readme: ---
  language: is
  datasets:
  - malromur
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  widget:
  - example_title: Malromur sample 1608
    src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/resolve/main/sample1608.flac
  - example_title: Malromur sample 3860
    src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/resolve/main/sample3860.flac
  model-index:
  - name: XLSR Wav2Vec2 Icelandic by Mehrdad Farahani
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Malromur is
        type: malromur
        args: lt
      metrics:
         - name: Test WER
           type: wer
           value: 09.21
          
  ---
  
  # Wav2Vec2-Large-XLSR-53-Icelandic
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Icelandic using [Malromur](https://clarin.is/en/resources/malromur/). When using this model, make sure that your speech input is sampled at 16kHz.
  
  ## Usage
  The model can be used directly (without a language model) as follows:
  
  **Requirements**
  ```bash
  # requirement packages
  !pip install git+https://github.com/huggingface/datasets.git
  !pip install git+https://github.com/huggingface/transformers.git
  !pip install torchaudio
  !pip install librosa
  !pip install jiwer
  !pip install num2words
  ```
  
  **Normalizer**
  ```bash
  
  # num2word packages
  # Original source: https://github.com/savoirfairelinux/num2words
  !mkdir -p ./num2words
  !wget -O num2words/__init__.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/__init__.py
  !wget -O num2words/base.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/base.py
  !wget -O num2words/compat.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/compat.py
  !wget -O num2words/currency.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/currency.py
  !wget -O num2words/lang_EU.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/lang_EU.py
  !wget -O num2words/lang_IS.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/lang_IS.py
  !wget -O num2words/utils.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/utils.py
  
  # Malromur_test selected based on gender and age
  !wget -O malromur_test.csv https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/malromur_test.csv
  
  # Normalizer
  !wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/normalizer.py
  ```
  
  **Prediction**
  ```python
  import librosa
  import torch
  import torchaudio
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  from datasets import load_dataset
  
  import numpy as np
  import re
  import string
  
  import IPython.display as ipd
  
  from normalizer import Normalizer
  
  normalizer = Normalizer(lang="is")
  
  
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      speech_array = speech_array.squeeze().numpy()
      speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
  
      batch["speech"] = speech_array
      return batch
  
  
  def predict(batch):
      features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
      input_values = features.input_values.to(device)
      attention_mask = features.attention_mask.to(device)
  
      with torch.no_grad():
          logits = model(input_values, attention_mask=attention_mask).logits 
          
      pred_ids = torch.argmax(logits, dim=-1)
  
      batch["predicted"] = processor.batch_decode(pred_ids)
      return batch
  
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-icelandic")
  model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-icelandic").to(device)
  
  dataset = load_dataset("csv", data_files={"test": "./malromur_test.csv"})["test"]
  dataset = dataset.map(
      normalizer, 
      fn_kwargs={"do_lastspace_removing": True, "text_key_name": "cleaned_sentence"},
      remove_columns=list(set(dataset.column_names) - set(['cleaned_sentence', 'path']))
  )
  
  dataset = dataset.map(speech_file_to_array_fn)
  result = dataset.map(predict, batched=True, batch_size=8)
  
  max_items = np.random.randint(0, len(result), 20).tolist()
  for i in max_items:
      reference, predicted =  result["cleaned_sentence"][i], result["predicted"][i]
      print("reference:", reference)
      print("predicted:", predicted)
      print('---')
  ```
  
  **Output:**
  ```text
  reference: eða eitthvað annað dýr
  predicted: eða eitthvað annað dýr
  ---
  reference: oddgerður
  predicted: oddgerður
  ---
  reference: eiðný
  predicted: eiðný
  ---
  reference: löndum
  predicted: löndum
  ---
  reference: tileinkaði bróður sínum markið
  predicted: tileinkaði bróður sínum markið
  ---
  reference: þetta er svo mikill hégómi
  predicted: þetta er svo mikill