metadata

library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: toki_pona_test_cogn_wav2vec2-xls-r-300m
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: tok
          split: test
          args: tok
        metrics:
          - name: Wer
            type: wer
            value: 0.04568527918781726

toki_pona_test_cogn_wav2vec2-xls-r-300m

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.0718
Wer: 0.0457

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: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 14
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.9966	147	2.6699	1.0
No log	2.0	295	0.6492	0.5195
No log	2.9966	442	0.1896	0.1911
2.6379	4.0	590	0.1464	0.1274
2.6379	4.9966	737	0.1177	0.1073
2.6379	6.0	885	0.1089	0.0935
0.1404	6.9966	1032	0.0978	0.0792
0.1404	8.0	1180	0.0957	0.0735
0.1404	8.9966	1327	0.0888	0.0697
0.1404	10.0	1475	0.0741	0.0584
0.0683	10.9966	1622	0.0710	0.0519
0.0683	12.0	1770	0.0687	0.0477
0.0683	12.9966	1917	0.0709	0.0458
0.0383	13.9525	2058	0.0718	0.0457

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3