readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-kinyarwanda
    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. -->
  
  # wav2vec2-large-xls-r-300m-kinyarwanda
  
  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: 0.3917
  - Wer: 0.3246
  
  ## 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: 7e-05
  - train_batch_size: 12
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 24
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 400
  - num_epochs: 8
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 9.0634        | 0.12  | 400   | 3.0554          | 1.0    |
  | 2.8009        | 0.24  | 800   | 1.5927          | 0.9554 |
  | 0.9022        | 0.36  | 1200  | 0.7328          | 0.6445 |
  | 0.6213        | 0.48  | 1600  | 0.6138          | 0.5510 |
  | 0.5299        | 0.6   | 2000  | 0.6072          | 0.5223 |
  | 0.4999        | 0.72  | 2400  | 0.5449          | 0.4969 |
  | 0.4731        | 0.84  | 2800  | 0.5261          | 0.4828 |
  | 0.458         | 0.96  | 3200  | 0.5058          | 0.4607 |
  | 0.4158        | 1.09  | 3600  | 0.4892          | 0.4463 |
  | 0.4037        | 1.21  | 4000  | 0.4759          | 0.4429 |
  | 0.4021        | 1.33  | 4400  | 0.4615          | 0.4330 |
  | 0.3934        | 1.45  | 4800  | 0.4593          | 0.4315 |
  | 0.3808        | 1.57  | 5200  | 0.4736          | 0.4344 |
  | 0.3838        | 1.69  | 5600  | 0.4569          | 0.4249 |
  | 0.3726        | 1.81  | 6000  | 0.4473          | 0.4140 |
  | 0.3623        | 1.93  | 6400  | 0.4403          | 0.4097 |
  | 0.3517        | 2.05  | 6800  | 0.4389          | 0.4061 |
  | 0.333         | 2.17  | 7200  | 0.4383          | 0.4104 |
  | 0.3354        | 2.29  | 7600  | 0.4360          | 0.3955 |
  | 0.3257        | 2.41  | 8000  | 0.4226          | 0.3942 |
  | 0.3275        | 2.53  | 8400  | 0.4206          | 0.4040 |
  | 0.3262        | 2.65  | 8800  | 0.4172          | 0.3875 |
  | 0.3206        | 2.77  | 9200  | 0.4209          | 0.3877 |
  | 0.323         | 2.89  | 9600  | 0.4177          | 0.3825 |
  | 0.3099        | 3.01  | 10000 | 0.4101          | 0.3691 |
  | 0.3008        | 3.14  | 10400 | 0.4055          | 0.3709 |
  | 0.2918        | 3.26  | 10800 | 0.4085          | 0.3800 |
  | 0.292         | 3.38  | 11200 | 0.4089          | 0.3713 |
  | 0.292         | 3.5   | 11600 | 0.4092          | 0.3730 |
  | 0.2785        | 3.62  | 12000 | 0.4151          | 0.3687 |
  | 0.2941        | 3.74  | 12400 | 0.4004          | 0.3639 |
  | 0.2838        | 3.86  | 12800 | 0.4108          | 0.3703 |
  | 0.2854        | 3.98  | 13200 | 0.3911          | 0.3596 |
  | 0.2683        | 4.1   | 13600 | 0.3944          | 0.3575 |
  | 0.2647        | 4.22  | 14000 | 0.3836          | 0.3538 |
  | 0.2704        | 4.34  | 14400 | 0.4006          | 0.3540 |
  | 0.2664        | 4.46  | 14800 | 0.3974          | 0.3553 |
  | 0.2662        | 4.58  | 15200 | 0.3890          | 0.3470 |
  | 0.2615        | 4.7   | 15600 | 0.3856          | 0.3507 |
  | 0.2553        | 4.82  | 16000 | 0.3814          | 0.3497 |
  | 0.2587        | 4.94  | 16400 | 0.3837          | 0.3440 |
  | 0.2522        | 5.06  | 16800 | 0.3834          | 0.3486 |
  | 0.2451        | 5.19  | 17200 | 0.3897          | 0.3414 |
  | 0.2423        | 5.31  | 17600 | 0.3864          | 0.3481 |
  | 0.2434        | 5.43  | 18000 | 0.3808          | 0.3416 |
  | 0.2525        | 5.55  | 18400 | 0.3795          | 0.3408 |
  | 0.2427        | 5.67  | 18800 | 0.3841          | 0.3411 |
  | 0.2411        | 5.79  | 19200 | 0.3804          | 0.3366 |
  | 0.2404        | 5.91  | 19600 | 0.3800          | 0.3328 |
  | 0.2372        | 6.03  | 20000 | 0.3749          | 0.3335 |
  | 0.2244        | 6.15  | 20400 | 0.3820          | 0.3327 |
  | 0.2381        | 6.27  | 20800 | 0.3789          | 0.3325 |
  | 0.2294        | 6.39  | 21200 | 0.3867          | 0.3298 |
  | 0.2378        | 6.51  | 21600 | 0.3843          | 0.3281 |
  | 0.2312        | 6.63  | 22000 | 0.3813          | 0.3277 |
  | 0.2411        | 6.75  | 22400 | 0.3780          | 0.3268 |
  | 0.2315        | 6.87  | 22800 | 0.3790          | 0.3280 |
  | 0.241         | 6.99  | 23200 | 0.3776          | 0.3281 |
  | 0.2313        | 7.11  | 23600 | 0.3929          | 0.3283 |
  | 0.2423        | 7.24  | 24000 | 0.3905    