readme: ---
  language: et
  datasets:
  - common_voice
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  widget:
  - label: Common Voice sample 1123
    src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-estonian/resolve/main/sample1123.flac
  - label: Common Voice sample 910
    src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-estonian/resolve/main/sample910.flac
  model-index:
  - name: XLSR Wav2Vec2 Estonian by Mehrdad Farahani
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice et
        type: common_voice
        args: et
      metrics:
         - name: Test WER
           type: wer
           value: 33.93
  ---
  
  
  # Wav2Vec2-Large-XLSR-53-Estonian
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Estonian using [Common Voice](https://huggingface.co/datasets/common_voice). 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
  ```
  
  
  **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
  
  chars_to_ignore = [
      ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
      "#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"', 
      "“", "%", "‘", "�", "–", "…", "_", "”", '“', '„'
  ]
  chars_to_mapping = {
  "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
  }
  
  def multiple_replace(text, chars_to_mapping):
      pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
      return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
  
  def remove_special_characters(text, chars_to_ignore_regex):
      text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
      return text
  
  def normalizer(batch, chars_to_ignore, chars_to_mapping):
      chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
      text = batch["sentence"].lower().strip()
      
      text = text.replace("\u0307", " ").strip()
      text = multiple_replace(text, chars_to_mapping)
      text = remove_special_characters(text, chars_to_ignore_regex)
  
      batch["sentence"] = text
      return batch
  
  
  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)[0]
      return batch
  
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian")
  model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian").to(device)
  
  dataset = load_dataset("common_voice", "et", split="test[:1%]")
  dataset = dataset.map(
      normalizer, 
      fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
      remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
  )
  
  dataset = dataset.map(speech_file_to_array_fn)
  result = dataset.map(predict)
  
  max_items = np.random.randint(0, len(result), 10).tolist()
  for i in max_items:
      reference, predicted =  result["sentence"][i], result["predicted"][i]
      print("reference:", reference)
      print("predicted:", predicted)
      print('---')
  ```
  
  **Output:**
  ```text
  reference: õhulossid lagunevad ning ees ootab maapind 
  predicted: õhulassid lagunevad ning ees ootab maapind
  ---
  reference: milliseks kiievisse pääsemise nimel võistlev muusik soome muusikamaastiku hetkeseisu hindab ning kas ta ka ennast sellel tulevikus tegutsemas näeb kuuled videost 
  predicted: milliseks gievisse pääsemise nimel võitlev muusiks soome muusikama aastiku hetke seisu hindab ning kas ta ennast selle tulevikus tegutsemast näeb kuulad videost
  ---
  reference: näiteks kui pool seina on tehtud tekib tunne et tahaks tegelikult natuke teistsugust ja hakkame otsast peale 
  predicted: näiteks kui pool seine on tehtud tekib tunnetahaks tegelikult matuka teistsugust jahappanna otsast peane
  ---
  reference: neuroesteetilised katsed näi