ethiopic-asr / README.md
Samuael's picture
End of training
3143b45 verified
metadata
library_name: transformers
license: apache-2.0
base_model: Samuael/geez-asr
tags:
  - generated_from_trainer
datasets:
  - alffa_amharic
metrics:
  - wer
model-index:
  - name: ethiopic-asr
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: alffa_amharic
          type: alffa_amharic
          config: clean
          split: None
          args: clean
        metrics:
          - name: Wer
            type: wer
            value: 0.14692601597777005

ethiopic-asr

This model is a fine-tuned version of Samuael/geez-asr on the alffa_amharic dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1301
  • Wer: 0.1469
  • Phoneme Cer: 0.0296
  • Cer: 0.0416

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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 1
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Phoneme Cer Cer
No log 0.0442 200 3.2216 1.0 1.0 1.0
No log 0.0883 400 3.1164 1.0 1.0 1.0
4.1769 0.1325 600 0.9628 0.5476 0.1141 0.1609
4.1769 0.1767 800 0.3181 0.2150 0.0430 0.0607
0.8455 0.2208 1000 0.2195 0.1759 0.0353 0.0503
0.8455 0.2650 1200 0.1913 0.1846 0.0365 0.0520
0.8455 0.3092 1400 0.1699 0.1591 0.0322 0.0454
0.2929 0.3534 1600 0.1603 0.1572 0.0316 0.0442
0.2929 0.3975 1800 0.1503 0.1567 0.0315 0.0442
0.2392 0.4417 2000 0.1476 0.1587 0.0318 0.0446
0.2392 0.4859 2200 0.1449 0.1565 0.0312 0.0438
0.2392 0.5300 2400 0.1409 0.1537 0.0308 0.0427
0.2166 0.5742 2600 0.1395 0.1551 0.0308 0.0428
0.2166 0.6184 2800 0.1345 0.1469 0.0290 0.0410
0.2068 0.6625 3000 0.1331 0.1509 0.0297 0.0419
0.2068 0.7067 3200 0.1346 0.1518 0.0301 0.0421
0.2068 0.7509 3400 0.1335 0.1507 0.0303 0.0426
0.2037 0.7951 3600 0.1312 0.1471 0.0297 0.0415
0.2037 0.8392 3800 0.1303 0.1438 0.0289 0.0406
0.1985 0.8834 4000 0.1300 0.1457 0.0292 0.0410
0.1985 0.9276 4200 0.1303 0.1471 0.0295 0.0414
0.1985 0.9717 4400 0.1301 0.1469 0.0296 0.0416

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0