n",
"Epoch 7/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.5312 - loss: 8.7231\n",
"Epoch 7: val_loss improved from 2.11174 to 1.68834, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5790 - loss: 5.9270 - val_accuracy: 0.7282 - val_loss: 1.6883\n",
"Epoch 8/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.5938 - loss: 6.1949\n",
"Epoch 8: val_loss improved from 1.68834 to 1.39033, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6495 - loss: 4.2499 - val_accuracy: 0.7282 - val_loss: 1.3903\n",
"Epoch 9/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.7812 - loss: 2.6101\n",
"Epoch 9: val_loss improved from 1.39033 to 1.19555, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6688 - loss: 3.7850 - val_accuracy: 0.7282 - val_loss: 1.1956\n",
"Epoch 10/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.5938 - loss: 3.2523\n",
"Epoch 10: val_loss improved from 1.19555 to 1.19277, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6258 - loss: 3.3801 - val_accuracy: 0.7282 - val_loss: 1.1928\n",
"Epoch 11/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.6250 - loss: 2.2360\n",
"Epoch 11: val_loss improved from 1.19277 to 1.07271, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6525 - loss: 3.1927 - val_accuracy: 0.7282 - val_loss: 1.0727\n",
"Epoch 12/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.6562 - loss: 4.2252\n",
"Epoch 12: val_loss improved from 1.07271 to 1.03591, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6514 - loss: 3.0835 - val_accuracy: 0.7282 - val_loss: 1.0359\n",
"Epoch 13/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.6562 - loss: 3.3775\n",
"Epoch 13: val_loss did not improve from 1.03591\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6636 - loss: 2.7042 - val_accuracy: 0.7282 - val_loss: 1.0713\n",
"Epoch 14/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.6562 - loss: 3.2629\n",
"Epoch 14: val_loss did not improve from 1.03591\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6326 - loss: 2.5686 - val_accuracy: 0.7282 - val_loss: 1.0756\n",
"Epoch 15/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.6562 - loss: 3.7654\n",
"Epoch 15: val_loss improved from 1.03591 to 1.01481, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6663 - loss: 2.3855 - val_accuracy: 0.7282 - val_loss: 1.0148\n",
"Epoch 16/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.6250 - loss: 2.1722\n",
"Epoch 16: val_loss improved from 1.01481 to 1.00236, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6389 - loss: 1.9125 - val_accuracy: 0.7282 - val_loss: 1.0024\n",
"Epoch 17/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.4062 - loss: 2.8053\n",
"Epoch 17: val_loss improved from 1.00236 to 1.00158, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5887 - loss: 2.0436 - val_accuracy: 0.7282 - val_loss: 1.0016\n",
"Epoch 18/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.6562 - loss: 2.1894\n",
"Epoch 18: val_loss did not improve from 1.00158\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6451 - loss: 1.7902 - val_accuracy: 0.7282 - val_loss: 1.0104\n",
"Epoch 19/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.6875 - loss: 1.2992\n",
"Epoch 19: val_loss improved from 1.00158 to 0.98988, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6305 - loss: 1.8078 - val_accuracy: 0.7282 - val_loss: 0.9899\n",
"Epoch 20/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.5938 - loss: 1.9796\n",
"Epoch 20: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6325 - loss: 1.6304 - val_accuracy: 0.7282 - val_loss: 1.0112\n",
"Epoch 21/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7500 - loss: 1.0891\n",
"Epoch 21: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7144 - loss: 1.2691 - val_accuracy: 0.7282 - val_loss: 1.0299\n",
"Epoch 22/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.6875 - loss: 1.4959\n",
"Epoch 22: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6914 - loss: 1.5631 - val_accuracy: 0.7282 - val_loss: 1.0585\n",
"Epoch 23/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7500 - loss: 1.3707\n",
"Epoch 23: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7303 - loss: 1.2198 - val_accuracy: 0.7282 - val_loss: 1.0398\n",
"Epoch 24/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.6562 - loss: 2.6802\n",
"Epoch 24: val_loss did not improve from 0.98988\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6958 - loss: 1.6243 - val_accuracy: 0.7282 - val_loss: 1.0526\n",
"Epoch 25/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.6250 - loss: 1.3346\n",
"Epoch 25: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6771 - loss: 1.2211 - val_accuracy: 0.7282 - val_loss: 1.0524\n",
"Epoch 26/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.5312 - loss: 2.1067\n",
"Epoch 26: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6778 - loss: 1.6074 - val_accuracy: 0.7282 - val_loss: 1.0376\n",
