readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  metrics:
  - wer
  model-index:
  - name: 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: 0.2073518915060671
  ---
  
  <!-- 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. -->
  
  # 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: 0.4075
  - Wer: 0.2074
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.4642        | 1.07  | 400   | 1.4491          | 0.8681 |
  | 0.9543        | 2.14  | 800   | 0.5998          | 0.4982 |
  | 0.5364        | 3.21  | 1200  | 0.4400          | 0.3549 |
  | 0.4236        | 4.28  | 1600  | 0.4348          | 0.3476 |
  | 0.3345        | 5.35  | 2000  | 0.3897          | 0.3000 |
  | 0.2938        | 6.42  | 2400  | 0.3893          | 0.3176 |
  | 0.2502        | 7.49  | 2800  | 0.4306          | 0.3000 |
  | 0.2376        | 8.56  | 3200  | 0.4023          | 0.2939 |
  | 0.1999        | 9.63  | 3600  | 0.3973          | 0.2652 |
  | 0.1859        | 10.7  | 4000  | 0.3701          | 0.2773 |
  | 0.1673        | 11.76 | 4400  | 0.4047          | 0.2661 |
  | 0.1555        | 12.83 | 4800  | 0.4207          | 0.2670 |
  | 0.1385        | 13.9  | 5200  | 0.4110          | 0.2700 |
  | 0.13          | 14.97 | 5600  | 0.4209          | 0.2575 |
  | 0.1185        | 16.04 | 6000  | 0.4385          | 0.2582 |
  | 0.11          | 17.11 | 6400  | 0.4334          | 0.2461 |
  | 0.1016        | 18.18 | 6800  | 0.4058          | 0.2450 |
  | 0.0913        | 19.25 | 7200  | 0.3923          | 0.2439 |
  | 0.0843        | 20.32 | 7600  | 0.4139          | 0.2434 |
  | 0.0782        | 21.39 | 8000  | 0.4111          | 0.2397 |
  | 0.0732        | 22.46 | 8400  | 0.4116          | 0.2327 |
  | 0.0644        | 23.53 | 8800  | 0.4041          | 0.2327 |
  | 0.0603        | 24.6  | 9200  | 0.4065          | 0.2232 |
  | 0.0553        | 25.67 | 9600  | 0.4198          | 0.2198 |
  | 0.0502        | 26.74 | 10000 | 0.4137          | 0.2172 |
  | 0.0472        | 27.81 | 10400 | 0.4084          | 0.2148 |
  | 0.0455        | 28.88 | 10800 | 0.4116          | 0.2109 |
  | 0.0417        | 29.95 | 11200 | 0.4075          | 0.2074 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  