Guang

image image image image

Universal function library of scientific calculation.

Requirements

Python 3
CUDA >= 10.0
PyTorch >= 1.0
Tensorflow >= 1.12.0
opencv-python
pydub
librosa
pyworld
soundfile
pypinyin
pomegranate
python-Levenshtein
streamlit
pyprobar
fire

Installation

# Before install `guang`, you need to have all the requirements installed.
pip install guang

​ Nightly Build

git clone https://github.com/beidongjiedeguang/guang.git
cd guang
python ./night_workflow.py

Examples

  • Convert audio in .mp3/ .wav format to (sample rate=16k, single channel) .wav format

    Use in bash/shell

    guang cvt2wav INPUT OUTPUT

    Use as function

    from guang.Voice.convert import cvt2wav
cvt2wav(input_name, output_name, sr=16000)

# Multi-process
from guang.Voice.convert import multi_cvt2wav 
multi_cvt2wav(PATH1, PATH2,sr=16000, n_cpu=None)

  • Let the video play at N times speed (install ffmpeg first)

    guang av_speed inputname outputname N --cut_frame=False

  • Fourier visualization

    guang fourier

  • Fourier draw anything you want

  • FFT convolution

    from guang.sci import fft
A = np.random.rand(100, 100)
B = np.random.rand(100, 100)
fft.fft_conv2d(A, B)

  • Use dict_dotable to convert a dictionary to dot-able dictionary:

    from guang.Utils.toolsFunc import dict_dotable
a = {'a':{'b':1}}
a = dict_dotable(a)
print(a.a.b)

>> 1

  • Use probar to display current progress has separated to pyprobar

    from guang.Utils.bar import bar, probar
for idx, x in probar(range(10)):
    time.sleep(0.8)

>> 100.00% |█████████████████████████████| 0'7.2"|0'7.2" ETC: 12-2 23:59:8

N = 1024
a = np.linspace(2, 5, N)
for idx, i in enumerate(a):
    time.sleep(0.01)
    bar(idx, N)
>> 100.00% |█████████████████████████████| 0:00:00|0:00:10  ETC: 02-19 20:33:34

  • @broadcast broadcast a non-broadcast function.

    from guang.Utils.toolsFunc import broadcast

@broadcast
def f(x):
    # A function that can map only a single element
    if x==1 or x==0:
        return x
    else:
        return f(x-1)+f(x-2)

>> f([2,4,10])
>> array([1, 3, 832040], dtype=object)

  • download download files from google drive.

    from guang.Utils.google import download
url = "https://drive.google.com/open?id=1eU57Fkv1DBEOqi-iOs1AebD02FqVDY23"
outname = "filename.zip"
download(url, outname)

  • txt2ph Convert Chinese characters to phoneme.

    from guang.Voice.txt2pinyin import txt2ph
txt2ph('你好，我是光')

>> [('n', 'i3'), ('h', 'ao3'), ('sp1',), ('uo3',), ('sh', 'ii4'), ('g', 'uang1')]

  • reduce_from_duration Remove files with duration less than least_time seconds. Note that this function does not work in the interactive interpreter because it uses multiprocessing.pool.Pool

    from guang.Voice.reduce import reduce_from_duration

reduced_list = reduce_from_duration(path_list, least_time=2)
print(len(path_list), len(reduced_list))

>> (6889, 6714)

  • Find NOT silence part of a sound file: find_no_silence

    from guang.Voice.reduce import find_no_silence
start, end, arg_start, arg_end = find_no_silence(filename)

  • Download wechat files.

    from guang.wechat.Utils.download import downloads
downloads(nickName='caloi', fileType='mp3', d_t=60)

  • Plots 3D Scatter:

    from guang.Utils.plotly import Scatter3d
from guang.Utils.interesting import Lorenz
trace = Lorenz.Trace()
x,y,z = trace[:,0], trace[:,1], trace[:,2]

fig = Scatter3d()
fig.scatter3d(x,y,z,mode="lines+markers",color_line=z,color_marker=None,marker_size=2)
fig.show()

  • Data dimension reduction:

    cd guang/ML/manifold
python test_digits.py

    python test_s_curve

  • :smiley:

Project links

Key dates

PyPI data
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  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for K.y from gravatar.com K.y

Credits

Author: K.y

License

GPL-v3

Tags

Deep Learning Machine Learning Neural Networks Natural Language Processing Computer Vision

Classifiers

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