"Epoch 27/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.5312 - loss: 1.5559\n",
"Epoch 27: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6782 - loss: 1.4016 - val_accuracy: 0.7282 - val_loss: 1.0039\n",
"Epoch 28/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8125 - loss: 0.6590\n",
"Epoch 28: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7852 - loss: 0.9609 - val_accuracy: 0.7282 - val_loss: 0.9953\n",
"Epoch 29/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.5938 - loss: 1.5004\n",
"Epoch 29: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7568 - loss: 1.1752 - val_accuracy: 0.7282 - val_loss: 1.0424\n",
"Epoch 30/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.6250 - loss: 1.5044\n",
"Epoch 30: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7052 - loss: 1.1279 - val_accuracy: 0.7282 - val_loss: 1.0329\n",
"Epoch 31/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.5938 - loss: 1.9206\n",
"Epoch 31: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7117 - loss: 1.3123 - val_accuracy: 0.7282 - val_loss: 1.0477\n",
"Epoch 32/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8438 - loss: 0.7891\n",
"Epoch 32: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7742 - loss: 0.9474 - val_accuracy: 0.7282 - val_loss: 1.0726\n",
"Epoch 33/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8750 - loss: 0.7086\n",
"Epoch 33: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7596 - loss: 1.1175 - val_accuracy: 0.7282 - val_loss: 1.0487\n",
"Epoch 34/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8750 - loss: 0.6791\n",
"Epoch 34: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7631 - loss: 1.0853 - val_accuracy: 0.7282 - val_loss: 1.0172\n",
"Epoch 35/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7188 - loss: 0.9913\n",
"Epoch 35: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7497 - loss: 1.0620 - val_accuracy: 0.7282 - val_loss: 0.9953\n",
"Epoch 36/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.8125 - loss: 0.6980\n",
"Epoch 36: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7718 - loss: 0.9211 - val_accuracy: 0.7282 - val_loss: 1.0045\n",
"Epoch 37/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8125 - loss: 0.6965\n",
"Epoch 37: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7637 - loss: 0.8684 - val_accuracy: 0.7282 - val_loss: 1.0102\n",
"Epoch 38/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9062 - loss: 0.6313\n",
"Epoch 38: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8126 - loss: 0.8493 - val_accuracy: 0.7282 - val_loss: 0.9966\n",
"Epoch 39/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.6875 - loss: 1.1845\n",
"Epoch 39: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7415 - loss: 1.0281 - val_accuracy: 0.7282 - val_loss: 1.0113\n",
"Epoch 40/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8125 - loss: 1.2241\n",
"Epoch 40: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7883 - loss: 1.0743 - val_accuracy: 0.7282 - val_loss: 1.0338\n",
"Epoch 41/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8438 - loss: 0.8787\n",
"Epoch 41: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8031 - loss: 0.8833 - val_accuracy: 0.7282 - val_loss: 1.0117\n",
"Epoch 42/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7188 - loss: 1.1767\n",
"Epoch 42: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7720 - loss: 0.9778 - val_accuracy: 0.7282 - val_loss: 0.9994\n",
"Epoch 43/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8125 - loss: 0.6985\n",
"Epoch 43: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7837 - loss: 0.9230 - val_accuracy: 0.7282 - val_loss: 1.0094\n",
"Epoch 44/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8750 - loss: 0.4748\n",
"Epoch 44: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7915 - loss: 0.9233 - val_accuracy: 0.7282 - val_loss: 1.0357\n",
"Epoch 45/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8750 - loss: 0.6965\n",
"Epoch 45: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8135 - loss: 0.8862 - val_accuracy: 0.7282 - val_loss: 1.0525\n",
"Epoch 46/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.7500 - loss: 0.7676\n",
"Epoch 46: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7945 - loss: 0.8852 - val_accuracy: 0.7282 - val_loss: 1.0297\n",
"Epoch 47/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.8438 - loss: 0.5471\n",
"Epoch 47: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7881 - loss: 0.8314 - val_accuracy: 0.7282 - val_loss: 0.9968\n",
"Epoch 48/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8750 - loss: 0.5049\n",
"Epoch 48: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8032 - loss: 0.8580 - val_accuracy: 0.7282 - val_loss: 0.9949\n",
