---
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: rah_toki_pona
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: tok
      split: test
      args: tok
    metrics:
    - name: Wer
      type: wer
      value: 0.06399569776821726
---

<!-- 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. -->

# rah_toki_pona

This model was finetuned from facebook/wav2vec2-xls-r-300m on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1053
- Wer: 0.0640

## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0516        | 3.22  | 400  | 0.1301          | 0.0996 |
| 0.0817        | 6.45  | 800  | 0.1319          | 0.0899 |
| 0.0567        | 9.67  | 1200 | 0.1009          | 0.0682 |
| 0.0376        | 12.9  | 1600 | 0.1053          | 0.0640 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2