readme: ---
  language: ca
  datasets:
  - common_voice 
  - parlament_parla
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Catalan XLSR Wav2Vec2 Large
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      datasets:
        - name: Common Voice ca
          type: common_voice
          args: ca
        - name: ParlamentParla
          url: https://www.openslr.org/59/
      metrics:
         - name: Test WER
           type: wer
           value: 6.92
         - name: Google Crowsourced Corpus WER
           type: wer
           value: 12.99
         - name: Audiobook “La llegenda de Sant Jordi” WER
           type: wer
           value: 13.23
  ---
  
  # Wav2Vec2-Large-XLSR-Català
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets.
  
  **Attention:** The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoice and ParlamentParla dataset and can be found [here](https://github.com/ccoreilly/wav2vec2-catala). Evaluating on the CV test dataset will produce a biased WER as 1144 audio files of that dataset were used in training/evaluation of this model.
  WER was calculated using this [test.csv](https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv) which was not seen by the model during training/evaluation.
  
  You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala)
  
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  ## Results
  
  Word error rate was evaluated on the following datasets unseen by the model:
  
  | Dataset | WER |
  | ------- | --- |
  | [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv)) | 6.92% |
  | [Google Crowsourced Corpus](https://www.openslr.org/69/) | 12.99% |
  | Audiobook “La llegenda de Sant Jordi” | 13.23% | 
  
  
  ## 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", "ca", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala") 
  model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala")
  
  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])
  ```