"Epoch 49/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.7188 - loss: 0.9730\n",
"Epoch 49: val_loss did not improve from 0.98988\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7768 - loss: 0.9519 - val_accuracy: 0.7282 - val_loss: 0.9950\n",
"Epoch 50/100\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7812 - loss: 1.1724\n",
"Epoch 50: val_loss improved from 0.98988 to 0.98715, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7758 - loss: 1.0263 - val_accuracy: 0.7282 - val_loss: 0.9871\n",
"Epoch 51/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.7500 - loss: 1.0490\n",
"Epoch 51: val_loss improved from 0.98715 to 0.98223, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7547 - loss: 1.0155 - val_accuracy: 0.7282 - val_loss: 0.9822\n",
"Epoch 52/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7500 - loss: 1.2764\n",
"Epoch 52: val_loss did not improve from 0.98223\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7949 - loss: 0.9619 - val_accuracy: 0.7282 - val_loss: 0.9835\n",
"Epoch 53/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.7500 - loss: 1.3644\n",
"Epoch 53: val_loss improved from 0.98223 to 0.97895, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7872 - loss: 1.0066 - val_accuracy: 0.7282 - val_loss: 0.9789\n",
"Epoch 54/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8125 - loss: 0.7706\n",
"Epoch 54: val_loss improved from 0.97895 to 0.96493, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8054 - loss: 0.8880 - val_accuracy: 0.7282 - val_loss: 0.9649\n",
"Epoch 55/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.6875 - loss: 1.0864\n",
"Epoch 55: val_loss improved from 0.96493 to 0.95673, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7823 - loss: 0.8469 - val_accuracy: 0.7282 - val_loss: 0.9567\n",
"Epoch 56/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8125 - loss: 1.0572\n",
"Epoch 56: val_loss improved from 0.95673 to 0.95037, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7951 - loss: 0.8759 - val_accuracy: 0.7282 - val_loss: 0.9504\n",
"Epoch 57/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7812 - loss: 1.0491\n",
"Epoch 57: val_loss did not improve from 0.95037\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8068 - loss: 0.8893 - val_accuracy: 0.7282 - val_loss: 0.9679\n",
"Epoch 58/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.8438 - loss: 0.8212\n",
"Epoch 58: val_loss did not improve from 0.95037\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8067 - loss: 0.8464 - val_accuracy: 0.7282 - val_loss: 0.9785\n",
"Epoch 59/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7812 - loss: 0.8074\n",
"Epoch 59: val_loss did not improve from 0.95037\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8110 - loss: 0.7783 - val_accuracy: 0.7282 - val_loss: 0.9657\n",
"Epoch 60/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9375 - loss: 0.5068\n",
"Epoch 60: val_loss did not improve from 0.95037\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8376 - loss: 0.7360 - val_accuracy: 0.7282 - val_loss: 0.9663\n",
"Epoch 61/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7500 - loss: 0.8686\n",
"Epoch 61: val_loss did not improve from 0.95037\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8111 - loss: 0.7876 - val_accuracy: 0.7282 - val_loss: 0.9632\n",
"Epoch 62/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8125 - loss: 0.7523\n",
"Epoch 62: val_loss did not improve from 0.95037\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8128 - loss: 0.7946 - val_accuracy: 0.7282 - val_loss: 0.9733\n",
"Epoch 63/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.7812 - loss: 0.8872\n",
"Epoch 63: val_loss did not improve from 0.95037\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8017 - loss: 0.7635 - val_accuracy: 0.7282 - val_loss: 0.9592\n",
"Epoch 64/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.6562 - loss: 1.1472\n",
"Epoch 64: val_loss improved from 0.95037 to 0.94993, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7825 - loss: 0.8701 - val_accuracy: 0.7282 - val_loss: 0.9499\n",
"Epoch 65/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.7188 - loss: 0.8833\n",
"Epoch 65: val_loss improved from 0.94993 to 0.94609, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7906 - loss: 0.8554 - val_accuracy: 0.7282 - val_loss: 0.9461\n",
"Epoch 66/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7188 - loss: 0.9959\n",
"Epoch 66: val_loss improved from 0.94609 to 0.92950, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7973 - loss: 0.8486 - val_accuracy: 0.7282 - val_loss: 0.9295\n",
"Epoch 67/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.6562 - loss: 1.2690\n",
"Epoch 67: val_loss did not improve from 0.92950\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7939 - loss: 0.8727 - val_accuracy: 0.7282 - val_loss: 0.9404\n",
