Jaehun Kim eldrin

## my_melspec.py
import math
from typing import Callable, Optional
from warnings import warn

import torch
from torch import Tensor
from torchaudio import functional as F
from torchaudio.compliance import kaldi


## README-Template.md

      
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                eldrin
                / README-Template.md
            
            
              Created
              July 29, 2020 08:35
                — forked from PurpleBooth/README-Template.md
            
              
                A template to make good README.md
              
          
    Project Title

One Paragraph of project description goes here
Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Prerequisites


## wrmf_test.py
import os
from functools import partial

import numpy as np
import numba as nb
from scipy import sparse as sp

from implicit.als import AlternatingLeastSquares
from skopt import gp_minimize
from skopt.space import Real, Integer

## flatten_bibs.py
import os
import argparse

import bibtexparser


def read_bibtex(fn):
    """Load bib file
    """
    with open(fn) as bibf:

## process_essentia.py
from os.path import join, dirname, basename
import glob
import json
import argparse
from multiprocessing import Pool

import pandas as pd

from flatten_json import flatten_json
from tqdm import tqdm

## rf_feature_importance.py
from os.path import join, basename, splitext
import argparse

import numpy as np
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt

# setup parser
parser = argparse.ArgumentParser()
parser.add_argument("X", help="filename of the feature file (`.npy`) to visualize")

## visualize_pca_2d.py
from os.path import join, basename, splitext
import argparse

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

# setup parser
parser = argparse.ArgumentParser()
parser.add_argument("X", help="filename of the feature file (`.npy`) to visualize")

## easy_feature_extraction_keras2.py
import sys

from keras.models import Model
from keras.layers import GlobalAveragePooling2D as GAP2D
from keras.layers import concatenate as concat
import keras
import kapre

import librosa
import numpy as np
	import math
	from typing import Callable, Optional
	from warnings import warn

	import torch
	from torch import Tensor
	from torchaudio import functional as F
	from torchaudio.compliance import kaldi
	import os
	from functools import partial

	import numpy as np
	import numba as nb
	from scipy import sparse as sp

	from implicit.als import AlternatingLeastSquares
	from skopt import gp_minimize
	from skopt.space import Real, Integer
	import os
	import argparse

	import bibtexparser


	def read_bibtex(fn):
	"""Load bib file
	"""
	with open(fn) as bibf:
	from os.path import join, dirname, basename
	import glob
	import json
	import argparse
	from multiprocessing import Pool

	import pandas as pd

	from flatten_json import flatten_json
	from tqdm import tqdm
	from os.path import join, basename, splitext
	import argparse

	import numpy as np
	from sklearn.ensemble import RandomForestClassifier
	import matplotlib.pyplot as plt

	# setup parser
	parser = argparse.ArgumentParser()
	parser.add_argument("X", help="filename of the feature file (`.npy`) to visualize")
	import sys

	from keras.models import Model
	from keras.layers import GlobalAveragePooling2D as GAP2D
	from keras.layers import concatenate as concat
	import keras
	import kapre

	import librosa
	import numpy as np