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# Standard Tasks

This table describes the standard tasks that are already defined in the tasks configuration file. You can run any of these tasks straight out of the box.

Task | Description
--- | ---
`AfpAddTrackStream` | Adds a new audio track to an audio fingerprint database, receiving the audio data as a stream, and converting to AFP features before indexing.
`AfpAddTrackWav` | Adds a new audio track to an audio fingerprint database, reading the data from an audio file, and converting to AFP features before indexing.
`AfpDatabaseInfo` | Returns a list of all tracks that are currently stored in the specified database.
`AfpDatabaseOptimize` | Optimizes the internal indexing of the specified database. This task permanently removes files that have been tagged for deletion using the `AfpRemoveTrack` task, and optimizes lookup functions for newly added tracks.
`AfpMatchStream` | Receives audio data as a binary stream, and searches it for any indexed audio sections.
`AfpMatchWav` | Reads in data from an audio file, and searches it for any indexed audio sections.
`AfpRemoveTrack` | Removes specified tracks from an audio fingerprint database.
`AfptAddTrackStream` | Performs the same task as `AfpAddTrackStream`, but uses a template database (`fptdb`), which improves robustness to audio mismatches at the cost of scaleability.
`AfptAddTrackWav` | Performs the same task as `AfpAddTrackWav`, but uses a template database (`fptdb`), which improves robustness to audio mismatches at the cost of scaleability.
`AfptDatabaseInfo` | Performs the same task as `AfpDatabaseInfo`, but uses a template database (`fptdb`), which improves robustness to audio mismatches at the cost of scaleability.
`AfptMatchStream` | Performs the same task as `AfpMatchStream`, but uses template-based matching as opposed to landmarks, which improves robustness to audio mismatches at the cost of scaleability.
`AfptMatchWav` | Performs the same task as `AfpMatchWav`, but uses template-based matching as opposed to landmarks, which improves robustness to audio mismatches at the cost of scaleability.
`AfptRemoveTrack` | Performs the same task as `AfpRemoveTrack`, but uses a template database (`fptdb`), which improves robustness to audio mismatches at the cost of scaleability.
`AmTrain` | Presents training audio and transcription data to the acoustic model training process, and creates accumulator files that are used to produce a final adapted acoustic model.
`AmTrainFinal` | Produces the adapted acoustic model, given a set of accumulator files created by the `AmTrain` task.
`AudioAnalysis` | Runs all the audio preprocessing tasks that are supported by the `audiopreproc` module in a single task.
`AudioSecurity` | Detects and labels segments of audio that contain alarms, screams, breaking glass, or gunshots.
`ClippingDetection` | Analyzes an audio file for the issue of audio clipping.
`ClusterSpeech` | Clusters wide-band speech into speaker segments.
`ClusterSpeechTel` | Clusters telephony speech into speaker segments.
`ClusterSpeechToTextTel` | Clusters two speakers in a phone call, and uses the resulting speaker clusters to improve speech-to-text performance slightly by using speaker-sided acoustic normalization. Any telephony artifacts such as dial tones or DTMF tones are included, interspersed with the recognized words.
`CombineFMD` | Combines several phoneme track files, which can then be used for phrase search.
`DataObfuscation` | Prepares training data with any sensitive or classified information concealed.
`DialToneIdentification` | Detects and identifies dial tones in audio.
`LangIDBndLif` | Reads in language ID features from file, and determines boundaries in the feature sequence where the language changes. Returns the language identification results between boundaries.
`LangIdBndStream` | Receives audio data as a binary stream, converts the audio into language ID features, and determines boundaries where the language changes. Returns the language identification results between boundaries.
`LangIdBndWav` | Reads in data from an audio file, converts it into language ID features, and determines boundaries where the language changes. Returns the language identification results between boundaries.
`LangIdCumLif` | Reads in language ID features from file, and returns the running language identification score at periodic intervals (that is, the score for all the input data from the start to the current point).
`LangIdCumStream` | Receives audio data as a binary stream, and converts it into language ID features. Returns the running language identification score at periodic intervals (that is, the score for all the input data from the start to the current point).
`LangIdCumWav` | Reads in data from an audio file, and converts it into language ID features. Returns the running language identification score at periodic intervals (that is, the score for all the input data from the start to the current point).
