readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  metrics:
  - wer
  model-index:
  - name: v2-wav2vec2-large-xls-r-300m-french-colab
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: common_voice
        type: common_voice
        config: fr
        split: train[:4%]
        args: fr
      metrics:
      - name: Wer
        type: wer
        value: 0.21538187009279086
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # v2-wav2vec2-large-xls-r-300m-french-colab
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3972
  - Wer: 0.2154
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.3672        | 1.07  | 400   | 0.4010          | 0.3241 |
  | 0.3467        | 2.14  | 800   | 0.4687          | 0.3642 |
  | 0.3223        | 3.21  | 1200  | 0.4495          | 0.3248 |
  | 0.3236        | 4.28  | 1600  | 0.4325          | 0.3289 |
  | 0.2764        | 5.35  | 2000  | 0.4101          | 0.3005 |
  | 0.2554        | 6.42  | 2400  | 0.4211          | 0.3148 |
  | 0.2198        | 7.49  | 2800  | 0.4217          | 0.2946 |
  | 0.2112        | 8.56  | 3200  | 0.4217          | 0.2930 |
  | 0.1813        | 9.63  | 3600  | 0.4110          | 0.2682 |
  | 0.1727        | 10.7  | 4000  | 0.3908          | 0.2791 |
  | 0.1543        | 11.76 | 4400  | 0.4284          | 0.2746 |
  | 0.154         | 12.83 | 4800  | 0.4096          | 0.2743 |
  | 0.134         | 13.9  | 5200  | 0.4157          | 0.2582 |
  | 0.1207        | 14.97 | 5600  | 0.4057          | 0.2525 |
  | 0.1145        | 16.04 | 6000  | 0.4255          | 0.2498 |
  | 0.0996        | 17.11 | 6400  | 0.4282          | 0.2488 |
  | 0.0971        | 18.18 | 6800  | 0.3763          | 0.2427 |
  | 0.0886        | 19.25 | 7200  | 0.3833          | 0.2473 |
  | 0.082         | 20.32 | 7600  | 0.3849          | 0.2402 |
  | 0.0765        | 21.39 | 8000  | 0.4083          | 0.2327 |
  | 0.07          | 22.46 | 8400  | 0.4132          | 0.2355 |
  | 0.0601        | 23.53 | 8800  | 0.4124          | 0.2332 |
  | 0.0583        | 24.6  | 9200  | 0.3956          | 0.2248 |
  | 0.0537        | 25.67 | 9600  | 0.4103          | 0.2289 |
  | 0.0487        | 26.74 | 10000 | 0.4050          | 0.2266 |
  | 0.0459        | 27.81 | 10400 | 0.3827          | 0.2173 |
  | 0.0426        | 28.88 | 10800 | 0.3943          | 0.2152 |
  | 0.0386        | 29.95 | 11200 | 0.3972          | 0.2154 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  