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{
"cells": [
{
"cell_type": "code",
"execution_count": 52,
"id": "c8f31382-77ac-47f8-bd3a-1c805b2d3e75",
"metadata": {},
"outputs": [],
"source": [
"import librosa\n",
"import soundfile\n",
"import os, glob, pickle\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "b0510279-2195-4784-a52b-20b6c18e216c",
"metadata": {},
"outputs": [],
"source": [
"# Extract features (mfcc, chroma, mel) from a sound file\n",
"def extract_feature(file_name, mfcc, chroma, mel):\n",
" with soundfile.SoundFile(file_name) as sound_file:\n",
" X = sound_file.read(dtype=\"float32\")\n",
" sample_rate=sound_file.samplerate\n",
" if chroma:\n",
" stft=np.abs(librosa.stft(X))\n",
" result=np.array([])\n",
" if mfcc:\n",
" mfccs=np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
" result=np.hstack((result, mfccs))\n",
" if chroma:\n",
" chroma=np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n",
" result=np.hstack((result, chroma))\n",
" if mel:\n",
" mel=np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate).T,axis=0)\n",
" result=np.hstack((result, mel))\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "d84a7785-e5b3-44ee-b484-a45fe61aa2af",
"metadata": {},
"outputs": [],
"source": [
"#Emotions in the RAVDESS dataset\n",
"emotions={\n",
" '01':'neutral',\n",
" '02':'calm',\n",
" '03':'happy',\n",
" '04':'sad',\n",
" '05':'angry',\n",
" '06':'fearful',\n",
" '07':'disgust',\n",
" '08':'surprised'\n",
"}\n",
"\n",
"# Emotions to observe\n",
"observed_emotions=['calm', 'happy', 'fearful', 'disgust']"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "5ebdbf11-1c7d-4bbf-9ff7-41b04cfbc902",
"metadata": {},
"outputs": [],
"source": [
"#Load the data and extract features for each sound file\n",
"def load_data(test_size=0.2):\n",
" x,y=[],[]\n",
" for file in glob.glob(\"C:\\\\Users\\\\Abhay\\\\Downloads\\\\dataset\\\\Actor_*\\\\*.wav\"):\n",
" file_name = os.path.basename(file)\n",
" emotion=emotions[file_name.split(\"-\")[2]]\n",
" if emotion not in observed_emotions:\n",
" continue\n",
" feature = extract_feature(file, mfcc=True, chroma=True, mel=True)\n",
" x.append(feature)\n",
" y.append(emotion)\n",
" return train_test_split(np.array(x), y, test_size=test_size, random_state=9)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "17e9421d-b474-4fc8-8321-435a2093c0cb",
"metadata": {},
"outputs": [],
"source": [
"#Split the dataset\n",
"x_train,x_test,y_train,y_test = load_data(test_size=0.25)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "eb1d0e4a-1766-4d3d-85ea-f69d88b6a007",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(576, 192)\n"
]
}
],
"source": [
"#Get the shape of the training and testing datasets\n",
"print((x_train.shape[0], x_test.shape[0]))"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "5a765afc-663d-48c0-9dbd-d58caf9069cc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Features extracted: 180\n"
]
}
],
"source": [
"# Get the number of features extracted\n",
"print(f'Features extracted: {x_train.shape[1]}')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "29c258f3-dbb6-4214-aea4-590487f5c68a",
"metadata": {},
"outputs": [],
"source": [
"#Initialize the Multi Layer Perceptron Classifier\n",
"model=MLPClassifier(alpha=0.01, batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive', max_iter=500)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "76939a33-c7fb-4ee3-b25f-af609dd3a5ce",
"metadata": {},
"outputs": [
{
"data": {
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"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
" learning_rate='adaptive', max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
" learning_rate='adaptive', max_iter=500)</pre></div></div></div></div></div>"
],
"text/plain": [
"MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
" learning_rate='adaptive', max_iter=500)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Train the model\n",
"model.fit(x_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "41976825-55d6-46eb-a389-eba2cacc540d",
"metadata": {},
"outputs": [],
"source": [
"# Predict for the test set\n",
"y_pred=model.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "2401ce73-6268-4751-9d68-3aa15f870f99",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 66.67%\n"
]
}
],
"source": [
"# Calculate the accuracy of our model\n",
"accuracy=accuracy_score(y_true=y_test, y_pred=y_pred)\n",
"\n",
"# Print the accuracy\n",
"print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "568ff907-2558-4f2b-bf4a-f10b889233cc",
"metadata": {},
"outputs": [],
"source": [
"with open('ser_model.pickle','wb') as f:\n",
" pickle.dump(model,f)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "19d865fc-504e-40dd-9822-a49ae0f3e568",
"metadata": {},
"outputs": [],
"source": [
"with open('ser_model.pickle','rb') as f:\n",
" mod = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "7cecff7e-060b-461d-a597-2b11ee731d97",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6666666666666666"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mod.score(x_test,y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bd184951-3715-4256-86ae-20d00a17a57b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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"name": "python3"
},
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|