readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: xtreme_s_xlsr_300m_fleurs_asr
    results: []
  ---
  
  <!-- 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. -->
  
  # xtreme_s_xlsr_300m_fleurs_asr
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Cer: 0.3330
  - Loss: 1.2864
  - Wer: 0.8344
  
  ## 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: 8
  - eval_batch_size: 4
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 4
  - total_train_batch_size: 32
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Cer    | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:------:|:---------------:|:------:|
  | 4.677         | 0.13  | 1000  | 1.0    | 3.2323          | 1.0    |
  | 4.1512        | 0.26  | 2000  | 0.5098 | 1.7858          | 0.9869 |
  | 1.119         | 0.39  | 3000  | 0.4412 | 1.6628          | 0.9063 |
  | 0.8573        | 0.52  | 4000  | 0.3588 | 1.3440          | 0.9016 |
  | 1.0232        | 0.65  | 5000  | 0.3690 | 1.3004          | 0.8775 |
  | 0.6328        | 0.78  | 6000  | 0.3354 | 1.2219          | 0.8331 |
  | 0.6636        | 0.91  | 7000  | 0.3604 | 1.2839          | 0.8637 |
  | 0.6536        | 1.04  | 8000  | 0.3420 | 1.2481          | 0.8504 |
  | 0.5002        | 1.17  | 9000  | 0.3518 | 1.2514          | 0.8403 |
  | 0.4785        | 1.3   | 10000 | 0.3399 | 1.2409          | 0.8570 |
  | 0.517         | 1.43  | 11000 | 0.3599 | 1.3058          | 0.8654 |
  | 0.506         | 1.56  | 12000 | 0.3484 | 1.2350          | 0.8441 |
  | 0.4013        | 1.69  | 13000 | 0.3327 | 1.1982          | 0.8246 |
  | 0.3521        | 1.82  | 14000 | 0.3270 | 1.1653          | 0.8265 |
  | 0.4265        | 1.95  | 15000 | 0.3562 | 1.2647          | 0.8564 |
  | 0.3949        | 2.08  | 16000 | 0.3490 | 1.2988          | 0.8480 |
  | 0.3059        | 2.21  | 17000 | 0.3327 | 1.2332          | 0.8323 |
  | 0.3618        | 2.34  | 18000 | 0.3480 | 1.2394          | 0.8517 |
  | 0.2567        | 2.47  | 19000 | 0.3365 | 1.2294          | 0.8394 |
  | 0.3501        | 2.6   | 20000 | 0.3271 | 1.1853          | 0.8250 |
  | 0.2766        | 2.73  | 21000 | 0.3425 | 1.2339          | 0.8443 |
  | 0.3396        | 2.86  | 22000 | 0.3501 | 1.2768          | 0.8669 |
  | 0.3566        | 2.99  | 23000 | 0.3477 | 1.2648          | 0.8710 |
  | 0.3166        | 3.12  | 24000 | 0.3550 | 1.3773          | 0.8641 |
  | 0.2388        | 3.25  | 25000 | 0.3301 | 1.2374          | 0.8316 |
  | 0.2057        | 3.38  | 26000 | 0.3429 | 1.2846          | 0.8560 |
  | 0.2264        | 3.51  | 27000 | 0.3469 | 1.2676          | 0.8542 |
  | 0.1998        | 3.64  | 28000 | 0.3531 | 1.3365          | 0.8655 |
  | 0.2701        | 3.77  | 29000 | 0.3518 | 1.3124          | 0.8711 |
  | 0.18          | 3.9   | 30000 | 0.3498 | 1.3095          | 0.8648 |
  | 0.1337        | 4.03  | 31000 | 0.3397 | 1.2941          | 0.8452 |
  | 0.162         | 4.16  | 32000 | 0.3320 | 1.2942          | 0.8295 |
  | 0.2776        | 4.29  | 33000 | 0.3275 | 1.2690          | 0.8276 |
  | 0.1634        | 4.42  | 34000 | 0.3307 | 1.3145          | 0.8331 |
  | 0.2172        | 4.54  | 35000 | 0.3334 | 1.3031          | 0.8435 |
  | 0.1305        | 4.67  | 36000 | 0.3303 | 1.2768          | 0.8321 |
  | 0.1436        | 4.8   | 37000 | 0.3353 | 1.2968          | 0.8416 |
  | 0.134         | 4.93  | 38000 | 0.3330 | 1.2864          | 0.8344 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0.dev0
  - Pytorch 1.10.1+cu111
  - Datasets 1.18.4.dev0
  - Tokenizers 0.11.6
  