readme: ---
  language: eu
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Large 53 Basque by pcuenq 
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice eu
        type: common_voice
        args: eu
      metrics:
         - name: Test WER
           type: wer
           value: 15.34
  ---
  
  # Wav2Vec2-Large-XLSR-53-EU
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Basque using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
  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:
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  test_dataset = load_dataset("common_voice", "eu", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu")
  model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # 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()
  	return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset["speech"][:2], 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"][:2])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows on the Basque test data of Common Voice.
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  test_dataset = load_dataset("common_voice", "eu", split="test")
  wer = load_metric("wer")
  
  model_name = "pcuenq/wav2vec2-large-xlsr-53-eu"
  
  processor = Wav2Vec2Processor.from_pretrained(model_name)
  model = Wav2Vec2ForCTC.from_pretrained(model_name)
  model.to("cuda")
  
  ## Text pre-processing
  
  chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\￼\…\’\ː\'\‹\›\`\´\®\—\→]'
  chars_to_ignore_pattern = re.compile(chars_to_ignore_regex)
  
  def remove_special_characters(batch):
      batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " "
      return batch
  
  ## Audio pre-processing
  
  import librosa
  def speech_file_to_array_fn(batch):
      speech_array, sample_rate = torchaudio.load(batch["path"])
      batch["speech"] = librosa.resample(speech_array.squeeze().numpy(), sample_rate, 16_000)
      return batch
  
  # Text transformation and audio resampling
  def cv_prepare(batch):
      batch = remove_special_characters(batch)
      batch = speech_file_to_array_fn(batch)
      return batch
  
  # Number of CPUs or None
  num_proc = 16
  test_dataset = test_dataset.map(cv_prepare, remove_columns=['path'], num_proc=num_proc)
  
  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)
  
  # WER Metric computation
  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
  ```
  
  **Test Result**: 15.34 %
  
  ## Training
  
  The Common Voice `train` and `validation` datasets were used for training. Training was performed for 22 + 20 epochs with the following parameters:
  
  - Batch size 16, 2 gradient accumulation steps.
  - Learning rate: 2.5e-4
  - Activation dropout: 0.05
  - Attention dropout: 0.1
  - Hidden dropout: 0.05
  - Feature proj. dropout: 0.05
  - Mask time probability: 0.08
  - Layer dropout: 0.05
  
  