iman 0.0.33

Python package for daily Tasks

from iman import *

1-plt

2-now() get time

3-F format floating point

4-D format int number

5-Write_List(MyList,Filename)

6-Write_Dic(MyDic,Filename)

7-Read(Filename) read txt file

8-Read_Lines(Filename) read txt file line by line and return list

9-Write(_str,Filename)

10-gf(pattern) Get files in a directory

11-gfa(directory , ext=”.”) Get Files in a Directory and SubDirectories

12-ReadE(Filename) Read Excel files

13-PM(dir) creat directory

14-PB(fname) get basename

15-PN(fname) get file name

16-PE(fname) get ext

17-PD(fname) get directory

18-PS(fname) get size

19-PJ(segments) Join Path

20-clear() clear cmd

21-os

22-np

23-RI(start_int , end_int , count=1) random int

24-RF(start_float , end_float , count=1) random float

25-RS(Arr) shuffle

26-LJ(job_file_name)

27-SJ(value , job_file_name)

28-LN(np_file_name)

29-SN(arr , np_file_name)

30-cmd(command , redirect=True) Run command in CMD

from iman import Audio

1-Read(filename,sr) Read wav alaw and mp3 (Just MONO)

2-Resample(data , fs, sr)

3-Read_Alaw(filename)

4-ReadMp3(filename,sr,mono=True)

5-Write(filename, data ,fs)

6-frame(y)

7-split(y)

8-ReadT(filename) Read wav file with torchaudio return data and sr

9-VAD(y,top_db=40, frame_length=200, hop_length=80)

from iman import info

1-get() info about cpu and gpu need torch

2-cpu() get cpu percentage usage

3-gpu() get gpu memory usage

4-memory() get ram usage GB

5-plot(fname=”log.txt” , delay=1)

from iman import metrics

1-EER(lab,score)

2-cosine_distance(v1,v2)

3-roc(lab,score)

4-wer(ref, hyp)

5-cer(ref, hyp)

6-wer_list(ref_list , hyp_list)

7-cer_list(ref_list , hyp_list)

from iman import tsne

1-plot(fea , label)

from iman import xvector

1-xvec,lda_xvec,gender = get(filename , model(model_path , model_name , model_speaker_num))

from iman import web

1-change_wallpaper()

2-dl(url)

from iman import matlab

1-np2mat(param , mat_file_name)

2-dic2mat(param , mat_file_name)

3-mat2dic (mat_file_name)

from iman import Features

1-mfcc.SB.Get(wav,sample_rate) Compute MFCC with speechbrain - input must read with torchaudio

2-mfcc.SB.Normal(MFCC) Mean Var Normalization Utt with speechbrain

3-mfcc.LS.Get(wav,sample_rate) Compute MFCC with Librosa - input is numpy array

4-mfcc.LS.Normal(MFCC , win_len=150) Mean Var Normalization Local 150 left and 150 right

from iman import AUG

1-Add_Noise(data , noise , snr) Don't need sox

x=AUG.aug(sox_path) Use this Just in WINDOWS

2-x.mp3(fname , sr, fout,ratio)

3-x.speed(fname,fout,ratio)

4-x.volume(fname ,fout,ratio)