iman 2.0.6

Python package for daily Tasks

Overview

iman is a comprehensive Python package offering a wide array of utilities for audio processing, file manipulation, machine learning, system operations, web utilities, and more. It provides tools for tasks such as audio feature extraction, voice activity detection, file I/O, system monitoring, and integration with frameworks like PyTorch and TensorFlow. The package is organized into multiple submodules, each designed for specific functionalities, as detailed below.

Installation

Install iman via pip:

pip install iman

Ensure dependencies like numpy, torch, tensorflow, speechbrain, librosa, matplotlib, pandas, and external tools like ffmpeg, ffprobe, and WinRAR are installed. Some functions require pre-trained models or specific paths (e.g., model files, ffmpeg_path).

Usage

Below are examples of key functionalities from the iman package. For detailed function signatures and parameters, refer to the sections below or use the built-in help system:

Example: Audio Processing

from iman import Audio

# Read a WAV file
data, sr = Audio.Read("audio.wav", sr=16000, start_from=0, dur=None, mono=True, ffmpeg_path="c:\\ffmpeg.exe", ffprobe_path="c:\\ffprobe.exe")

# Resample and write audio
resampled = Audio.Resample(data, fs=sr, sr=8000)
Audio.Write("output.wav", resampled, fs=8000)

Example: File Operations

from iman import *

# Get files matching a pattern
files = gf("*.txt")

# Write a dictionary to a file
my_dict = {"key1": "value1", "key2": "value2"}
Write_Dic(my_dict, "output.txt")

Example: VAD with Segmenter

from iman.sad_torch_mfcc import Segmenter

seg = Segmenter(batch_size=32, vad_type="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")
isig, wav, mfcc = seg("audio.wav")

Modules and Functions

The iman package is organized into several submodules, each with specific functions. Below is a complete list of modules and their functions as provided.

iman

• plt: Matplotlib plotting library.

• now(): Get current time.

• F: Format floating-point number.

• D: Format integer number.

• Write_List(MyList, Filename): Write a list to a text file.

• Write_Dic(MyDic, Filename): Write a dictionary to a text file.

• Read(Filename): Read a text file.

• Read_Lines(Filename): Read a text file line by line and return a list.

• Write(_str, Filename): Write a string to a text file.

• gf(pattern): Get files in a directory matching a pattern.

• gfa(directory_pattern, ext="*.*"): Get files in a directory and subdirectories.

• ReadE(Filename): Read Excel files.

• PM(dir): Create a directory.

• PB(fname): Get basename of a file.

• PN(fname): Get filename without path.

• PE(fname): Get file extension.

• PD(fname): Get directory of a file.

• PS(fname): Get file size.

• PJ(segments): Join path segments.

• clear(): Clear command-line interface.

• os: Python os module.

• np: NumPy module.

• RI(start_int, end_int, count=1): Generate random integers.

• RF(start_float, end_float, count=1): Generate random floats.

• RS(Arr): Shuffle an array.

• LJ(job_file_name): Load job file (details not specified).

• SJ(value, job_file_name): Save job file (details not specified).

• LN(np_file_name): Load NumPy file (details not specified).

• SN(arr, np_file_name): Save NumPy array to file.

• cmd(command, redirect=True): Run a command in CMD.

• PX(fname): Check existence of a file.

• RC(Arr, size=1): Random choice from an array.

• onehot(data, nb_classes): Convert data to one-hot encoding.

• exe(pyfile): Convert Python file to executable (requires PyInstaller).

• FWL(wavfolder, sr): Get total audio length in a folder.

• norm(vector): Normalize a vector (vector/magnitude(vector)).

• delete(pattern): Delete files matching a pattern.

• rename(fname, fout): Rename a file.

• separate(pattern, folout): Separate vocal from music.

• dll(fname): Create a .pyd file from a Python file.

• get_hard_serial(): Get hardware serial number.

• mute_mic(): Toggle microphone on/off.

• PA(fname): Get absolute path of a file.

iman.Audio

• Read(filename, sr, start_from, dur, mono, ffmpeg_path, ffprobe_path): Read WAV, ALAW, MP3, and other audio formats.

• Resample(data, fs, sr): Resample audio data.

• Write(filename, data, fs): Write audio data to a file.

• frame(y): Frame audio data (details not specified).

• split(y): Split audio data (details not specified).

• ReadT(filename, sr, mono=True): Read and resample WAV file with torchaudio.

• VAD(y, top_db=40, frame_length=200, hop_length=80): Voice activity detection.

• compress(fname_pattern, sr=16000, ext='mp3', mono=True, ffmpeg_path='c:\\ffmpeg.exe', ofolder=None, worker=4): Compress audio files.

• clip_value(wav): Return clipping percentage in an audio file.

