readme: ---
  language: en
  datasets:
  - librispeech_asr
  tags:
  - speech
  - 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: data2vec-audio-large-960h
    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: 1.89
    - 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: 4.07
  ---
  
  # Data2Vec-Audio-Large-960h
  
  [Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
  
  The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
  make sure that your speech input is also sampled at 16Khz.
  
  [Paper](https://arxiv.org/abs/2202.03555)
  
  Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
  
  **Abstract**
  
  While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
  
  The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec .
  
  # Pre-Training method
  
  ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/data2vec.png)
  
  For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
  
  # Usage
  
  To transcribe audio files the model can be used as a standalone acoustic model as follows:
  
  ```python
   from transformers import Wav2Vec2Processor, Data2VecAudioForCTC
   from datasets import load_dataset
   import torch
   
   # load model and processor
   processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-large-960h")
   model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-large-960h")
       
   # load dummy dataset and read soundfiles
   ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
   
   # tokenize
   input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values  # Batch size 1
   
   # retrieve logits
   logits = model(input_values).logits
   
   # take argmax and decode
   predicted_ids = torch.argmax(logits, dim=-1)
   transcription = processor.batch_decode(predicted_ids)
   ```
   
    ## Evaluation
   
   This code snippet shows how to evaluate **facebook/data2vec-audio-large-960h** on LibriSpeech's "clean" and "other" test data.
   
  ```python
   from transformers import Wav2Vec2Processor, Data2VecAudioForCTC
   from datasets import load_dataset
   import torch
   from jiwer import wer
   
   # load model and processor
   processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-large-960h").to("cuda")
   model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-large-960h")
   
  
  librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
  
  def map_to_pred(batch):
      input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
      with torch.no_grad():
          logits = model(input_values.to("cuda")).logits
  
      predicted_ids = torch.argmax(logits, dim=-1)
      transcription = processor.batch_decode(predicted_ids)
      batch["transcription"] = transcription
      return batch
  
  result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"])
  
  print("WER:", wer(result["text"], result["transcription"]))
  ```
  
  *Result (WER)*:
  
  | "clean" | "other" |
  |---|---|
  | 1.89 | 4.07 |