code: def parse_arguments():
      parser = argparse.ArgumentParser(
          description="A demonstration script "
          "for pyAudioAnalysis library"
      )
      tasks = parser.add_subparsers(
          title="subcommands",
          description="available tasks",
          dest="task",
          metavar="",
      )
  
      dirMp3Wav = tasks.add_parser(
          "dirMp3toWav",
          help="Convert all .mp3 files in a directory "
          "to .wav format",
      )
      dirMp3Wav.add_argument(
          "-i", "--input", required=True, help="Input folder"
      )
      dirMp3Wav.add_argument(
          "-r",
          "--rate",
          type=int,
          choices=[8000, 16000, 32000, 44100],
          required=True,
          help="Samplerate of generated WAV files",
      )
      dirMp3Wav.add_argument(
          "-c",
          "--channels",
          type=int,
          choices=[1, 2],
          required=True,
          help="Audio channels of generated WAV files",
      )
  
      dirWavRes = tasks.add_parser(
          "dirWavResample",
          help="Change samplerate of .wav " "files in a directory",
      )
      dirWavRes.add_argument(
          "-i", "--input", required=True, help="Input folder"
      )
      dirWavRes.add_argument(
          "-r",
          "--rate",
          type=int,
          choices=[8000, 16000, 32000, 44100],
          required=True,
          help="Samplerate of generated WAV files",
      )
      dirWavRes.add_argument(
          "-c",
          "--channels",
          type=int,
          choices=[1, 2],
          required=True,
          help="Audio channels of generated WAV files",
      )
  
      featExt = tasks.add_parser(
          "featureExtractionFile",
          help="Extract audio features from file",
      )
      featExt.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
      featExt.add_argument(
          "-o", "--output", required=True, help="Output file"
      )
      featExt.add_argument(
          "-mw",
          "--mtwin",
          type=float,
          required=True,
          help="Mid-term window size",
      )
      featExt.add_argument(
          "-ms",
          "--mtstep",
          type=float,
          required=True,
          help="Mid-term window step",
      )
      featExt.add_argument(
          "-sw",
          "--stwin",
          type=float,
          default=0.050,
          help="Short-term window size",
      )
      featExt.add_argument(
          "-ss",
          "--ststep",
          type=float,
          default=0.050,
          help="Short-term window step",
      )
  
      beat = tasks.add_parser(
          "beatExtraction",
          help="Compute beat features of an audio file",
      )
      beat.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
      beat.add_argument(
          "--plot", action="store_true", help="Generate plot"
      )
  
      featExtDir = tasks.add_parser(
          "featureExtractionDir",
          help="Extract audio features " "from files in a folder",
      )
      featExtDir.add_argument(
          "-i", "--input", required=True, help="Input directory"
      )
      featExtDir.add_argument(
          "-mw",
          "--mtwin",
          type=float,
          required=True,
          help="Mid-term window size",
      )
      featExtDir.add_argument(
          "-ms",
          "--mtstep",
          type=float,
          required=True,
          help="Mid-term window step",
      )
      featExtDir.add_argument(
          "-sw",
          "--stwin",
          type=float,
          default=0.050,
          help="Short-term window size",
      )
      featExtDir.add_argument(
          "-ss",
          "--ststep",
          type=float,
          default=0.050,
          help="Short-term window step",
      )
  
      featVis = tasks.add_parser("featureVisualization")
      featVis.add_argument(
          "-i", "--input", required=True, help="Input directory"
      )
  
      spectro = tasks.add_parser("fileSpectrogram")
      spectro.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
  
      chroma = tasks.add_parser("fileChromagram")
      chroma.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
  
