Showing 3 open source projects for "data quality cleansing"

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  • 1
    Diffgram

    Diffgram

    Training data (data labeling, annotation, workflow) for all data types

    From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality.
    Downloads: 4 This Week
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  • 2
    JuliusModels

    JuliusModels

    Open source speech models for Julius in English and other languages.

    Open source speech models for Julius speech decoder. Its aim is to give access a wider community of speech recognition enthusiasts to quality models, which they can use in their own projects on different OS platforms (Unix, Windows, etc...) All of the models are based on HTK modelling software and data sets available freely on the Internet.
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    Downloads: 13 This Week
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  • 3
    Speechalyzer

    Speechalyzer

    Process large speech data wrt transcription, labeling and annotation

    Speechalyzer: a tool for the daily work of a 'speech worker' It is optimized to process large speech data sets with respect to transcription, labeling and annotation. It is implemented as a client server based framework in Java and interfaces software for speech recognition, synthesis, speech classification and quality evaluation. The application is mainly the processing of training data for speech recognition and classification models and performing benchmarking tests on speech-to-text, text-to-speech and speech classification software systems.
    Downloads: 0 This Week
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