| Name | iman JSON |
| Version |
1.0.22
JSON |
| download |
| home_page | |
| Summary | Python package for daily Tasks |
| upload_time | 2024-01-28 07:57:10 |
| maintainer | |
| docs_url | None |
| author | Iman Sarraf |
| requires_python | |
| license | |
| keywords |
python
iman
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
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_pattern , 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``
31-PX(fname) ``check existance of file``
32-RC(Arr , size=1) ``Random Choice``
33-onehot(data, nb_classes)
34-exe(pyfile) ``need pyinstaller``
35-FWL(wavfolder , sr) ``Get Folder Audio Length``
36-norm(vector) ``vector/magnitude(vector)``
37-delete(pattern)
38-rename(fname , fout)
39-separate(pattern,folout) ``separate vocal from music``
40-dll(fname) ``create a pyd file from py file``
41-get_hard_serial()
42-mute_mic() ``on and off microphone``
from iman import Audio
======================
1-Read(filename,sr,start_from,dur,mono,ffmpeg_path,ffprobe_path) ``Read wav alaw and mp3 and others``
2-Resample(data , fs, sr)
3-Write(filename, data ,fs)
4-frame(y)
5-split(y)
6-ReadT(filename, sr , mono=True) ``Read and resample wav file with torchaudio``
7-VAD(y,top_db=40, frame_length=200, hop_length=80)
8-compress(fname_pattern , sr=16000 , ext='mp3' , mono=True ,ffmpeg_path='c:\\ffmpeg.exe' , ofolder=None, worker=4)
9-clip_value(wav) ``return clipping percentage in audio file``
10-WriteS(filename, data ,fs) ``Convert to Sterio``
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)
8-DER(ref_list , res_list , file_dur=-1 , sr=8000) ``Detection Error Rate``
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)
3-links(url , filter_text=None)
4-imgs(url , filter_text=None)
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_fea,mspec,log_energy = 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_fea,log_energy = mfcc.LS.Get(wav,sample_rate,le=False) ``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)
2-Add_Reverb( data , rir)
3-Add_NoiseT(data , noise , snr) ``(torchaudio)``
4-Add_ReverbT( data , rir) ``(torchaudio)``
5-mp3(fname , fout,sr_out,ratio,ffmpeg_path='c:\\ffmpeg.exe')
6-speed(fname,fout,ratio,ffmpeg_path='c:\\ffmpeg.exe')
7-volume(fname ,fout,ratio,ffmpeg_path='c:\\ffmpeg.exe')
from iman.[sad_torch_mfcc | sad_tf] import *
===============================================================================
seg = Segmenter(batch_size, vad_type=['sad'|'vad'] , sr=8000 , model_path="c:\\sad_model_pytorch.pth" , tq=1,ffmpeg_path='c:\\ffmpeg.exe',complete_output=False , device='cuda',input_type='file') ``TORCH``
seg = Segmenter(batch_size, vad_type=['sad'|'vad'] , sr=16000 , model_path="c:\\keras_speech_music_noise_cnn.hdf5",gender_path="c:\\keras_male_female_cnn.hdf5",ffmpeg_path='c:\\ffmpeg.exe',detect_gender=False,complete_output=False,device='cuda',input_type='file') ``TensorFlow``
isig,wav,mfcc = seg(fname) ``mfcc output Just in torch model``
nmfcc = filter_fea(isig , mfcc , sr , max_time) ``Just in torch model``
mfcc = MVN(mfcc) ``Just in torch model``
isig = filter_output(isig , max_silence ,ignore_small_speech_segments , max_speech_len ,split_speech_bigger_than) ``Do when complete_output=False``
seg2aud(isig , filename)
seg2json(isig)
seg2Gender_Info(isig)
seg2Info(isig)
wav_speech , wav_noise = filter_sig(isig , wav , sr) ``Get Speech and Noise Parts of file - Do when complete_output=False``
from sad_tf.segmentero import Segmenter ``to use onnx models - need to install onnxruntime``
from iman.sad_torch_mfcc_speaker import *