• WriteS(filename, data, fs): Convert and write audio to stereo.

iman.info

• get(): Get information about CPU and GPU (requires torch).

• cpu(): Get CPU percentage usage.

• gpu(): Get GPU memory usage.

• memory(): Get RAM usage in GB.

• plot(fname="log.txt", delay=1): Plot system metrics from a log file.

iman.metrics

• EER(lab, score): Compute Equal Error Rate.

• cosine_distance(v1, v2): Compute cosine distance between two vectors.

• roc(lab, score): Compute ROC curve.

• wer(ref, hyp): Compute Word Error Rate.

• cer(ref, hyp): Compute Character Error Rate.

• wer_list(ref_list, hyp_list): Compute WER for lists.

• cer_list(ref_list, hyp_list): Compute CER for lists.

• DER(ref_list, res_list, file_dur=-1, sr=8000): Compute Detection Error Rate.

iman.tsne

• plot(fea, label): Plot t-SNE visualization of features.

iman.xvector

• xvec, lda_xvec, gender = get(filename, model(model_path, model_name, model_speaker_num)): Extract x-vectors for speaker recognition.

iman.web

• change_wallpaper(): Change system wallpaper.

• dl(url): Download a file from a URL.

• links(url, filter_text=None): Extract links from a URL.

• imgs(url, filter_text=None): Extract images from a URL.

iman.matlab

• np2mat(param, mat_file_name): Convert NumPy array to MATLAB file.

• dic2mat(param, mat_file_name): Convert dictionary to MATLAB file.

• mat2dic(mat_file_name): Convert MATLAB file to dictionary.

iman.Features

• mfcc_fea, mspec, log_energy = mfcc.SB.Get(wav, sample_rate): Compute MFCC with SpeechBrain (input must be read with torchaudio).

• mfcc.SB.Normal(MFCC): Mean-variance normalization of MFCC with SpeechBrain.

• mfcc_fea, log_energy = mfcc.LS.Get(wav, sample_rate, le=False): Compute MFCC with Librosa (input is NumPy array).

• mfcc.LS.Normal(MFCC, win_len=150): Mean-variance normalization (local, 150 frames left and right).

iman.AUG

• Add_Noise(data, noise, snr): Add noise to audio data.

• Add_Reverb(data, rir): Add reverberation to audio data.

• Add_NoiseT(data, noise, snr): Add noise using torchaudio.

• Add_ReverbT(data, rir): Add reverberation using torchaudio.

• mp3(fname, fout, sr_out, ratio, ffmpeg_path='c:\\ffmpeg.exe'): Convert to MP3.

• speed(fname, fout, ratio, ffmpeg_path='c:\\ffmpeg.exe'): Change audio speed.

• volume(fname, fout, ratio, ffmpeg_path='c:\\ffmpeg.exe'): Adjust audio volume.

iman.sad_torch_mfcc | iman.sad_tf

• Initializer (PyTorch):

  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')

• Initializer (TensorFlow):

  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')

• isig, wav, mfcc = seg(fname): Process audio file (MFCC output only in PyTorch model).

• nmfcc = filter_fea(isig, mfcc, sr, max_time): Filter features (PyTorch only).

• mfcc = MVN(mfcc): Mean-variance normalization (PyTorch only).

• isig = filter_output(isig, max_silence, ignore_small_speech_segments, max_speech_len, split_speech_bigger_than): Filter output when complete_output=False.

• seg2aud(isig, filename): Convert segments to audio.

• seg2json(isig): Convert segments to JSON.

• seg2Gender_Info(isig): Extract gender information from segments.

• seg2Info(isig): Extract segment information.

• wav_speech, wav_noise = filter_sig(isig, wav, sr): Get speech and noise parts (when complete_output=False).

• sad_tf.segmentero:

  from sad_tf.segmentero import Segmenter  # Use ONNX models (requires onnxruntime)

iman.sad_torch_mfcc_speaker

• Initializer:

  seg = Segmenter(batch_size, vad_type=['sad'|'vad'], sr=8000, model_path="c:\\sad_model_pytorch.pth", max_time=120, tq=1, ffmpeg_path='c:\\ffmpeg.exe', device='cuda', pad=False)

• mfcc, len(sec) = seg(fname): Process audio file, MFCC padded to max_time if pad=True.

iman.sad_tf_mlp_speaker

• Initializer:

  seg = Segmenter(batch_size, vad_type=['sad'|'vad'], sr=8000, model_path="sad_tf_mlp.h5", max_time=120, tq=1, ffmpeg_path='c:\\ffmpeg.exe', device='cuda', pad=False)

• mfcc, len(sec) = seg(fname): Process audio file, MFCC padded to max_time if pad=True.

iman.Report

• Initializer:

  r = Report.rep(log_dir=None)

• WS(_type, _name, value, itr): Add scalar to TensorBoard.

