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MALIBA-AI
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minianka-asr

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
sudoping01/malian-languages-dataset
Generated from Trainer
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minianka-asr / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - automatic-speech-recognition
  - sudoping01/malian-languages-dataset
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-malian-languages-minianka-dataset
    results: []

wav2vec2-malian-languages-minianka-dataset

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SUDOPING01/MALIAN-LANGUAGES-DATASET - MINIANKA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1794
  • Wer: 0.1271

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
  • gradient_accumulation_steps: 2
  • 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: 15.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.4706 100 3.8351 1.0
No log 0.9412 200 2.8794 1.0
No log 1.4094 300 0.6950 0.5312
No log 1.88 400 0.3555 0.3603
2.6845 2.3482 500 0.2666 0.2772
2.6845 2.8188 600 0.2253 0.2401
2.6845 3.2871 700 0.2025 0.2246
2.6845 3.7576 800 0.1880 0.1979
2.6845 4.2259 900 0.1853 0.1959
0.2121 4.6965 1000 0.1887 0.1909
0.2121 5.1647 1100 0.1745 0.1709
0.2121 5.6353 1200 0.1602 0.1674
0.2121 6.1035 1300 0.1627 0.1588
0.2121 6.5741 1400 0.1593 0.1563
0.1148 7.0424 1500 0.1660 0.1593
0.1148 7.5129 1600 0.1655 0.1551
0.1148 7.9835 1700 0.1581 0.1520
0.1148 8.4518 1800 0.1771 0.1493
0.1148 8.9224 1900 0.1778 0.1482
0.0723 9.3906 2000 0.1683 0.1402
0.0723 9.8612 2100 0.1676 0.1378
0.0723 10.3294 2200 0.1672 0.1365
0.0723 10.8 2300 0.1646 0.1332
0.0723 11.2682 2400 0.1751 0.1337
0.0504 11.7388 2500 0.1742 0.1353
0.0504 12.2071 2600 0.1809 0.1329
0.0504 12.6776 2700 0.1769 0.1298
0.0504 13.1459 2800 0.1752 0.1289
0.0504 13.6165 2900 0.1782 0.1275
0.0382 14.0847 3000 0.1789 0.1289
0.0382 14.5553 3100 0.1783 0.1275

Framework versions

  • Transformers 4.47.0
  • Pytorch 2.5.1+cu121
  • Datasets 3.3.1
  • Tokenizers 0.21.0