readme: ---
  language: nl
  datasets:
  - common_voice
  tags:
  - audio
  - automatic-speech-recognition
  - phoneme-recognition
  model-index:
  - name: wav2vec2-base-960h-phoneme-reco-dutch
    results:
    - task:
        name: Automatic Phoneme Recognition
        type: automatic-phoneme-recognition
      dataset:
        name: CommonVoice (clean)
        type: librispeech_asr
        config: clean
        split: test
        args: 
          language: nl
      metrics:
      - name: Test PER
        type: per
        value: 20.83
      - name: Val PER
        type: per
        value: 16.18
  ---
  
  # Model Description
  
  The Wav2vec2 base model [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) fine tuned on phoneme recognition task for the dutch language.
  
  # Usage
  
  To transcribe in phonemes 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("Clementapa/wav2vec2-base-960h-phoneme-reco-dutch")
   model = Wav2Vec2ForCTC.from_pretrained("Clementapa/wav2vec2-base-960h-phoneme-reco-dutch")
       
   # load dummy dataset and read soundfiles
   ds = load_dataset("common_voice", "nl", 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)
   ```