class_docstr: Transcriber: speech to text pipeline
docstr: ```
  basic wrapper speech transcription
  
  Args:
    model_name(str): Helsinki-NLP model
    device(str): device to use (e.g., 'cuda', 'cpu')
  ```
docstr_tok: ["``","`","basic","wrapper","speech","transcription","Args",":","model_name","(","str",")",":","Helsinki-NLP","model","device","(","str",")",":","device","to","use","(","e.g.",",","'cuda","'",",","'cpu","'",")","``","`"]
code: def __init__(
      self, model_name="facebook/wav2vec2-base-960h", device=None
  ):
      """
      ```
      basic wrapper speech transcription
  
      Args:
        model_name(str): Helsinki-NLP model
        device(str): device to use (e.g., 'cuda', 'cpu')
      ```
      """
      super().__init__(device=device, quantize=False)
      # if not SOUNDFILE:
      # raise ImportError("SoundFile library not installed or libsndfile not found: pip install soundfile")
      if not LIBROSA:
          raise ImportError(
              "librosa library must be installed: pip install librosa. Conda users may also have to install ffmpeg: conda install -c conda-forge ffmpeg"
          )
  
      # load model and processor
      self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(
          self.torch_device
      )
      self.processor = Wav2Vec2Processor.from_pretrained(model_name)
  
file_path: ktrain/ktrain/text/speech/core.py
imports: from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  from ...torch_base import TorchBase
label_desc: Use image node_id 1 to create a new Transcriber object from inherited base classes: TorchBase with example: obj = Transcriber(model_name, device)