readme: ---
  language: en
  datasets:
  - patrickvonplaten/librispeech_asr_dummy
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - en
  - speech
  ---
  
  # Fine-tuned facebook/wav2vec2-base large model for speech recognition in English
  
  Fine-tuned [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on English using the train and validation splits of [zodata](https://www.kaggle.com/datasets/mohamedk0emad/zodata).
  the dataset has 307912 transcibed voice samples, we used 6158 samples for training and 6036 samples for testing
  and the result for testing with WER accuracy metric is:
  Test WER: 0.340
  
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  This model has been fine-tuned thanks to the GPU credits given by the [kaggle](https://www.kaggle.com/)
  
  
  # 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 tokenizer
   processor = Wav2Vec2Processor.from_pretrained("souzan/zomodel")
   model = Wav2Vec2ForCTC.from_pretrained("souzan/zomodel")
       
   # 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-base-960h** on LibriSpeech's "clean" and "other" test data.
   
  ```python
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import torch
  from jiwer import wer
  
  
  librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
  
  model = Wav2Vec2ForCTC.from_pretrained("souzan/zomodel").to("cuda")
  processor = Wav2Vec2Processor.from_pretrained("souzan/zomodel")
  
  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"]))
  ```
  
  