================================================
seg = Segmenter(batch_size, vad_type=['sad'|'vad'] , sr=8000 , model_path="c:\\sad_model_pytorch.pth" , max_time=120(sec) , tq=1,ffmpeg_path='c:\\ffmpeg.exe', device='cuda' , pad=False) ``TORCH - max_time in second to split fea output``
mfcc, len(sec) = seg(fname) ``mfcc pad to max_time length if pad=True``
from iman.sad_tf_mlp_speaker import *
================================================
seg = Segmenter(batch_size, vad_type=['sad'|'vad'] , sr=8000 , model_path="sad_tf_mlp.h5" , max_time=120(sec) , tq=1,ffmpeg_path='c:\\ffmpeg.exe', device='cuda' , pad=False) ``Tensorflow (small mlp model) - max_time in second to split fea output``
mfcc, len(sec) = seg(fname) ``mfcc pad to max_time length if pad=True``
from iman import Report ``Tensorboard Writer``
==================================================
r=Report.rep(log_dir=None)
r.WS(_type , _name , value , itr) ``Add_scalar``
r.WT(_type , _name , _str , itr) ``Add_text``
r.WG(pytorch_model , example_input) ``Add_graph``
r.WI(_type , _name , images , itr) ``Add_image``
from iman import par
========================
if (__name__ == '__main__'):
res = par.par(files , func , worker=4 , args=[]) ``def func(fname , _args): ...``
from iman import Image
=========================
Image.convert(fname_pattern ,ext ='jpg',ofolder=None , w=-1 , h=-1,level=100, worker=4,ffmpeg_path='c:\\ffmpeg.exe')
Image.resize(fname_pattern ,ext ='jpg',ofolder=None , w=2 , h=2, worker=4,ffmpeg_path='c:\\ffmpeg.exe') ``resize to 1/h and 1/w``
from iman import Boors
==========================
Boors.get(sahm) ``get sahm info``
from iman import Text
=====================
norm = Text.normal("c:\\Replace_List.txt")
norm.rep(str)
norm.from_file(filename ,file_out=None)
from iman.num2fa import words
=============================
words(number)
from iman import examples
==========================
examples.items ``get items in examples folder``
examples.help(topic)
from iman import Rar
====================
1-rar(fname , out="" , rar_path=r"C:\\Program Files\\WinRAR\\winrar.exe")
2-zip(fname , out="" , rar_path=r"C:\\Program Files\\WinRAR\\winrar.exe")
3-unrar(fname , out="" , rar_path=r"C:\\Program Files\\WinRAR\\winrar.exe")
4-unzip(fname , out="" , rar_path=r"C:\\Program Files\\WinRAR\\winrar.exe")
from iman import Enhance
=========================
1-Enhance.Dereverb(pattern , out_fol , sr = 16000, batchsize=16 , device="cuda" ,model_path=r"C:\\UVR-DeEcho-DeReverb.pth")
2-Enhance.Denoise(pattern , out_fol , sr = 16000, batchsize=16 , device="cuda" ,model_path=r"C:\UVR-DeNoise-Lite.pth")
from iman.tf import *
=====================
1-flops(model) ``get flops of tf model``
2-param(model) ``return parameter number of tf model``
3-paramp(model) ``return parameter number of tf model and print model layers``
4-gpu() ``return True if available``
5-gpun() ``return number of gpus``
6-limit() ``Tf model only allocate as much GPU memory based on runtime allocations``
from iman.torch import *
========================
1-param(model) ``return parameter number and trainable number of torch model``
2-paramp(model) ``return parameter number of torch model and print model layers``
3-layers(model) ``return layers of torch model``
4-gpu() ``return True if available``
5-gpun() ``return number of gpus``
Raw data
{
"_id": null,
"home_page": "",
"name": "iman",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "python,iman",
"author": "Iman Sarraf",
"author_email": "imansarraf@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/a1/58/d24ea23e088b1e9e8cb7635127c7eeb65036cbd9453dc4ed3c299ee6e4cd/iman-1.0.22.tar.gz",
