anisayari

import pylast
import pandas as pd
from tqdm import tqdm
API_KEY_lastfm = "key_here"
API_SECRET_lastfm = "	key_here"
username_lastfm = "username"

def build_dataset_by_tag(tag_list,output_file):
    print('[INFO] Bulding Dataset by Tag....')
    network_lastfm = pylast.LastFMNetwork(api_key=API_KEY_lastfm, api_secret=API_SECRET_lastfm,username=username_lastfm)

anisayari

import csv
import pandas as pd
import googleapiclient
from tqdm import tqdm

DEVELOPER_KEY_GCP = "key_here"

def get_video_id_from_youtube(df):
    # Disable OAuthlib's HTTPS verification when running locally.
    # *DO NOT* leave this option enabled in production.

anisayari

import youtube_dl
import pandas as pd
def get_youtube_music(row):
    path = 'data/music/{}/{}.mp3'.format(row['style'],str(row['uuid']))
    exists = os.path.isfile(path)
    if exists:
        print('Already exist {}'.format(row['uuid']))
        return
    if row['videoID_youtube'] != "missing":
        videoID = row['videoID_youtube']

anisayari

import librosa
import numpy as np
import pandas as pd
from tqdm import tqdm

def extract_features_from_a_song(x,sr):
    dict_features = {
        'zcr': np.mean(librosa.feature.zero_crossing_rate(x)),
        'chroma_stft': np.mean(librosa.feature.chroma_stft(x, sr=sr)),
        'mfcc': np.mean(librosa.feature.mfcc(x, sr=sr)),

anisayari

def reduce_mem_usage(df):
    """ iterate through all the columns of a dataframe and modify the data type
        to reduce memory usage.
    """
    start_mem = df.memory_usage().sum() / 1024 ** 2
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))

    for col in df.columns:
        col_type = df[col].dtype

anisayari

def reduce_mem_usage(df):
    """ iterate through all the columns of a dataframe and modify the data type
        to reduce memory usage.
    """
    start_mem = df.memory_usage().sum() / 1024 ** 2
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))

    for col in df.columns:
        col_type = df[col].dtype

anisayari

import cv2
import dlib
import PIL.Image
import numpy as np
from imutils import face_utils
import argparse
from pathlib import Path
import os
import ntpath

anisayari

print('[INFO] Starting System...')
print('[INFO] Importing pretrained model..')
pose_predictor_68_point = dlib.shape_predictor("pretrained_model/shape_predictor_68_face_landmarks.dat")
pose_predictor_5_point = dlib.shape_predictor("pretrained_model/shape_predictor_5_face_landmarks.dat")
face_encoder = dlib.face_recognition_model_v1("pretrained_model/dlib_face_recognition_resnet_model_v1.dat")
face_detector = dlib.get_frontal_face_detector()
print('[INFO] Importing pretrained model..')

anisayari

import React, { useEffect, useRef } from 'react';
import * as THREE from 'three';
import { EffectComposer } from 'three/examples/jsm/postprocessing/EffectComposer';
import { RenderPass } from 'three/examples/jsm/postprocessing/RenderPass';
import { UnrealBloomPass } from 'three/examples/jsm/postprocessing/UnrealBloomPass';
import { OutputPass } from 'three/examples/jsm/postprocessing/OutputPass';
import ShaderCode from './ShaderCode';

const AudioVisualizer3D = ({ audioStream, type }) => {
    const { vertexShader, fragmentShader } = ShaderCode();