class_docstr: Prepares Floats or Doubles from a processed audio file(s)
  This component is needed to process audio.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``NONE``               ``AUDIO``
  ====================== ======================
  
  Parameters
  ----------
  inputCol
      Input column name
  outputCol
      Output column name
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from pyspark.ml import Pipeline
  >>> data = spark.read.option("inferSchema", value = True)                    .parquet("./tmp/librispeech_asr_dummy_clean_audio_array_parquet")                    .select($"float_array".cast("array<float>").as("audio_content"))
  >>> audioAssembler = AudioAssembler().setInputCol("audio_content").setOutputCol("audio_assembler")
  >>> result = audioAssembler.transform(data)
  >>> result.select("audio_assembler").show()
  >>> result.select("audio_assembler").printSchema()
  root
   |-- audio_content: array (nullable = true)
   |    |-- element: float (containsNull = true)
class_docstr_tok: ["Prepares","Floats","or","Doubles","from","a","processed","audio","file","(","s",")","This","component","is","needed","to","process","audio",".","======================","======================","Input","Annotation","types","Output","Annotation","type","======================","======================","``","NONE","``","``","AUDIO","``","======================","======================","Parameters","--","--","--","--","--","inputCol","Input","column","name","outputCol","Output","column","name","Examples","--","--","--","--",">",">",">","import","sparknlp",">",">",">","from","sparknlp.base","import","*",">",">",">","from","pyspark.ml","import","Pipeline",">",">",">","data","=","spark.read.option","(","``","inferSchema","''",",","value","=","True",")",".parquet","(","``",".\/tmp\/librispeech_asr_dummy_clean_audio_array_parquet","''",")",".select","(","$","''","float_array","''",".cast","(","``","array","<","float",">","''",")",".as","(","``","audio_content","''",")",")",">",">",">","audioAssembler","=","AudioAssembler","(",")",".setInputCol","(","``","audio_content","''",")",".setOutputCol","(","``","audio_assembler","''",")",">",">",">","result","=","audioAssembler.transform","(","data",")",">",">",">","result.select","(","``","audio_assembler","''",")",".show","(",")",">",">",">","result.select","(","``","audio_assembler","''",")",".printSchema","(",")","root","|","--","audio_content",":","array","(","nullable","=","true",")","|","|","--","element",":","float","(","containsNull","=","true",")"]
docstr: Sets output column name.
  
  Parameters
  ----------
  value : str
      Name of the Output Column
docstr_tok: ["Sets","output","column","name",".","Parameters","--","--","--","--","--","value",":","str","Name","of","the","Output","Column"]
code: def setOutputCol(self, value):
      """Sets output column name.
  
      Parameters
      ----------
      value : str
          Name of the Output Column
      """
      return self._set(outputCol=value)
  
file_path: spark-nlp/python/sparknlp/base/audio_assembler.py
imports: from pyspark import keyword_only
  from pyspark.ml.param import TypeConverters, Params, Param
  from sparknlp.common import AnnotatorType
  from sparknlp.internal import AnnotatorTransformer
label_desc: Use image node_id 4 for calling the AudioAssembler obj's underlying member method code with example usage: obj.setOutputCol(value) and returns: self