code: def test_large_model_pt_with_lm(self):
      dataset = load_dataset("Narsil/asr_dummy")
      filename = dataset["test"][3]["file"]
  
      speech_recognizer = pipeline(
          task="automatic-speech-recognition",
          model="patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm",
          framework="pt",
      )
      self.assertEqual(speech_recognizer.type, "ctc_with_lm")
  
      output = speech_recognizer(filename)
      self.assertEqual(
          output,
          {
              "text": "y en las ramas medio sumergidas revoloteaban algunos pájaros de quimérico y legendario plumaje"
          },
      )
  
      # Override back to pure CTC
      speech_recognizer.type = "ctc"
      output = speech_recognizer(filename)
      # plumajre != plumaje
      self.assertEqual(
          output,
          {
              "text": (
                  "y en las ramas medio sumergidas revoloteaban algunos pájaros de quimérico y legendario plumajre"
              )
          },
      )
  
      speech_recognizer.type = "ctc_with_lm"
      # Simple test with CTC with LM, chunking + timestamps
      output = speech_recognizer(
          filename, chunk_length_s=2.0, return_timestamps="word"
      )
      self.assertEqual(
          output,
          {
              "text": (
                  "y en las ramas medio sumergidas revoloteaban algunos pájaros de quimérico y legendario plumajcri"
              ),
              "chunks": [
                  {"text": "y", "timestamp": (0.52, 0.54)},
                  {"text": "en", "timestamp": (0.6, 0.68)},
                  {"text": "las", "timestamp": (0.74, 0.84)},
                  {"text": "ramas", "timestamp": (0.94, 1.24)},
                  {"text": "medio", "timestamp": (1.32, 1.52)},
                  {"text": "sumergidas", "timestamp": (1.56, 2.22)},
                  {"text": "revoloteaban", "timestamp": (2.36, 3.0)},
                  {"text": "algunos", "timestamp": (3.06, 3.38)},
                  {"text": "pájaros", "timestamp": (3.46, 3.86)},
                  {"text": "de", "timestamp": (3.92, 4.0)},
                  {"text": "quimérico", "timestamp": (4.08, 4.6)},
                  {"text": "y", "timestamp": (4.66, 4.68)},
                  {"text": "legendario", "timestamp": (4.74, 5.26)},
                  {"text": "plumajcri", "timestamp": (5.34, 5.74)},
              ],
          },
      )
  
imports: import unittest
  import numpy
  import pytest
  from datasets import load_dataset
  from huggingface_hub import snapshot_download
  from transformers import MODEL_FOR_CTC_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, AutoFeatureExtractor, AutoTokenizer, Speech2TextForConditionalGeneration, Wav2Vec2ForCTC, WhisperForConditionalGeneration, WhisperProcessor
  from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
  from transformers.pipelines.audio_utils import chunk_bytes_iter
  from transformers.pipelines.automatic_speech_recognition import chunk_iter
  from transformers.testing_utils import is_torch_available, nested_simplify, require_pyctcdecode, require_tf, require_torch, require_torchaudio, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 6 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.test_large_model_pt_with_lm() without return types