readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_9_0
  model-index:
  - name: fine-tune-xlsr-53-wav2vec2-on-swahili-sagemaker-2
    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-xlsr-53-wav2vec2-on-swahili-sagemaker-2
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_9_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2089
  - Wer: 0.2356
  
  ## 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
  - 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.3715        | 0.22  | 400   | 3.1337          | 1.0    |
  | 1.7928        | 0.44  | 800   | 0.7137          | 0.6290 |
  | 0.5382        | 0.66  | 1200  | 0.5686          | 0.4708 |
  | 0.4263        | 0.89  | 1600  | 0.3693          | 0.4091 |
  | 0.3705        | 1.11  | 2000  | 0.3925          | 0.3747 |
  | 0.3348        | 1.33  | 2400  | 0.2908          | 0.3597 |
  | 0.3151        | 1.55  | 2800  | 0.3403          | 0.3388 |
  | 0.2977        | 1.77  | 3200  | 0.2698          | 0.3294 |
  | 0.2901        | 1.99  | 3600  | 0.6100          | 0.3173 |
  | 0.2432        | 2.22  | 4000  | 0.2893          | 0.3213 |
  | 0.256         | 2.44  | 4400  | 0.2604          | 0.3087 |
  | 0.2453        | 2.66  | 4800  | 0.2448          | 0.3077 |
  | 0.2427        | 2.88  | 5200  | 0.2391          | 0.2925 |
  | 0.2235        | 3.1   | 5600  | 0.8570          | 0.2907 |
  | 0.2078        | 3.32  | 6000  | 0.2289          | 0.2884 |
  | 0.199         | 3.55  | 6400  | 0.2303          | 0.2852 |
  | 0.2092        | 3.77  | 6800  | 0.2270          | 0.2769 |
  | 0.2           | 3.99  | 7200  | 0.2588          | 0.2823 |
  | 0.1806        | 4.21  | 7600  | 0.2324          | 0.2757 |
  | 0.1789        | 4.43  | 8000  | 0.2051          | 0.2721 |
  | 0.1753        | 4.65  | 8400  | 0.2290          | 0.2695 |
  | 0.1734        | 4.88  | 8800  | 0.2161          | 0.2686 |
  | 0.1648        | 5.1   | 9200  | 0.2139          | 0.2695 |
  | 0.158         | 5.32  | 9600  | 0.2218          | 0.2632 |
  | 0.151         | 5.54  | 10000 | 0.2060          | 0.2594 |
  | 0.1534        | 5.76  | 10400 | 0.2199          | 0.2638 |
  | 0.1485        | 5.98  | 10800 | 0.2023          | 0.2584 |
  | 0.1332        | 6.2   | 11200 | 0.2160          | 0.2547 |
  | 0.1319        | 6.43  | 11600 | 0.2045          | 0.2547 |
  | 0.1329        | 6.65  | 12000 | 0.2072          | 0.2545 |
  | 0.1329        | 6.87  | 12400 | 0.2014          | 0.2502 |
  | 0.1307        | 7.09  | 12800 | 0.2045          | 0.2487 |
  | 0.1197        | 7.31  | 13200 | 0.1987          | 0.2491 |
  | 0.118         | 7.53  | 13600 | 0.1947          | 0.2442 |
  | 0.1194        | 7.76  | 14000 | 0.1863          | 0.2430 |
  | 0.1157        | 7.98  | 14400 | 0.3602          | 0.2430 |
  | 0.1095        | 8.2   | 14800 | 0.2074          | 0.2408 |
  | 0.1051        | 8.42  | 15200 | 0.2113          | 0.2410 |
  | 0.1073        | 8.64  | 15600 | 0.2064          | 0.2395 |
  | 0.1025        | 8.86  | 16000 | 0.2012          | 0.2396 |
  | 0.1027        | 9.09  | 16400 | 0.2342          | 0.2372 |
  | 0.0998        | 9.31  | 16800 | 0.2206          | 0.2357 |
  | 0.0935        | 9.53  | 17200 | 0.2151          | 0.2356 |
  | 0.0959        | 9.75  | 17600 | 0.2096          | 0.2355 |
  | 0.095         | 9.97  | 18000 | 0.2089          | 0.2354 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.4
  - Tokenizers 0.13.0
  