# 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)