readme: ---
  language: en
  datasets:
  - librispeech_asr
  tags:
  - speech
  
  license: apache-2.0
  ---
  
  # Wav2Vec2-Large-960h
  
  [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
  
  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/2006.11477)
  
  Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
  
  **Abstract**
  
  We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
  
  The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
  
  
  # Usage
  
  To transcribe audio files the model can be used as a standalone acoustic model as follows:
  
  ```python
   from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
   from datasets import load_dataset
   import torch
   
   # load model and processor
   processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
   model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-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/wav2vec2-large-960h** on LibriSpeech's "clean" and "other" test data.
   
  ```python
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import soundfile as sf
  import torch
  from jiwer import wer
  
  
  librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
  
  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h").to("cuda")
  processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
  
  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=["speech"])
  
  print("WER:", wer(result["text"], result["transcription"]))
  ```
  
  *Result (WER)*:
  
  | "clean" | "other" |
  |---|---|
  | 2.8 | 6.3 |