`LangIdFeature` | Converts audio files in the relevant language into language identification feature (.lif) files, which are required for training classifiers.
`LangIdOptimize` | Optimizes the balance between language classifiers. After training, some classifiers might be stronger than others because of properties of the training material and the languages in question. The optimization process weights the language models so that weaker languages have increased accuracy, without compromising accuracy for stronger language models. This process improves consistent performance.
`LangIdSegLif` | Reads in language ID features from file, processes the data in fixed-sized chunks, and returns the language identification results for each chunk.
`LangIdSegStream` | Receives audio data as a binary stream, and converts it into language ID features. Processes the data in fixed-sized chunks, and returns the language identification results for each chunk.
`LangIdSegWav` | Reads in data from an audio file and converts it into language ID features. Processes the data in fixed-sized chunks and returns the language identification results for each chunk.
`LangIdTrain` | Reads in a set of language ID feature files created from audio representing a single language (using the `LangIdFeature` task), and uses this data to train a new language classifier.
`LanguageModelBuild` | Builds a new language model from a set of text files.
`LMListVocab` | Lists the most common words in the specified language model.
`LMLookUp` | Verifies whether a specified word is present in the vocabulary of a particular language model, and, if it is present, how frequently it occurs.
`LMPerplexity` | Analyzes the perplexity of a sample text file, when given a specific language model.
`PunctuateCtm` | Adds punctuation to any `.ctm` file
`Scorer` | Scores the recognition transcript (such as that generated by the `SpeechToText` task), when given a reference transcript file.
`SearchFMD` | Searches a phoneme track file for one or more specified phrases.
`SegmentText` | Inserts whitespace between words in a text file (for languages that do not separate words with whitespace).
`SegmentWav` | Attempts to segment audio into sections by speaker even if no trained speakers exist in the system.
`SidPackage` | Packages a set of trained speaker models into a single speaker classification file. Deprecated: The `SidPackage` task is deprecated for HPE IDOL Speech Server version 11.2.0. HPE recommends that you use the `SpkIdDevelFinal` task instead. This task is still available for existing implementations, but it might be incompatible with new functionality. The parameter might be deleted in future.
`SidTrain` | Takes an audio file and a base model (by default, the USM model), and writes a speaker training parameter (SPT) file. Deprecated: The `SidTrain` task is deprecated for HPE IDOL Speech Server version 11.2.0. HPE recommends that you use the `SpkIdTrain` task instead. This task is still available for existing implementations, but it might be incompatible with new functionality. The parameter might be deleted in future.
`SidTrainFinal` | Takes one or more SPT files and the base model, and produces a new speaker model. Deprecated: The `SidTrainFinal` task is deprecated for HPE IDOL Speech Server version 11.2.0. HPE recommends that you use the `SpkIdTrain` task instead. This task is still available for existing implementations, but it might be incompatible with new functionality. The parameter might be deleted in future.
`SNRCalculation` | Calculates SNR levels across an audio file.
`SpeechSilClassification` | Segments an audio file into sections of speech, non-speech, and music.
`SpkIdDevel` | Processes speaker ID feature files to generate scores for tuning model thresholds.
`SpkIdDevelFinal` | Estimates the thresholds for a set of speaker templates.
`SpkIdDevelStream` | Creates or updates a development (`.atd`) file for an audio stream.
`SpkIdDevelWav` | Creates or updates a development (`.atd`) file for an audio file.
`SpkIdEvalStream` | Analyzes an audio stream to identify any sections where the trained speakers are present.
`SpkIdEvalWav` | Analyzes an audio file to identify any sections where the trained speakers are present.
`SpkIdFeature` | Creates a speaker ID feature file.
`SpkIdSetAdd` | Takes one or more audio template files, and adds them to an audio template set file.
`SpkIdSetDelete` | Removes a template from an audio template set file.
`SpkIdSetEditThresh` | Modifies the threshold of a single template in an audio template set file.
`SpkIdSetInfo` | Retrieves information on an audio template set file.
`SpkIdTmpEditThresh` | Modifies the threshold of a single template.
`SpkIdTmpInfo` | Retrieves information on an audio template file.
`SpkIdTrain` | Uses one or more feature files to train a speaker template.
