readme: ---
  language: en
  datasets:
  - LIUM/tedlium
  tags:
  - speech
  license: apache-2.0
  ---
  # Wav2Vec2-Large-Tedlium
  The Wav2Vec2 large model fine-tuned on the TEDLIUM corpus.
  
  The model is initialised with Facebook's [Wav2Vec2 large LV-60k](https://huggingface.co/facebook/wav2vec2-large-lv60) checkpoint pre-trained on 60,000h of audiobooks from the LibriVox project. It is fine-tuned on 452h of TED talks from the [TEDLIUM](https://huggingface.co/datasets/LIUM/tedlium) corpus (Release 3). When using the model, make sure that your speech input is sampled at 16Khz.
  
  The model achieves a word error rate (WER) of 8.4% on the dev set and 8.2% on the test set. [Training logs](https://wandb.ai/sanchit-gandhi/tedlium/runs/10c85yc4?workspace=user-sanchit-gandhi) document the training and evaluation progress over 50k steps of fine-tuning.
  
  See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how this model was fine-tuned.
  
  
  # 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("sanchit-gandhi/wav2vec2-large-tedlium")
   model = Wav2Vec2ForCTC.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium")
       
   # load dummy dataset
   ds = load_dataset("sanchit-gandhi/tedlium_dummy", split="validation")
   
   # process audio inputs
   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)
   print("Target: ", ds["text"][0])
   print("Transcription: ", transcription[0])
   ```
   
  ## Evaluation
   
  This code snippet shows how to evaluate **Wav2Vec2-Large-Tedlium** on the TEDLIUM test data.
   
  ```python
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import torch
  from jiwer import wer
  
  tedlium_eval = load_dataset("LIUM/tedlium", "release3", split="test")
  model = Wav2Vec2ForCTC.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium").to("cuda")
  processor = Wav2Vec2Processor.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium")
  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 = tedlium_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
  print("WER:", wer(result["text"], result["transcription"]))
  ```