• WT(_type, _name, _str, itr): Add text to TensorBoard.

• WG(pytorch_model, example_input): Add graph to TensorBoard.

• WI(_type, _name, images, itr): Add image to TensorBoard.

iman.par

• Parallel Processing:

  if __name__ == '__main__':
      res = par.par(files, func, worker=4, args=[])  # func defined as: def func(fname, _args): ...

iman.Image

• Image.convert(fname_pattern, ext='jpg', ofolder=None, w=-1, h=-1, level=100, worker=4, ffmpeg_path='c:\\ffmpeg.exe'): Convert images to specified format.

• Image.resize(fname_pattern, ext='jpg', ofolder=None, w=2, h=2, worker=4, ffmpeg_path='c:\\ffmpeg.exe'): Resize images to 1/w and 1/h.

iman.Boors

• Boors.get(sahm): Get stock information.

iman.Text

• Initializer:

  norm = Text.normal("c:\\Replace_List.txt")

• norm.rep(str): Replace text based on normalization rules.

• norm.from_file(filename, file_out=None): Normalize text from a file.

iman.num2fa

• words(number): Convert number to Persian words.

iman.Rar

• rar(fname, out="", rar_path=r"C:\\Program Files\\WinRAR\\winrar.exe"): Create RAR archive.

• zip(fname, out="", rar_path=r"C:\\Program Files\\WinRAR\\winrar.exe"): Create ZIP archive.

• unrar(fname, out="", rar_path=r"C:\\Program Files\\WinRAR\\winrar.exe"): Extract RAR archive.

• unzip(fname, out="", rar_path=r"C:\\Program Files\\WinRAR\\winrar.exe"): Extract ZIP archive.

iman.Enhance

• Enhance.Dereverb(pattern, out_fol, sr=16000, batchsize=16, device="cuda", model_path=r"C:\\UVR-DeEcho-DeReverb.pth"): Dereverberate audio files.

• Enhance.Denoise(pattern, out_fol, sr=16000, batchsize=16, device="cuda", model_path=r"C:\\UVR-DeNoise-Lite.pth"): Denoise audio files.

iman.tf

• flops(model): Get FLOPs of a TensorFlow model.

• param(model): Get parameter count of a TensorFlow model.

• paramp(model): Get parameter count and print model layers.

• gpu(): Return True if GPU is available.

• gpun(): Return number of GPUs.

• limit(): Limit GPU memory allocation for TensorFlow models.

iman.torch

• param(model): Get parameter and trainable count of a PyTorch model.

• paramp(model): Get parameter count and print model layers.

• layers(model): Get layers of a PyTorch model.

• gpu(): Return True if GPU is available.

• gpun(): Return number of GPUs.

iman.yt

• dl(url): Download a YouTube video.

• list_formats(url): List available formats for a YouTube link.

iman.svad

• segments, wav = svad(filename, sampling_rate=16000, min_speech_duration_ms=250, max_speech_duration_s=float('inf'), min_silence_duration_ms=100): Run fast speech activity detection and return speech segments.

iman.word

• documnet = Documnet(): Create New Documnet

• adds(document , top_margin = 4 , bottom_margin=4 , start_page =1): Add Section with footer page number

• addpic(document , image_path , width , align = "c"): Add picture to document

• addp(document , text , rtl = True , align="J" , font = "B Nazanin" , size = 12 , bold = False , color = RGBColor(0, 0, 0) , line_spacing = 0.8, right_indent= 0 , left_indent= 0 , space_after=0, bullet = False ): Add paragraph to documnet

• get_docx_page_count_win32(docx_filename): Count Page Number

• convert_to_persian_numbers(text): Convert numbers in text to persian format

• docx_to_pdf(docx_path , pdf_path): Convet docx to pdf

• merge_pdfs(pdf_files, output_filename): merge pdf files

Dependencies

The iman package requires the following:

• Python Packages: numpy, torch, tensorflow, speechbrain, librosa, matplotlib, pandas, onnxruntime (for ONNX models).

• External Tools: ffmpeg, ffprobe, WinRAR (for RAR/ZIP operations).

• Optional: Pre-trained models (e.g., for VAD, x-vector, dereverberation) specified in function arguments.

Check the package’s requirements.txt for specific versions.

Contributing

Contributions are welcome! Submit bug reports, feature requests, or pull requests via the project’s GitHub repository (if available). Follow contribution guidelines and include tests for new features.

License

iman is licensed under the MIT License (assumed). See the LICENSE file for details.

Contact

For support, contact the maintainers via the project’s GitHub page or email (if provided).

Note

Some functions require external tools (e.g., ffmpeg, WinRAR) or pre-trained models. Ensure these are configured correctly. Paths like c:\\ffmpeg.exe are Windows-specific; adjust for other operating systems.