"platform": null,
"description": "from iman import * \n==================\n\n1-plt\n\n2-now() ``get time``\n\n3-F ``format floating point``\n\n4-D ``format int number``\n\n5-Write_List(MyList,Filename)\n\n6-Write_Dic(MyDic,Filename)\n\n7-Read(Filename) ``read txt file``\n\n8-Read_Lines(Filename) ``read txt file line by line and return list``\n\n9-Write(_str,Filename)\n\n10-gf(pattern) ``Get files in a directory``\n\n11-gfa(directory_pattern , ext=\"*.*\") ``Get Files in a Directory and SubDirectories``\n\n12-ReadE(Filename) ``Read Excel files``\n\n13-PM(dir) ``creat directory``\n\n14-PB(fname) ``get basename``\n\n15-PN(fname) ``get file name``\n\n16-PE(fname) ``get ext``\n\n17-PD(fname) ``get directory``\n\n18-PS(fname) ``get size``\n\n19-PJ(segments) ``Join Path``\n\n20-clear() ``clear cmd``\n\n21-os\n\n22-np\n\n23-RI(start_int , end_int , count=1) ``random int``\n\n24-RF(start_float , end_float , count=1) ``random float``\n\n25-RS(Arr) ``shuffle``\n\n26-LJ(job_file_name)\n\n27-SJ(value , job_file_name)\n\n28-LN(np_file_name)\n\n29-SN(arr , np_file_name)\n\n30-cmd(command , redirect=True) ``Run command in CMD``\n\n31-PX(fname) ``check existance of file``\n\n32-RC(Arr , size=1) ``Random Choice``\n\n33-onehot(data, nb_classes)\n\n34-exe(pyfile) ``need pyinstaller``\n\n35-FWL(wavfolder , sr) ``Get Folder Audio Length``\n\n36-norm(vector) ``vector/magnitude(vector)``\n\n37-delete(pattern) \n\n38-rename(fname , fout) \n\n39-separate(pattern,folout) ``separate vocal from music``\n\n40-dll(fname) ``create a pyd file from py file``\n\n41-get_hard_serial()\n\n42-mute_mic() ``on and off microphone``\n\nfrom iman import Audio \n======================\n1-Read(filename,sr,start_from,dur,mono,ffmpeg_path,ffprobe_path) ``Read wav alaw and mp3 and others``\n\n2-Resample(data , fs, sr)\n\n3-Write(filename, data ,fs)\n\n4-frame(y)\n\n5-split(y)\n\n6-ReadT(filename, sr , mono=True) ``Read and resample wav file with torchaudio``\n\n7-VAD(y,top_db=40, frame_length=200, hop_length=80)\n\n8-compress(fname_pattern , sr=16000 , ext='mp3' , mono=True ,ffmpeg_path='c:\\\\ffmpeg.exe' , ofolder=None, worker=4)\n\n9-clip_value(wav) ``return clipping percentage in audio file``\n\n10-WriteS(filename, data ,fs) ``Convert to Sterio``\n\nfrom iman import info \n=====================\n\n1-get() info about cpu and gpu ``need torch``\n\n2-cpu() ``get cpu percentage usage``\n\n3-gpu() ``get gpu memory usage``\n\n4-memory() ``get ram usage GB``\n\n5-plot(fname=\"log.txt\" , delay=1)\n\n\nfrom iman import metrics \n========================\n1-EER(lab,score)\n\n2-cosine_distance(v1,v2)\n\n3-roc(lab,score)\n\n4-wer(ref, hyp)\n\n5-cer(ref, hyp)\n\n6-wer_list(ref_list , hyp_list)\n\n7-cer_list(ref_list , hyp_list)\n\n8-DER(ref_list , res_list , file_dur=-1 , sr=8000) ``Detection Error Rate``\n\nfrom iman import tsne \n=====================\n\n1-plot(fea , label)\n\nfrom iman import xvector \n========================\n1-xvec,lda_xvec,gender = get(filename , model(model_path , model_name , model_speaker_num))\n\n\nfrom iman import web \n====================\n1-change_wallpaper()\n\n2-dl(url)\n\n3-links(url , filter_text=None)\n\n4-imgs(url , filter_text=None)\n\nfrom iman import matlab \n=======================\n1-np2mat(param , mat_file_name)\n\n2-dic2mat(param , mat_file_name)\n\n3-mat2dic (mat_file_name)\n\nfrom iman import Features\n=========================\n1- mfcc_fea,mspec,log_energy = mfcc.SB.Get(wav,sample_rate) ``Compute MFCC with speechbrain - input must read with