readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_9_0
  model-index:
  - name: fine-tune-xls-r-300m-wav2vec2-on-swahili-sagemaker
    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. -->
  
  # fine-tune-xls-r-300m-wav2vec2-on-swahili-sagemaker
  
  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_9_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1866
  - Wer: 0.2346
  
  ## 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: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.0466        | 0.22  | 400   | 3.2175          | 1.0    |
  | 1.8604        | 0.44  | 800   | 0.6654          | 0.6770 |
  | 0.5942        | 0.66  | 1200  | 0.4830          | 0.5013 |
  | 0.4631        | 0.89  | 1600  | 0.3532          | 0.4274 |
  | 0.3949        | 1.11  | 2000  | 0.3612          | 0.4008 |
  | 0.3634        | 1.33  | 2400  | 0.3024          | 0.3691 |
  | 0.3385        | 1.55  | 2800  | 0.3271          | 0.3633 |
  | 0.3225        | 1.77  | 3200  | 0.2795          | 0.3431 |
  | 0.3062        | 1.99  | 3600  | 0.2651          | 0.3353 |
  | 0.2573        | 2.22  | 4000  | 0.2832          | 0.3241 |
  | 0.2608        | 2.44  | 4400  | 0.2921          | 0.3225 |
  | 0.2583        | 2.66  | 4800  | 0.3887          | 0.3119 |
  | 0.2536        | 2.88  | 5200  | 0.2526          | 0.3156 |
  | 0.236         | 3.1   | 5600  | 0.2473          | 0.2988 |
  | 0.2202        | 3.32  | 6000  | 0.2537          | 0.3016 |
  | 0.2119        | 3.55  | 6400  | 0.2769          | 0.2979 |
  | 0.2128        | 3.77  | 6800  | 0.2363          | 0.2940 |
  | 0.2124        | 3.99  | 7200  | 0.2198          | 0.2846 |
  | 0.1854        | 4.21  | 7600  | 0.2230          | 0.2855 |
  | 0.184         | 4.43  | 8000  | 0.2194          | 0.2781 |
  | 0.1799        | 4.65  | 8400  | 0.2226          | 0.2752 |
  | 0.1792        | 4.88  | 8800  | 0.2242          | 0.2712 |
  | 0.1702        | 5.1   | 9200  | 0.2185          | 0.2721 |
  | 0.1613        | 5.32  | 9600  | 0.2106          | 0.2696 |
  | 0.1611        | 5.54  | 10000 | 0.2132          | 0.2673 |
  | 0.1588        | 5.76  | 10400 | 0.1980          | 0.2604 |
  | 0.1564        | 5.98  | 10800 | 0.2021          | 0.2617 |
  | 0.1386        | 6.2   | 11200 | 0.2017          | 0.2603 |
  | 0.1378        | 6.43  | 11600 | 0.1994          | 0.2585 |
  | 0.1345        | 6.65  | 12000 | 0.1974          | 0.2559 |
  | 0.1407        | 6.87  | 12400 | 0.1946          | 0.2526 |
  | 0.1336        | 7.09  | 12800 | 0.1991          | 0.2505 |
  | 0.1178        | 7.31  | 13200 | 0.1973          | 0.2504 |
  | 0.1174        | 7.53  | 13600 | 0.1977          | 0.2485 |
  | 0.1169        | 7.76  | 14000 | 0.1936          | 0.2454 |
  | 0.1163        | 7.98  | 14400 | 0.1910          | 0.2441 |
  | 0.1074        | 8.2   | 14800 | 0.1990          | 0.2456 |
  | 0.1055        | 8.42  | 15200 | 0.1901          | 0.2420 |
  | 0.108         | 8.64  | 15600 | 0.1900          | 0.2389 |
  | 0.1019        | 8.86  | 16000 | 0.1895          | 0.2385 |
  | 0.0998        | 9.09  | 16400 | 0.1919          | 0.2364 |
  | 0.0966        | 9.31  | 16800 | 0.1897          | 0.2362 |
  | 0.0903        | 9.53  | 17200 | 0.1858          | 0.2347 |
  | 0.0887        | 9.75  | 17600 | 0.1883          | 0.2348 |
  | 0.0934        | 9.97  | 18000 | 0.1866          | 0.2345 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.4
  - Tokenizers 0.13.0
  