readme: ---
  language:
  - ca
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - collectivat/tv3_parla
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - projecte-aina/parlament_parla
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  - collectivat/tv3_parla
  - projecte-aina/parlament_parla
  model-index:
  - name: wav2vec2-xls-r-300m-ca
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_8_0 ca
        type: mozilla-foundation/common_voice_8_0
        args: ca
      metrics:
      - name: Test WER
        type: wer
        value: 13.170091241317552
      - name: Test CER
        type: cer
        value: 3.356726205534543
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: projecte-aina/parlament_parla ca
        type: projecte-aina/parlament_parla
        args: clean
      metrics:
      - name: Test WER
        type: wer
        value: 8.048005647723261
      - name: Test CER
        type: cer
        value: 2.240912911020065
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: collectivat/tv3_parla ca
        type: collectivat/tv3_parla
        args: ca
      metrics:
      - name: Test WER
        type: wer
        value: 23.320629787889285
      - name: Test CER
        type: cer
        value: 10.439216202089989
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: speech-recognition-community-v2/dev_data ca
        type: speech-recognition-community-v2/dev_data
        args: ca
      metrics:
      - name: Test WER
        type: wer
        value: 31.99671115046487
      - name: Test CER
        type: cer
        value: 15.820020687277325
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: ca
      metrics:
      - name: Test WER
        type: wer
        value: 22.04
  ---
  
  # wav2vec2-xls-r-300m-ca
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets.
  It achieves the following results on the evaluation set (for the three datasets):
  - Loss: 0.2472
  - Wer: 0.1499
  
  ## Model description
  
  Please check the original [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) Model card. This is just a finetuned version of that model.
  
  ## Intended uses & limitations
  
  As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language.
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py).
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7.5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 18.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training.
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 6.2099        | 0.09  | 500   | 3.4125          | 1.0    |
  | 2.9961        | 0.18  | 1000  | 2.9224          | 1.0    |
  | 2.2147        | 0.26  | 1500  | 0.6521          | 0.5568 |
  | 1.3017        | 0.35  | 2000  | 0.3153          | 0.2761 |
  | 1.1196        | 0.44  | 2500  | 0.2444          | 0.2367 |
  | 1.0712        | 0.53  | 3000  | 0.2324          | 0.2132 |
  | 1.052         | 0.62  | 3500  | 0.2173          | 0.2032 |
  | 1.2813        | 2.13  | 4000  | 0.3326          | 0.2099 |
  | 1.2365        | 2.4   | 4500  | 0.3224          | 0.2003 |
  | 1.2193        | 2.66  | 5000  | 0.3198          | 0.1957 |
  | 1.2072        | 2.93  | 5500  | 0.3063          | 0.1933 |
  | 1.213         | 3.2   | 6000  | 0.3051          | 0.1980 |
  | 1.2074        | 3.46  | 6500  | 0.3012          | 0.1879 |
  | 1.1918  