      trainClass = tasks.add_parser(
          "trainClassifier", help="Train an SVM or KNN classifier"
      )
      trainClass.add_argument(
          "-i",
          "--input",
          nargs="+",
          required=True,
          help="Input directories",
      )
      trainClass.add_argument(
          "--method",
          choices=[
              "svm",
              "svm_rbf",
              "knn",
              "randomforest",
              "gradientboosting",
              "extratrees",
          ],
          required=True,
          help="Classifier type",
      )
      trainClass.add_argument(
          "--beat", action="store_true", help="Compute beat features"
      )
      trainClass.add_argument(
          "-o",
          "--output",
          required=True,
          help="Generated classifier filename",
      )
  
      trainReg = tasks.add_parser("trainRegression")
      trainReg.add_argument(
          "-i", "--input", required=True, help="Input directory"
      )
      trainReg.add_argument(
          "--method",
          choices=["svm", "randomforest", "svm_rbf"],
          required=True,
          help="Classifier type",
      )
      trainReg.add_argument(
          "--beat", action="store_true", help="Compute beat features"
      )
      trainReg.add_argument(
          "-o",
          "--output",
          required=True,
          help="Generated classifier filename",
      )
  
      classFile = tasks.add_parser(
          "classifyFile",
          help="Classify a file using an " "existing classifier",
      )
      classFile.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
      classFile.add_argument(
          "--model",
          choices=[
              "svm",
              "svm_rbf",
              "knn",
              "randomforest",
              "gradientboosting",
              "extratrees",
          ],
          required=True,
          help="Classifier type (svm or knn or"
          " randomforest or "
          "gradientboosting or "
          "extratrees)",
      )
      classFile.add_argument(
          "--classifier", required=True, help="Classifier to use (path)"
      )
  
      trainHMM = tasks.add_parser(
          "trainHMMsegmenter_fromfile",
          help="Train an HMM from file + annotation data",
      )
      trainHMM.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
      trainHMM.add_argument(
          "--ground",
          required=True,
          help="Ground truth path (segments CSV file)",
      )
      trainHMM.add_argument(
          "-o", "--output", required=True, help="HMM model name (path)"
      )
      trainHMM.add_argument(
          "-mw",
          "--mtwin",
          type=float,
          required=True,
          help="Mid-term window size",
      )
      trainHMM.add_argument(
          "-ms",
          "--mtstep",
          type=float,
          required=True,
          help="Mid-term window step",
      )
  
      trainHMMDir = tasks.add_parser(
          "trainHMMsegmenter_fromdir",
          help="Train an HMM from file + annotation "
          "data stored in a directory (batch)",
      )
      trainHMMDir.add_argument(
          "-i", "--input", required=True, help="Input audio folder"
      )
      trainHMMDir.add_argument(
          "-o", "--output", required=True, help="HMM model name (path)"
      )
      trainHMMDir.add_argument(
          "-mw",
          "--mtwin",
          type=float,
          required=True,
          help="Mid-term window size",
      )
      trainHMMDir.add_argument(
          "-ms",
          "--mtstep",
          type=float,
          required=True,
          help="Mid-term window step",
      )
  
      segmentClassifyFile = tasks.add_parser(
          "segmentClassifyFile",
          help="Segmentation - classification "
          "of a WAV file given a trained "
          "SVM or kNN",
      )
      segmentClassifyFile.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
      segmentClassifyFile.add_argument(
          "--model",
          choices=[
              "svm",
              "svm_rbf",
              "knn",
              "randomforest",
              "gradientboosting",
              "extratrees",
          ],
          required=True,
          help="Model type",
      )
      segmentClassifyFile.add_argument(
          "--modelName", required=True, help="Model path"
      )
  
      segmentClassifyFileHMM = tasks.add_parser(
          "segmentClassifyFileHMM",
          help="Segmentation - "
          "classification of a WAV "
          "file given a trained HMM",
      )
      segmentClassifyFileHMM.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
      segmentClassifyFileHMM.add_argument(
          "--hmm", required=True, help="HMM Model to use (path)"
      )
  