"Epoch 68/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8125 - loss: 0.7729\n",
"Epoch 68: val_loss did not improve from 0.92950\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8011 - loss: 0.8859 - val_accuracy: 0.7282 - val_loss: 0.9327\n",
"Epoch 69/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7812 - loss: 0.7983\n",
"Epoch 69: val_loss did not improve from 0.92950\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8240 - loss: 0.7458 - val_accuracy: 0.7282 - val_loss: 0.9319\n",
"Epoch 70/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.8125 - loss: 0.7055\n",
"Epoch 70: val_loss improved from 0.92950 to 0.92735, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8036 - loss: 0.7931 - val_accuracy: 0.7282 - val_loss: 0.9274\n",
"Epoch 71/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.7812 - loss: 0.8137\n",
"Epoch 71: val_loss improved from 0.92735 to 0.92599, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8023 - loss: 0.7893 - val_accuracy: 0.7282 - val_loss: 0.9260\n",
"Epoch 72/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8438 - loss: 0.6523\n",
"Epoch 72: val_loss improved from 0.92599 to 0.92060, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8132 - loss: 0.7949 - val_accuracy: 0.7282 - val_loss: 0.9206\n",
"Epoch 73/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.8125 - loss: 0.8290\n",
"Epoch 73: val_loss did not improve from 0.92060\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8300 - loss: 0.7700 - val_accuracy: 0.7282 - val_loss: 0.9273\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 74/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8438 - loss: 0.7377\n",
"Epoch 74: val_loss did not improve from 0.92060\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8034 - loss: 0.8272 - val_accuracy: 0.7282 - val_loss: 0.9297\n",
"Epoch 75/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8125 - loss: 0.8221\n",
"Epoch 75: val_loss did not improve from 0.92060\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8141 - loss: 0.8071 - val_accuracy: 0.7282 - val_loss: 0.9264\n",
"Epoch 76/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.8438 - loss: 0.6241\n",
"Epoch 76: val_loss did not improve from 0.92060\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8313 - loss: 0.7314 - val_accuracy: 0.7282 - val_loss: 0.9241\n",
"Epoch 77/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8125 - loss: 0.7567\n",
"Epoch 77: val_loss improved from 0.92060 to 0.91963, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8189 - loss: 0.7449 - val_accuracy: 0.7282 - val_loss: 0.9196\n",
"Epoch 78/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7812 - loss: 0.7788\n",
"Epoch 78: val_loss did not improve from 0.91963\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8098 - loss: 0.7512 - val_accuracy: 0.7282 - val_loss: 0.9206\n",
"Epoch 79/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.7188 - loss: 0.9284\n",
"Epoch 79: val_loss did not improve from 0.91963\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7784 - loss: 0.8218 - val_accuracy: 0.7282 - val_loss: 0.9208\n",
"Epoch 80/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8438 - loss: 0.5897\n",
"Epoch 80: val_loss improved from 0.91963 to 0.91612, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7774 - loss: 0.7675 - val_accuracy: 0.7282 - val_loss: 0.9161\n",
"Epoch 81/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7500 - loss: 0.8273\n",
"Epoch 81: val_loss improved from 0.91612 to 0.91471, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8109 - loss: 0.7768 - val_accuracy: 0.7282 - val_loss: 0.9147\n",
"Epoch 82/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8125 - loss: 0.7718\n",
"Epoch 82: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8297 - loss: 0.6972 - val_accuracy: 0.7282 - val_loss: 0.9167\n",
"Epoch 83/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8125 - loss: 0.7190\n",
"Epoch 83: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8061 - loss: 0.7488 - val_accuracy: 0.7282 - val_loss: 0.9196\n",
"Epoch 84/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7812 - loss: 0.9175\n",
"Epoch 84: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8036 - loss: 0.7816 - val_accuracy: 0.7282 - val_loss: 0.9226\n",
"Epoch 85/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8125 - loss: 0.7815\n",
"Epoch 85: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8151 - loss: 0.7643 - val_accuracy: 0.7282 - val_loss: 0.9201\n",
"Epoch 86/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.9062 - loss: 0.4617\n",
"Epoch 86: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8332 - loss: 0.6944 - val_accuracy: 0.7282 - val_loss: 0.9173\n",
"Epoch 87/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8125 - loss: 0.8332\n",
