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48 lines
2.8 KiB
ReStructuredText
Speech Classification
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==================================
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Speech Classification refers to a set of tasks or problems of getting a program to automatically classify input utterance or audio segment into categories,
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such as Speech Command Recognition (multi-class), Voice Activity Detection (binary or multi-class), and Audio Sentiment Classification (typically multi-class), etc.
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**Speech Command Recognition** is the task of classifying an input audio pattern into a discrete set of classes.
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It is a subset of Automatic Speech Recognition, sometimes referred to as Key Word Spotting, in which a model is constantly analyzing speech patterns to detect certain "command" classes.
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Upon detection of these commands, a specific action can be taken by the system.
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It is often the objective of command recognition models to be small and efficient so that they can be deployed onto low-power sensors and remain active for long durations of time.
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**Voice Activity Detection (VAD)** also known as speech activity detection or speech detection, is the task of predicting which parts of input audio contain speech versus background noise.
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It is an essential first step for a variety of speech-based applications including Automatic Speech Recognition.
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It serves to determine which samples to be sent to the model and when to close the microphone.
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The full documentation tree is as follows:
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.. toctree::
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:maxdepth: 8
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models
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datasets
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results
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configs
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Resource and Documentation Guide
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--------------------------------
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Hands-on speech classification tutorial notebooks can be found under ``<NeMo_git_repo>/tutorials/asr/``.
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There are training and offline & online microphone inference tutorials for Speech Command Detection and Voice Activity Detection tasks.
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This and most other tutorials can be run on Google Colab by specifying the link to the notebooks' GitHub pages on Colab.
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If you are looking for information about a particular Speech Classification model or would like to find out more about the model
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architectures available in the `nemo_asr` collection, check out the :doc:`Models <./models>` page.
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Documentation on dataset preprocessing can be found on the :doc:`Datasets <./datasets>` page.
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NeMo includes preprocessing scripts for several common ASR datasets, and this page contains instructions on running
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those scripts.
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It also includes guidance for creating your own NeMo-compatible dataset, if you have your own data.
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Information about how to load model checkpoints (either local files or pretrained ones from NGC), perform inference, as well as a list
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of the checkpoints available on NGC are located on the :doc:`Checkpoints <./results>` page.
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Documentation for configuration files specific to the ``nemo_asr`` models can be found on the
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:doc:`Configuration Files <./configs>` page.
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