class_docstr: Wav2Vec2 Model with a language modeling head on top for Connectionist Temporal
  Classification (CTC). Wav2Vec2 was proposed in wav2vec 2.0: A Framework for
  Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou,
  Abdelrahman Mohamed, Michael Auli.
  
  The annotator takes audio files and transcribes it as text. The audio needs to be
  provided pre-processed an array of floats.
  
  Note that this annotator is currently not supported on Apple Silicon processors such
  as the M1. This is due to the processor not supporting instructions for XLA.
  
  Pretrained models can be loaded with ``pretrained`` of the companion object:
  
  >>> speechToText = Wav2Vec2ForCTC.pretrained() \
  ...     .setInputCols(["audio_assembler"]) \
  ...     .setOutputCol("text")
  
  
  The default model is ``"asr_wav2vec2_base_960h"``, if no name is provided.
  
  For available pretrained models please see the
  `Models Hub <https://sparknlp.org/models>`__.
  
  To see which models are compatible and how to import them see
  https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended
  examples, see
  `Wav2Vec2ForCTCTestSpec <https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/audio/Wav2Vec2ForCTCTestSpec.scala>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``AUDIO``              ``DOCUMENT``
  ====================== ======================
  
  Parameters
  ----------
  
  batchSize
      Size of each batch, by default 2
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> audioAssembler = AudioAssembler() \
  ...     .setInputCol("audio_content") \
  ...     .setOutputCol("audio_assembler")
  >>> speechToText = Wav2Vec2ForCTC \
  ...     .pretrained() \
  ...     .setInputCols(["audio_assembler"]) \
  ...     .setOutputCol("text")
  >>> pipeline = Pipeline().setStages([audioAssembler, speechToText])
  >>> processedAudioFloats = spark.createDataFrame([[rawFloats]]).toDF("audio_content")
  >>> result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats)
  >>> result.select("text.result").show(truncate = False)
  +------------------------------------------------------------------------------------------+
  |result                                                                                    |
  +------------------------------------------------------------------------------------------+
  |[MISTER QUILTER IS THE APOSTLE OF THE MIDLE CLASES AND WE ARE GLAD TO WELCOME HIS GOSPEL ]|
  +------------------------------------------------------------------------------------------+
code: def __init__(
      self,
      classname="com.johnsnowlabs.nlp.annotators.audio.Wav2Vec2ForCTC",
      java_model=None,
  ):
      super(Wav2Vec2ForCTC, self).__init__(
          classname=classname, java_model=java_model
      )
      self._setDefault(batchSize=2)
  
file_path: spark-nlp/python/sparknlp/annotator/audio/wav2vec2_for_ctc.py
imports: from sparknlp.common import *
label_desc: Use image node_id 2 to create a new Wav2Vec2ForCTC object from inherited base classes: AnnotatorModel, HasBatchedAnnotateAudio, HasAudioFeatureProperties, HasEngine with example: obj = Wav2Vec2ForCTC(classname, java_model)