# Apply an hmm for segmentation-classificaiton to a WAV file
segmentclassifyFileWrapperHMM(args.input, args.hmm)
elif args.task == "segmentationEvaluation":
# Evaluate segmentation-classification for a list of WAV files
# (and ground truth CSVs) stored in a folder
segmentationEvaluation(args.input, args.modelName, args.model)
elif args.task == "regressionFile":
# Apply a regression model to an audio signal stored in a WAV file
regressionFileWrapper(args.input, args.model, args.regression)
elif args.task == "classifyFolder":
# Classify every WAV file in a given path
classifyFolderWrapper(args.input, args.model, args.classifier,
args.details)
elif args.task == "regressionFolder":
# Apply a regression model on every WAV file in a given path
regressionFolderWrapper(args.input, args.model, args.regression)
elif args.task == "silenceRemoval":
# Detect non-silent segments in a WAV file and
# output to seperate WAV files
silenceRemovalWrapper(args.input, args.smoothing, args.weight)
elif args.task == "speakerDiarization":
# Perform speaker diarization on a WAV file
speakerDiarizationWrapper(args.input, args.num, args.flsd)
elif args.task == "speakerDiarizationScriptEval":
# Evaluate speaker diarization given a folder that contains
# WAV files and .segment (Groundtruth files)
aS.speakerDiarizationEvaluateScript(args.input, args.LDAs)
elif args.task == "thumbnail":
# Audio thumbnailing
thumbnailWrapper(args.input, args.size)
# Apply an hmm for segmentation-classificaiton to a WAV file
segmentclassifyFileWrapperHMM(args.input, args.hmm)
elif args.task == "segmentationEvaluation":
# Evaluate segmentation-classification for a list of WAV files
# (and ground truth CSVs) stored in a folder
segmentationEvaluation(args.input, args.modelName, args.model)
elif args.task == "regressionFile":
# Apply a regression model to an audio signal stored in a WAV file
regressionFileWrapper(args.input, args.model, args.regression)
elif args.task == "classifyFolder":
# Classify every WAV file in a given path
classifyFolderWrapper(args.input, args.model, args.classifier,
args.details)
elif args.task == "regressionFolder":
# Apply a regression model on every WAV file in a given path
regressionFolderWrapper(args.input, args.model, args.regression)
elif args.task == "silenceRemoval":
# Detect non-silent segments in a WAV file and
# output to seperate WAV files
silenceRemovalWrapper(args.input, args.smoothing, args.weight)
elif args.task == "speakerDiarization":
# Perform speaker diarization on a WAV file
speakerDiarizationWrapper(args.input, args.num, args.flsd)
elif args.task == "speakerDiarizationScriptEval":
# Evaluate speaker diarization given a folder that contains
# WAV files and .segment (Groundtruth files)
aS.speakerDiarizationEvaluateScript(args.input, args.LDAs)
elif args.task == "thumbnail":
# Audio thumbnailing
thumbnailWrapper(args.input, args.size)