"Epoch 87: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8034 - loss: 0.7576 - val_accuracy: 0.7282 - val_loss: 0.9181\n",
"Epoch 88/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9062 - loss: 0.4584\n",
"Epoch 88: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8133 - loss: 0.7535 - val_accuracy: 0.7282 - val_loss: 0.9193\n",
"Epoch 89/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8438 - loss: 0.7613\n",
"Epoch 89: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8365 - loss: 0.7370 - val_accuracy: 0.7282 - val_loss: 0.9191\n",
"Epoch 90/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8750 - loss: 0.7454\n",
"Epoch 90: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8401 - loss: 0.6890 - val_accuracy: 0.7282 - val_loss: 0.9179\n",
"Epoch 91/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7812 - loss: 0.8456\n",
"Epoch 91: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8015 - loss: 0.7629 - val_accuracy: 0.7282 - val_loss: 0.9177\n",
"Epoch 92/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.6562 - loss: 1.1234\n",
"Epoch 92: val_loss did not improve from 0.91471\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7767 - loss: 0.8235 - val_accuracy: 0.7282 - val_loss: 0.9155\n",
"Epoch 93/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.8750 - loss: 0.6304\n",
"Epoch 93: val_loss improved from 0.91471 to 0.90965, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8300 - loss: 0.6764 - val_accuracy: 0.7282 - val_loss: 0.9097\n",
"Epoch 94/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8438 - loss: 0.7582\n",
"Epoch 94: val_loss improved from 0.90965 to 0.90822, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8243 - loss: 0.7305 - val_accuracy: 0.7282 - val_loss: 0.9082\n",
"Epoch 95/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8438 - loss: 0.6170\n",
"Epoch 95: val_loss improved from 0.90822 to 0.90646, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8193 - loss: 0.6996 - val_accuracy: 0.7282 - val_loss: 0.9065\n",
"Epoch 96/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8125 - loss: 0.5991\n",
"Epoch 96: val_loss improved from 0.90646 to 0.90536, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8026 - loss: 0.7276 - val_accuracy: 0.7282 - val_loss: 0.9054\n",
"Epoch 97/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.8125 - loss: 0.7258\n",
"Epoch 97: val_loss did not improve from 0.90536\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8285 - loss: 0.7067 - val_accuracy: 0.7282 - val_loss: 0.9055\n",
"Epoch 98/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.7812 - loss: 0.7810\n",
"Epoch 98: val_loss improved from 0.90536 to 0.90286, saving model to audio_classification.keras\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8163 - loss: 0.7018 - val_accuracy: 0.7282 - val_loss: 0.9029\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 99/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7188 - loss: 1.1051\n",
"Epoch 99: val_loss did not improve from 0.90286\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8075 - loss: 0.7663 - val_accuracy: 0.7282 - val_loss: 0.9030\n",
"Epoch 100/100\n",
"\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.8125 - loss: 0.6395\n",
"Epoch 100: val_loss did not improve from 0.90286\n",
"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7935 - loss: 0.7532 - val_accuracy: 0.7282 - val_loss: 0.9033\n",
"Test accuracy: 72.82%\n"
]
}
],
"source": [
"\n",
"\n",
"# Define the model\n",
"model = Sequential()\n",
"\n",
"model.add(Dense(256, input_shape=(40,)))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"\n",
"model.add(Dense(128))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"\n",
"model.add(Dense(64))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"\n",
"model.add(Dense(len(categories)))\n",
"model.add(Activation('softmax'))\n",
"\n",
"# Compile the model\n",
"model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')\n",
"\n",
"# Train the model\n",
"num_epochs = 100\n",
"num_batch_size = 32\n",
"\n",
"checkpointer = ModelCheckpoint(filepath='audio_classification.keras', \n",
" verbose=1, save_best_only=True)\n",
"\n",
"history = model.fit(X_train, y_train, batch_size=num_batch_size, epochs=num_epochs, validation_data=(X_test, y_test), callbacks=[checkpointer], verbose=1)\n",
"\n",
"# Evaluate the model\n",
"test_accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
"print(f'Test accuracy: {test_accuracy[1] * 100:.2f}%')\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0559c8e5",
"metadata": {},
"outputs": [],
"source": [
"# Save the model\n",
"model.save('infant_cry_classification_model.keras')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "171ff113",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
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"nbformat": 4,
"nbformat_minor": 5
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|