Language Model Action Set

This example is not available for the Lua programming language.

Training an RNN Acoustic Model

This section contains Python code. For more information about coding in Python, see Getting Started with SAS Viya for Python and SAS Viya: System Programming Guide.

The following code shows how you can perform end-to-end speech recognition in CAS:

               casOut = {"name": "test_audio", "replace": True},
               )

 table                  = "test_audio",
 casOut                 = {"name": "test_dlscore_input", "replace": True},
 audioColumn            = "_audio_",
 copyvars               = ["_path_"],
 frameExtractionOptions = {"frameShift": 10, "frameLength": 25, "dither": 0.0},
 melBanksOptions        = {"nBins": 40},
 mfccOptions            = {"nCeps": 40},
 featureScalingMethod   = "STANDARDIZATION",
 nOutputFrames          = 1500,

               casOut = {"name": "asr", "replace": True},
              )
               casOut = {"name": "pretrained_weights", "replace": True},
              )
               casOut = {"name": "pretrained_weights_attr", "replace": True},
              )
               name = "pretrained_weights",
               attrtable = "pretrained_weights_attr"
               )

       model          = "asr",
       initweights    = "pretrained_weights",
       layerImageType = 'WIDE',
       copyvars       = ['_path_'],
       casOut         = {"name": "test_rnn_scores", "replace": True},
       )

                  casOut = "language_model"
                 )

                  langModelTable = "language_model",
                  blankLabel     = " ",
                  spaceLabel     = "&",
                  copyvars       = ['_path_'],
                  casOut         = "test_results"
                 )

    name  = "test_results.csv",
    replace = True
    )

This example is not available for the R programming language.