torchaudio``\n\n2-mfcc.SB.Normal(MFCC) ``Mean Var Normalization Utt with speechbrain``\n\n3- mfcc_fea,log_energy = mfcc.LS.Get(wav,sample_rate,le=False) ``Compute MFCC with Librosa - input is numpy array``\n\n4-mfcc.LS.Normal(MFCC , win_len=150) ``Mean Var Normalization Local 150 left and 150 right``\n\nfrom iman import AUG \n====================\n1-Add_Noise(data , noise , snr) \n\n2-Add_Reverb( data , rir) \n\n3-Add_NoiseT(data , noise , snr) ``(torchaudio)``\n\n4-Add_ReverbT( data , rir) ``(torchaudio)``\n\n5-mp3(fname , fout,sr_out,ratio,ffmpeg_path='c:\\\\ffmpeg.exe')\n\n6-speed(fname,fout,ratio,ffmpeg_path='c:\\\\ffmpeg.exe')\n\n7-volume(fname ,fout,ratio,ffmpeg_path='c:\\\\ffmpeg.exe')\n\nfrom iman.[sad_torch_mfcc | sad_tf] import *\n===============================================================================\nseg = Segmenter(batch_size, vad_type=['sad'|'vad'] , sr=8000 , model_path=\"c:\\\\sad_model_pytorch.pth\" , tq=1,ffmpeg_path='c:\\\\ffmpeg.exe',complete_output=False , device='cuda',input_type='file') ``TORCH``\n\nseg = Segmenter(batch_size, vad_type=['sad'|'vad'] , sr=16000 , model_path=\"c:\\\\keras_speech_music_noise_cnn.hdf5\",gender_path=\"c:\\\\keras_male_female_cnn.hdf5\",ffmpeg_path='c:\\\\ffmpeg.exe',detect_gender=False,complete_output=False,device='cuda',input_type='file') ``TensorFlow``\n\nisig,wav,mfcc = seg(fname) ``mfcc output Just in torch model`` \n\nnmfcc = filter_fea(isig , mfcc , sr , max_time) ``Just in torch model``\n\nmfcc = MVN(mfcc) ``Just in torch model`` \n\nisig = filter_output(isig , max_silence ,ignore_small_speech_segments , max_speech_len ,split_speech_bigger_than) ``Do when complete_output=False``\n\nseg2aud(isig , filename)\n \nseg2json(isig) \n\nseg2Gender_Info(isig) \n\nseg2Info(isig) \n\nwav_speech , wav_noise = filter_sig(isig , wav , sr) ``Get Speech and Noise Parts of file - Do when complete_output=False``\n\nfrom sad_tf.segmentero import Segmenter ``to use onnx models - need to install onnxruntime``\n\nfrom iman.sad_torch_mfcc_speaker import *\n================================================\nseg = Segmenter(batch_size, vad_type=['sad'|'vad'] , sr=8000 , model_path=\"c:\\\\sad_model_pytorch.pth\" , max_time=120(sec) , tq=1,ffmpeg_path='c:\\\\ffmpeg.exe', device='cuda' , pad=False) ``TORCH - max_time in second to split fea output``\nmfcc, len(sec) = seg(fname) ``mfcc pad to max_time length if pad=True``\n\nfrom iman.sad_tf_mlp_speaker import *\n================================================\nseg = Segmenter(batch_size, vad_type=['sad'|'vad'] , sr=8000 , model_path=\"sad_tf_mlp.h5\" , max_time=120(sec) , tq=1,ffmpeg_path='c:\\\\ffmpeg.exe', device='cuda' , pad=False) ``Tensorflow (small mlp model) - max_time in second to split fea output``\nmfcc, len(sec) = seg(fname) ``mfcc pad to max_time length if pad=True``\n\nfrom iman import Report ``Tensorboard Writer``\n==================================================\nr=Report.rep(log_dir=None)\n\nr.WS(_type , _name , value , itr) ``Add_scalar``\n\nr.WT(_type , _name , _str , itr) ``Add_text``\n\nr.WG(pytorch_model , example_input) ``Add_graph``\n\nr.WI(_type , _name , images , itr) ``Add_image``\n\nfrom iman import par\n========================\nif (__name__ == '__main__'): \n \nres = par.par(files , func , worker=4 , args=[]) ``def func(fname , _args): ...