readme: ---
  language: fo
  datasets:
  - ravnursson
  tags:
  - audio
  - automatic-speech-recognition
  - faroese
  - xlrs-53-faroese
  - ravnur-project
  - faroe-islands
  license: cc-by-4.0
  widget:
  model-index:
  - name: wav2vec2-large-xlsr-53-faroese-100h
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Ravnursson (Test)
        type: carlosdanielhernandezmena/ravnursson_asr
        split: test
        args: 
          language: fo
      metrics:
      - name: WER
        type: wer
        value: 7.6
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Ravnursson (Dev)
        type: carlosdanielhernandezmena/ravnursson_asr
        split: validation
        args: 
          language: fo
      metrics:
      - name: WER
        type: wer
        value: 5.5
  ---
  
  # wav2vec2-large-xlsr-53-faroese-100h
  
  The "wav2vec2-large-xlsr-53-faroese-100h" is an acoustic model suitable for Automatic Speech Recognition in Faroese. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" with 100 hours of Faroese data released by the Ravnur Project (https://maltokni.fo/en/) from the Faroe Islands.
  
  The specific dataset used to create the model is called "Ravnursson Faroese Speech and Transcripts" and it is available at http://hdl.handle.net/20.500.12537/276.
  	
  The fine-tuning process was perform during November (2022) in the servers of the Language and Voice Lab (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.
  
  # Evaluation
  ```python
  import torch
  from transformers import Wav2Vec2Processor
  from transformers import Wav2Vec2ForCTC
  
  #Load the processor and model.
  MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h"
  processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
  model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)
  
  #Load the dataset
  from datasets import load_dataset, load_metric, Audio
  ds=load_dataset("carlosdanielhernandezmena/ravnursson_asr",split='test')
  
  #Downsample to 16kHz
  ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
  
  #Process the dataset
  def prepare_dataset(batch):
      audio = batch["audio"]
      #Batched output is "un-batched" to ensure mapping is correct
      batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
      with processor.as_target_processor():
          batch["labels"] = processor(batch["normalized_text"]).input_ids
      return batch
  ds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1)
  
  #Define the evaluation metric
  import numpy as np
  wer_metric = load_metric("wer")
  def compute_metrics(pred):
      pred_logits = pred.predictions
      pred_ids = np.argmax(pred_logits, axis=-1)
      pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
      pred_str = processor.batch_decode(pred_ids)
      #We do not want to group tokens when computing the metrics
      label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
      wer = wer_metric.compute(predictions=pred_str, references=label_str)
      return {"wer": wer}
  
  #Do the evaluation (with batch_size=1)
  model = model.to(torch.device("cuda"))
  def map_to_result(batch):
      with torch.no_grad():
          input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0)
          logits = model(input_values).logits
      pred_ids = torch.argmax(logits, dim=-1)
      batch["pred_str"] = processor.batch_decode(pred_ids)[0]
      batch["sentence"] = processor.decode(batch["labels"], group_tokens=False)
      return batch
  results = ds.map(map_to_result,remove_columns=ds.column_names)
  
  #Compute the overall WER now.
  print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["sentence"])))
  
  ```
  **Test Result**: 0.076
  
  # BibTeX entry and citation info
  *When publishing results based on these models please refer to:*
  ```bibtex
  @misc{mena2022xlrs53faroese,
        title={Acoustic Model in Faroese: wav2vec2-large-xlsr-53-faroese-100h.}, 
        author={Hernandez Mena, Carlos Daniel},
        year={2022},
        url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h},
  }
  ```
  
  # Acknowledgements
  
  We want to thank to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.
  
  Special thanks to Annika Simonsen and to The Ravnur Project for making their
  "Basic Language Resource Kit"(BLARK 1.0) publicly available through the research paper "Creating a Basic Language Resource Kit for Faroese" https://aclanthology.org/2022.lrec-1.495.pdf
  