Files
pengujian-bacaan/.ipynb_checkpoints/Pengujian Notebook-checkpoint.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "7d2216e0",
"metadata": {},
"source": [
"### Load Suara Ke Librosa dan Menghitung nilai MFCC\n",
"#### MFCC (Mel-frequency cepstral coefficients) dihitung dalam bentuk 2D array (List) "
]
},
{
"cell_type": "code",
"execution_count": 307,
"id": "55e239c0",
"metadata": {},
"outputs": [],
"source": [
"import librosa\n",
"import librosa.display as display\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"y1, sr1 = librosa.load('bacaan/ustazah/b&t/b2.wav',sr=48000) #load bacaan ustazah ke librosa\n",
"y2, sr2 = librosa.load('coba.wav',sr=48000) #load bacaan pengujian ke librosa\n",
"\n",
"# print(y1)\n",
"# print(len(y1))\n",
"# print(len(y2))\n",
"\n",
"# len_sound1 = len(y1)\n",
"# len_sound2 = len(y2)\n",
"\n",
"# if(len_sound1 > len_sound2):\n",
"# y2 = librosa.util.fix_length(y2, size=len_sound1)\n",
"# else:\n",
"# y1 = librosa.util.fix_length(y1, size=len_sound2)\n",
"\n",
"\n",
"# plt.subplot(1, 2, 1) \n",
"mfcc1 = librosa.feature.mfcc(y1,sr1) #Computing MFCC values , mengubah suara menjadi 2D array\n",
"# print(mfcc1)\n",
"# # print(mfcc1)\n",
"# display.specshow(mfcc1)\n",
"\n",
"# plt.subplot(1, 2, 2)\n",
"mfcc2 = librosa.feature.mfcc(y2,sr2) #Computing MFCC values , mengubah suara menjadi 2D array\n",
"# # print(mfcc2)\n",
"# display.specshow(mfcc2)\n",
"\n",
"# librosa.get_duration(y=y1, sr=sr1)\n"
]
},
{
"cell_type": "code",
"execution_count": 308,
"id": "c8d304d7",
"metadata": {},
"outputs": [],
"source": [
"# librosa.get_duration(y=y2, sr=sr2)"
]
},
{
"cell_type": "markdown",
"id": "1b86b52f",
"metadata": {},
"source": [
"#### Menampilkan visualisasi suara 1 dalam bentuk waveshow "
]
},
{
"cell_type": "code",
"execution_count": 309,
"id": "b181d059",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# fig, ax = plt.subplots()\n",
"plt.figure(figsize=(14, 5))\n",
"display.waveshow(y1, sr=sr1)\n",
"plt.savefig('spec.png')"
]
},
{
"cell_type": "markdown",
"id": "802547fa",
"metadata": {},
"source": [
"#### Menampilkan visualisasi suara 2 dalam bentuk waveshow "
]
},
{
"cell_type": "code",
"execution_count": 310,
"id": "137ac26f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14, 5))\n",
"display.waveshow(y2, sr=sr2)\n",
"plt.savefig('spec1.png')"
]
},
{
"cell_type": "markdown",
"id": "5b8fb13c",
"metadata": {},
"source": [
"#### Menghitung jarak normlisasi antara dua suara\n",
"#### Semakin dekat dengan angka 0, semakin mirip/sama kedua suara tersebut"
]
},
{
"cell_type": "code",
"execution_count": 311,
"id": "31bc2889",
"metadata": {},
"outputs": [],
"source": [
"# from dtw import dtw\n",
"# from numpy.linalg import norm\n",
"\n",
"# dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T, dist=lambda x, y: norm(x - y, ord=1))\n",
"# print('Normalized distance between the two sounds: '+ dist.__str__())"
]
},
{
"cell_type": "markdown",
"id": "00f25b00",
"metadata": {},
"source": [
"#### Menghitung kesamaan/kemiripan dua suara dengan rumus cosine_similarity"
]
},
{
"cell_type": "code",
"execution_count": 312,
"id": "ccb9aca0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8781866567291359\n"
]
}
],
"source": [
"\n",
"\n",
"def dot(A,B): \n",
" return (sum(a*b for a,b in zip(A,B))) #rumus cosine similarity\n",
"\n",
"def cosine_similarity(a,b):\n",
" return dot(a,b) / ( (dot(a,a) **.5) * (dot(b,b) ** .5) ) #rumus cosine similarity\n",
"\n",
"# from math import*\n",
"# def euclidean_distance(x,y):\n",
"# return sqrt(sum(pow(a-b,2) for a, b in zip(x, y)))\n",
" \n",
"# def manhattan_distance(x,y):\n",
"# return sum(abs(a-b) for a,b in zip(x,y))\n",
"\n",
"# def jaccard_similarity(x,y):\n",
" \n",
"# intersection_cardinality = len(set.intersection(*[set(x), set(y)]))\n",
"# union_cardinality = len(set.union(*[set(x), set(y)]))\n",
"# return intersection_cardinality/float(union_cardinality)\n",
"\n",
"# def square_rooted(x):\n",
" \n",
"# return round(sqrt(sum([a*a for a in x])),3)\n",
" \n",
"# def cosine_similarity(x,y):\n",
" \n",
"# numerator = sum(a*b for a,b in zip(x,y))\n",
"# denominator = square_rooted(x)*square_rooted(y)\n",
"# return round(numerator/float(denominator),3)\n",
"\n",
"array1 = [] \n",
"for nums in mfcc1:\n",
" for val in nums:\n",
" array1.append(val) # mengubah 2D array menjadi 1D array\n",
" \n",
"# print(array1)\n",
" \n",
"array2 = []\n",
"for nums in mfcc2:\n",
" for val in nums:\n",
" array2.append(val) # mengubah 2D array menjadi 1D array\n",
" \n",
"\n",
"print(cosine_similarity(array1, array2))\n",
"\n",
"\n",
"# print(jaccard_similarity(array1, array2))"
]
},
{
"cell_type": "code",
"execution_count": 313,
"id": "8b2a0727",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"# from scipy.spatial.distance import cosine,cityblock,euclidean,minkowski,jaccard\n",
"# print(jaccard(array1,array2))"
]
}
],
"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": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}