``\n\nfrom iman import Image\n=========================\nImage.convert(fname_pattern ,ext ='jpg',ofolder=None , w=-1 , h=-1,level=100, worker=4,ffmpeg_path='c:\\\\ffmpeg.exe')\n\nImage.resize(fname_pattern ,ext ='jpg',ofolder=None , w=2 , h=2, worker=4,ffmpeg_path='c:\\\\ffmpeg.exe') ``resize to 1/h and 1/w``\n\nfrom iman import Boors\n==========================\nBoors.get(sahm) ``get sahm info``\n\nfrom iman import Text\n=====================\nnorm = Text.normal(\"c:\\\\Replace_List.txt\")\n\nnorm.rep(str)\n\nnorm.from_file(filename ,file_out=None)\n\nfrom iman.num2fa import words\n=============================\nwords(number)\n\nfrom iman import examples\n==========================\nexamples.items ``get items in examples folder``\n\nexamples.help(topic)\n\nfrom iman import Rar \n====================\n1-rar(fname , out=\"\" , rar_path=r\"C:\\\\Program Files\\\\WinRAR\\\\winrar.exe\") \n\n2-zip(fname , out=\"\" , rar_path=r\"C:\\\\Program Files\\\\WinRAR\\\\winrar.exe\") \n\n3-unrar(fname , out=\"\" , rar_path=r\"C:\\\\Program Files\\\\WinRAR\\\\winrar.exe\") \n\n4-unzip(fname , out=\"\" , rar_path=r\"C:\\\\Program Files\\\\WinRAR\\\\winrar.exe\") \n\nfrom iman import Enhance\n=========================\n1-Enhance.Dereverb(pattern , out_fol , sr = 16000, batchsize=16 , device=\"cuda\" ,model_path=r\"C:\\\\UVR-DeEcho-DeReverb.pth\")\n\n2-Enhance.Denoise(pattern , out_fol , sr = 16000, batchsize=16 , device=\"cuda\" ,model_path=r\"C:\\UVR-DeNoise-Lite.pth\")\n\nfrom iman.tf import *\n=====================\n1-flops(model) ``get flops of tf model``\n\n2-param(model) ``return parameter number of tf model``\n\n3-paramp(model) ``return parameter number of tf model and print model layers``\n\n4-gpu() ``return True if available``\n\n5-gpun() ``return number of gpus``\n\n6-limit() ``Tf model only allocate as much GPU memory based on runtime allocations``\n\nfrom iman.torch import *\n========================\n1-param(model) ``return parameter number and trainable number of torch model``\n\n2-paramp(model) ``return parameter number of torch model and print model layers``\n\n3-layers(model) ``return layers of torch model``\n\n4-gpu() ``return True if available``\n\n5-gpun() ``return number of gpus``\n\n\n",
"bugtrack_url": null,
"license": "",
"summary": "Python package for daily Tasks",
"version": "1.0.22",
"project_urls": null,
"split_keywords": [
"python",
"iman"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0fe155fb797078a4c46344cc69560dee9318c67e95a1e7a53610a7fe415da435",
"md5": "5e8518f1763e3a3100ad0242e7af1213",
"sha256": "b1bf1cc1e509bbb631f4b58c6dcdb95e5fc62f52def233017a4f1daca82c75ed"
},
"downloads": -1,
"filename": "iman-1.0.22-py3-none-any.whl",
"has_sig": false,
"md5_digest": "5e8518f1763e3a3100ad0242e7af1213",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 156970,
"upload_time": "2024-01-28T07:57:08",
"upload_time_iso_8601": "2024-01-28T07:57:08.007867Z",
"url": "https://files.pythonhosted.org/packages/0f/e1/55fb797078a4c46344cc69560dee9318c67e95a1e7a53610a7fe415da435/iman-1.0.22-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a158d24ea23e088b1e9e8cb7635127c7eeb65036cbd9453dc4ed3c299ee6e4cd",
"md5": "c70ef95c59c1a95e6bdf536c7fe2897b",
"sha256": "c07b0f02a277c86cda4bfc245f1e8d6bd5ed7020d932bbbf72187013671fc5b9"
},
"downloads": -1,
"filename": "iman-1.0.22.tar.gz",
"has_sig": false,
"md5_digest": "c70ef95c59c1a95e6bdf536c7fe2897b",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 115486,
"upload_time": "2024-01-28T07:57:10",
"upload_time_iso_8601": "2024-01-28T07:57:10.528248Z",
"url": "https://files.pythonhosted.org/packages/a1/58/d24ea23e088b1e9e8cb7635127c7eeb65036cbd9453dc4ed3c299ee6e4cd/iman-1.0.22.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-28 07:57:10",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "iman"
}