`SpkIdTrainStream` | Takes an audio stream containing speech data from the speaker to be trained, and creates a new speaker template file.
`SpkIdTrainWav` | Takes a single audio file containing speech data from the speaker to be trained, and creates a new speaker template file.
`StreamSidOptimize` | Receives sample audio data for a trained (or untrained) speaker from a binary stream file, and updates statistics used to calculate speaker thresholds across the whole speaker classifier set.
`StreamSidTrain` | Receives sample audio data for a specific speaker from a binary stream, and creates a speaker model to represent the speaker.
`StreamSpeakerId` | Segments an audio stream by speaker and identifies known speakers, unknown speakers, and periods of non-speech within the audio. To run the `StreamSpeakerID` task, speakers must be trained to HPE IDOL Speech Server. Deprecated: The `StreamSpeakerId` task is deprecated for HPE IDOL Speech Server version 11.2.0. HPE recommends that you use the `spkIdEvalStream` task instead. This task is still available for existing implementations, but it might be incompatible with new functionality. The parameter might be deleted in future.
`StreamToText` | Converts live audio into a text transcript.
`StreamToTextMusicFilter` | Converts live audio into a text transcript and categorizes the audio so that you can remove any sections consisting of music or noise.
`StreamToTextMusicFilterPunct` | Converts live audio into a text transcript that includes simple punctuation, and categorizes the audio so that you can remove any sections consisting of music or noise.
`StreamToTextPunct` | Converts live audio into a text transcript that includes simple punctuation.
`TelWavToText` | Transcribes a telephony audio file, including dial tones and DTMF dial tones.
`TelWavToTextPunct` | Transcribes a telephony audio file, including dial tones and DTMF dial tones, and punctuation.
`TextNorm` | Takes a raw text transcription file and produces a normalized form (by removing punctuation, rewriting numbers as words, altering word cases, and so on).
`TranscriptAlign` | If a transcript is available for an audio recording, the transcript alignment function can place time locations for each word in the transcript. You can use this function to align subtitles with audio or video files.
`TranscriptCheck` | Checks how well a text transcript matches the audio data, and identifies large missing or erroneous sections.
`WavPhraseSearch` | Searches for a specified phrase or phrases in an audio file.
`WavSidOptimize` | Reads in sample audio for a trained (or untrained) speaker from an audio file, and updates statistics used to calculate speaker thresholds across the whole speaker classifier set. Deprecated: The `WavSidOptimize` task is deprecated for HPE IDOL Speech Server version 11.2.0. HPE recommends that you use the `SpkIdDevelWav` task instead. This task is still available for existing implementations, but it might be incompatible with new functionality. The parameter might be deleted in future.
`WavSidTrain` | Reads in sample audio for a specific speaker from an audio file, and creates a speaker model to represent the speaker. Deprecated: The `WavSidTrain` task is deprecated for HPE IDOL Speech Server version 11.2.0. HPE recommends that you use the `SpkIdTrainWav` task instead. This task is still available for existing implementations, but it might be incompatible with new functionality. The parameter might be deleted in future.
`WavSpeakerId` | Segments audio by speaker and identifies known speakers, unknown speakers, and periods of non-speech within the audio file. To run the `WavSpeakerID` task, speakers must be trained to HPE IDOL Speech Server. Deprecated: The `WavSpeakerId` task is deprecated for HPE IDOL Speech Server version 11.2.0. HPE recommends that you use the `SpkIdEvalWav` task instead. This task is still available for existing implementations, but it might be incompatible with new functionality. The parameter might be deleted in future.
`WavToFMD` | Creates a phoneme time track (.fmd) file from a single audio file.
`WavToPlh` | Reads data from an audio file and produces an audio feature (.plh) file, such as those used in the acoustic model adaptation process (the `AmTrain` task).
`WavToText` | Converts an audio file into a text transcript. Note: To use `WavToText` to submit audio data as a binary data block for speech-to-text, submit the task data without specifying a `.wav` file.
`WavToTextPunct` | Converts an audio file into a text transcript with simple sentence-forming punctuation (for example, full stops and initial capital letters).

For details about each task, including the required action and configuration parameters, see the HPE IDOL Speech Server Reference.

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