readme: ---
  language: en
  datasets:
  - librispeech_asr
  tags:
  - automatic-speech-recognition
  license: apache-2.0
  ---
  
  ## Test model
  
  To test this model run the following code:
  
  ```python
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC
  import torchaudio
  import torch
  
  ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
  
  model = Wav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2_tiny_random_robust")
  
  def load_audio(batch):
      batch["samples"], _ = torchaudio.load(batch["file"])
      return batch
      
  ds = ds.map(load_audio)
  
  input_values = torch.nn.utils.rnn.pad_sequence([torch.tensor(x[0]) for x in ds["samples"][:10]], batch_first=True)
  
  # forward
  logits = model(input_values).logits
  pred_ids = torch.argmax(logits, dim=-1)
  
  # dummy loss
  dummy_labels = pred_ids.clone()
  dummy_labels[dummy_labels == model.config.pad_token_id] = 1  # can't have CTC blank token in label
  dummy_labels = dummy_labels[:, -(dummy_labels.shape[1] // 4):] # make sure labels are shorter to avoid "inf" loss (can still happen though...)
  loss = model(input_values, labels=dummy_labels).loss
  ```
  