readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_9_0
  model-index:
  - name: fine-tune-300m-pretrain-swahili-radio2022-1-on-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-300m-pretrain-swahili-radio2022-1-on-sagemaker
  
  This model is a fine-tuned version of [mutisya/wav2vec2-xls-r-300m-pretrain-swahili-radio2022-1](https://huggingface.co/mutisya/wav2vec2-xls-r-300m-pretrain-swahili-radio2022-1) on the common_voice_9_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.8034
  - Wer: 0.9617
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.6048        | 0.22  | 400   | 3.1516          | 1.0    |
  | 2.8624        | 0.44  | 800   | 3.5961          | 1.0    |
  | 2.8579        | 0.66  | 1200  | 3.7368          | 1.0    |
  | 2.8512        | 0.89  | 1600  | 3.8754          | 1.0    |
  | 2.9587        | 1.11  | 2000  | 3.5821          | 1.0    |
  | 2.8613        | 1.33  | 2400  | 3.5670          | 1.0    |
  | 2.8381        | 1.55  | 2800  | 3.3847          | 1.0    |
  | 2.8345        | 1.77  | 3200  | 3.3968          | 1.0    |
  | 2.8321        | 1.99  | 3600  | 3.4520          | 1.0000 |
  | 2.83          | 2.22  | 4000  | 3.5448          | 1.0    |
  | 2.8247        | 2.44  | 4400  | 3.2949          | 1.0000 |
  | 2.827         | 2.66  | 4800  | 3.3803          | 1.0    |
  | 2.8388        | 2.88  | 5200  | 3.3738          | 1.0    |
  | 2.8426        | 3.1   | 5600  | 3.2432          | 1.0    |
  | 2.8371        | 3.32  | 6000  | 3.1967          | 1.0000 |
  | 2.8106        | 3.55  | 6400  | 3.2301          | 1.0000 |
  | 2.8254        | 3.77  | 6800  | 3.2726          | 1.0    |
  | 2.8359        | 3.99  | 7200  | 3.3437          | 1.0    |
  | 2.8363        | 4.21  | 7600  | 3.3891          | 1.0    |
  | 2.8311        | 4.43  | 8000  | 3.3041          | 1.0    |
  | 2.825         | 4.65  | 8400  | 3.2463          | 1.0    |
  | 2.8198        | 4.88  | 8800  | 3.2927          | 1.0000 |
  | 2.7996        | 5.1   | 9200  | 3.1479          | 0.9616 |
  | 2.774         | 5.32  | 9600  | 3.0166          | 0.9712 |
  | 2.7389        | 5.54  | 10000 | 2.9751          | 0.9659 |
  | 2.7073        | 5.76  | 10400 | 2.9367          | 1.0005 |
  | 2.6892        | 5.98  | 10800 | 2.9566          | 0.9557 |
  | 2.677         | 6.2   | 11200 | 2.9217          | 1.0000 |
  | 2.673         | 6.43  | 11600 | 2.9845          | 1.0000 |
  | 2.6608        | 6.65  | 12000 | 2.9512          | 1.0000 |
  | 2.6579        | 6.87  | 12400 | 2.9423          | 0.9557 |
  | 2.6442        | 7.09  | 12800 | 2.9593          | 0.9713 |
  | 2.6336        | 7.31  | 13200 | 2.9102          | 0.9713 |
  | 2.6287        | 7.53  | 13600 | 2.8817          | 0.9713 |
  | 2.6219        | 7.76  | 14000 | 2.9238          | 0.9616 |
  | 2.6187        | 7.98  | 14400 | 2.8422          | 0.9712 |
  | 2.6135        | 8.2   | 14800 | 2.8226          | 0.9617 |
  | 2.6135        | 8.42  | 15200 | 2.7926          | 0.9617 |
  | 2.607         | 8.64  | 15600 | 2.7876          | 0.9713 |
  | 2.6028        | 8.86  | 16000 | 2.7935          | 0.9617 |
  | 2.5977        | 9.09  | 16400 | 2.7860          | 0.9617 |
  | 2.5962        | 9.31  | 16800 | 2.7861          | 0.9713 |
  | 2.5874        | 9.53  | 17200 | 2.7871          | 0.9713 |
  | 2.5853        | 9.75  | 17600 | 2.7947          | 0.9617 |
  | 2.5816        | 9.97  | 18000 | 2.8029          | 0.9617 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.4
  - Tokenizers 0.13.0
  