Mynuddin-dev

A = np.array([[1, 2],[3,4],[5,6]])
B = np.array([[1, 2, 3], [4, 5, 6]])

Result=[]
for i in range(0,len(A)):
    temp=[]
    for j in range(0,len(B[0])):
        s = 0
        for k in range(0,len(A[0])):
            s += A[i][k]*B[k][j]

Mynuddin-dev

import numpy as np
input_matrix=np.array([[3,0,1,2,7,4],
                       [1,5,8,9,3,1],
                       [2,7,2,5,1,3],
                       [0,1,3,1,7,8],
                       [4,2,1,6,2,8],
                       [2,4,5,2,3,9] 
                      ])
                      

Mynuddin-dev

#!/usr/bin/bash

files=$(ls *.wav)

for file in $files;do
	filename=$(echo $file | cut -d "." -f 1)
	sox "$file" -r 48000 $filename{48000}.wav
	
done
```

Mynuddin-dev

import cv2

def take_photo():
    cap = cv2.VideoCapture(0) 
    ret , frame = cap.read()
    cv2.imwrite("SS.png",frame)
    cap.release()
    
    
take_photo()

Mynuddin-dev

import glob
from importlib.resources import path
from os import listdir
from os.path import isfile, join
import cv2
import os
import numpy as np
from random import shuffle
# from tqdm import tqdm
import pandas as pd

Mynuddin-dev

from keras.datasets import mnist
from keras.utils import to_categorical
from keras.layers import *
from keras.models import *
import numpy as np

## Load and preprocess the MNIST dataset
(train_images , train_labels) , (validation_images , validation_labels) = mnist.load_data()
## reshaping the images and normalizing the pixel values to be between 0 and 1.
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255

Mynuddin-dev

import cv2
import glob
import numpy as np
import os
import sys
import tensorflow as tf

from sklearn.model_selection import train_test_split

EPOCHS = 10

Mynuddin-dev

import cv2
import os
import numpy as np
path = "/home/myn/Downloads/all_images/"
destination = "/home/myn/Downloads/resized_all_images"
for img_name in os.listdir(path):
    # print(img_path)
    img = cv2.imread(str(path)+"/"+str(img_name))
    # print(img.shape)
    img = cv2.resize(img, (200,200), interpolation = cv2.INTER_AREA)

Mynuddin-dev

with open("librispeech-lexicon.txt") as f:
    liness = f.readlines()

vocabulary = open("vocab.txt" , 'w')
for line in liness:
   vocabulary.write(line.split()[0] +"\n")

Mynuddin-dev

with open("utt2spk") as f:
    liness = f.readlines()


utt2spk_final = open("utt2spk_final.txt" , 'w')
for line in liness:
    words = line.split()
    utt = (words[1],words[0])
    uttjoin = '-'.join(utt)
    # print(uttjoin)