      segmentationEvaluation = tasks.add_parser(
          "segmentationEvaluation",
          help="Segmentation - classification "
          "evaluation for a list of WAV "
          "files and CSV ground-truth "
          "stored in a folder",
      )
      segmentationEvaluation.add_argument(
          "-i", "--input", required=True, help="Input audio folder"
      )
      segmentationEvaluation.add_argument(
          "--model",
          choices=["svm", "knn", "hmm"],
          required=True,
          help="Model type",
      )
      segmentationEvaluation.add_argument(
          "--modelName", required=True, help="Model path"
      )
  
      regFile = tasks.add_parser("regressionFile")
      regFile.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
      regFile.add_argument(
          "--model",
          choices=["svm", "svm_rbf", "randomforest"],
          required=True,
          help="Regression type",
      )
      regFile.add_argument(
          "--regression", required=True, help="Regression model to use"
      )
  
      classFolder = tasks.add_parser("classifyFolder")
      classFolder.add_argument(
          "-i", "--input", required=True, help="Input folder"
      )
      classFolder.add_argument(
          "--model",
          choices=[
              "svm",
              "svm_rbf",
              "knn",
              "randomforest",
              "gradientboosting",
              "extratrees",
          ],
          required=True,
          help="Classifier type",
      )
      classFolder.add_argument(
          "--classifier",
          required=True,
          help="Classifier to use (filename)",
      )
      classFolder.add_argument(
          "--details",
          action="store_true",
          help="Plot details (otherwise only "
          "counts per class are shown)",
      )
  
      regFolder = tasks.add_parser("regressionFolder")
      regFolder.add_argument(
          "-i", "--input", required=True, help="Input folder"
      )
      regFolder.add_argument(
          "--model",
          choices=["svm", "knn"],
          required=True,
          help="Classifier type",
      )
      regFolder.add_argument(
          "--regression", required=True, help="Regression model to use"
      )
  
      silrem = tasks.add_parser(
          "silenceRemoval",
          help="Remove silence segments from a recording",
      )
      silrem.add_argument(
          "-i", "--input", required=True, help="input audio file"
      )
      silrem.add_argument(
          "-s",
          "--smoothing",
          type=float,
          default=1.0,
          help="smoothing window size in seconds.",
      )
      silrem.add_argument(
          "-w",
          "--weight",
          type=float,
          default=0.5,
          help="weight factor in (0, 1)",
      )
  
      spkrDir = tasks.add_parser("speakerDiarization")
      spkrDir.add_argument(
          "-i", "--input", required=True, help="Input audio file"
      )
      spkrDir.add_argument(
          "-n",
          "--num",
          type=int,
          required=True,
          help="Number of speakers",
      )
      spkrDir.add_argument(
          "--flsd", action="store_true", help="Enable FLsD method"
      )
  
      speakerDiarizationScriptEval = tasks.add_parser(
          "speakerDiarizationScriptEval",
          help="Train an SVM or KNN " "classifier",
      )
      speakerDiarizationScriptEval.add_argument(
          "-i", "--input", required=True, help="Input directory"
      )
      speakerDiarizationScriptEval.add_argument(
          "--LDAs",
          type=int,
          nargs="+",
          required=True,
          help="List FLsD params",
      )
  
      thumb = tasks.add_parser(
          "thumbnail",
          help="Generate a thumbnailWrapper " "for an audio file",
      )
      thumb.add_argument(
          "-i", "--input", required=True, help="input audio file"
      )
      thumb.add_argument(
          "-s",
          "--size",
          default=10.0,
          type=float,
          help="thumbnailWrapper size in seconds.",
      )
  
      return parser.parse_args()
  
imports: from __future__ import print_function
  import argparse
  import os
  import numpy
  import glob
  import matplotlib.pyplot
  import ShortTermFeatures
  import MidTermFeatures
  import audioTrainTest
  import audioSegmentation
  import audioVisualization
  import audioBasicIO
  import scipy.io.wavfile
  import matplotlib.patches
label_desc: Use image node_id 23 for calling a global function with example usage: parse_arguments() and returns: parser