eldrin

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

eldrin

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")

eldrin

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")

eldrin

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

eldrin

import os
import argparse

import bibtexparser


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

eldrin

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

eldrin

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

eldrin

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