readme: ---
  language: en
  datasets:
  - librispeech_asr
  tags:
  - audio
  - automatic-speech-recognition
  - hf-asr-leaderboard
  license: apache-2.0
  widget:
  - example_title: Librispeech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
  - example_title: Librispeech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
  model-index:
  - name: patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: LibriSpeech (clean)
        type: librispeech_asr
        config: clean
        split: test
        args: 
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 1.84
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: LibriSpeech (other)
        type: librispeech_asr
        config: other
        split: test
        args: 
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 3.71
  ---
  
  # Wav2Vec2-Base-960h + 4-gram
  
  This model is identical to [Facebook's Wav2Vec2-Large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), but is 
  augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official ngrams](https://www.openslr.org/11) is used.
   
   ## Evaluation
   
   This code snippet shows how to evaluate **patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram** on LibriSpeech's "clean" and "other" test data.
   
  ```python
  from datasets import load_dataset
  from transformers import AutoModelForCTC, AutoProcessor
  import torch
  from jiwer import wer
  
  model_id = "patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram"
  
  librispeech_eval = load_dataset("librispeech_asr", "other", split="test")
  
  model = AutoModelForCTC.from_pretrained(model_id).to("cuda")
  processor = AutoProcessor.from_pretrained(model_id)
  
  def map_to_pred(batch):
      inputs = processor(batch["audio"]["array"], sampling_rate=16_000, return_tensors="pt")
  
      inputs = {k: v.to("cuda") for k,v in inputs.items()}
  
      with torch.no_grad():
          logits = model(**inputs).logits
  
      transcription = processor.batch_decode(logits.cpu().numpy()).text[0]
      batch["transcription"] = transcription
      return batch
  
  result = librispeech_eval.map(map_to_pred, remove_columns=["audio"])
  
  print(wer(result["text"], result["transcription"]))
  ```
  
  *Result (WER)*:
  
  | "clean" | "other" |
  |---|---|
  | 1.84 | 3.71 |