readme: ---
  language: br
  datasets:
  - common_voice
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Breton by Marxav
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice br
        type: common_voice
        args: br
      metrics:
         - name: Test WER
           type: wer
           value: 43.43
  ---
  # wav2vec2-large-xlsr-53-breton
  The model can be used directly (without a language model) as follows:
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  lang = "br"
  test_dataset = load_dataset("common_voice", lang, split="test[:2%]") 
  
  processor = Wav2Vec2Processor.from_pretrained("Marxav/wav2vec2-large-xlsr-53-breton") 
  model = Wav2Vec2ForCTC.from_pretrained("Marxav/wav2vec2-large-xlsr-53-breton")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
     speech_array, sampling_rate = torchaudio.load(batch["path"])
     batch["speech"] = resampler(speech_array).squeeze().numpy()
     batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
     batch["sentence"] = re.sub("ʼ", "'", batch["sentence"])
     batch["sentence"] = re.sub("’", "'", batch["sentence"])
     batch["sentence"] = re.sub('‘', "'", batch["sentence"])
     return batch
  
  nb_samples = 2
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  
  inputs = processor(test_dataset["speech"][:nb_samples], sampling_rate=16_000, return_tensors="pt", padding=True)
  
  with torch.no_grad():
     logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
  
  predicted_ids = torch.argmax(logits, dim=-1)
  
  print("Prediction:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset["sentence"][:nb_samples])
  ```
  The above code leads to the following prediction for the first two samples:
  * Prediction: ["neller ket dont a-benn eus netra la vez ser merc'hed evel sich", 'an eil hag egile']
  * Reference: ["N'haller ket dont a-benn eus netra pa vezer nec'het evel-se.", 'An eil hag egile.']
  
  The model can be evaluated as follows on the {language} test data of Common Voice.
  ```python
  import re
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  lang = 'br'
  test_dataset = load_dataset("common_voice", lang, split="test")
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained('Marxav/wav2vec2-large-xlsr-53-breton')
  model = Wav2Vec2ForCTC.from_pretrained('Marxav/wav2vec2-large-xlsr-53-breton')
  model.to("cuda")
  
  chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
    batch["sentence"] = re.sub("ʼ", "'", batch["sentence"])
    batch["sentence"] = re.sub("’", "'", batch["sentence"])
    batch["sentence"] = re.sub('‘', "'", batch["sentence"])
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch
  
  test_dataset = test_dataset.map(remove_special_characters)
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
    with torch.no_grad():
      logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
  
      pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch
  
  result = test_dataset.map(evaluate, batched=True, batch_size=8)
  
  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
  ```
  
  **Test Result**: 43.43%
  ## Training
  The Common Voice `train`, `validation` datasets were used for training.