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W2V2_Bert_DigitalUmuganda_Afrivoice_Shona_86hr_v1

Automatic Speech Recognition
Transformers
Safetensors
Shona
wav2vec2-bert
Generated from Trainer
Eval Results
Inference Endpoints
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W2V2_Bert_DigitalUmuganda_Afrivoice_Shona_86hr_v1 / README.md
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metadata
library_name: transformers
language:
  - sn
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
datasets:
  - DigitalUmuganda/Afrivoice
metrics:
  - wer
model-index:
  - name: facebook/w2v-bert-2.0
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Afrivoice
          type: DigitalUmuganda/Afrivoice
        metrics:
          - name: Wer
            type: wer
            value: 0.2852422372321298

facebook/w2v-bert-2.0

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the Afrivoice dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3279
  • Wer: 0.2852
  • Cer: 0.0608

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.025
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.6237 1.0 3770 0.2098 0.2662 0.0444
0.198 2.0 7540 0.2007 0.2578 0.0431
0.1894 3.0 11310 0.1866 0.2487 0.0414
0.1734 4.0 15080 0.1879 0.2471 0.0430
0.1616 5.0 18850 0.1895 0.2596 0.0430
0.1535 6.0 22620 0.1861 0.2449 0.0419
0.1464 7.0 26390 0.1742 0.2410 0.0394
0.1404 8.0 30160 0.1716 0.2285 0.0377
0.1351 9.0 33930 0.1749 0.2323 0.0385
0.1284 10.0 37700 0.1792 0.2358 0.0391
0.1242 11.0 41470 0.1780 0.2355 0.0395
0.1169 12.0 45240 0.1938 0.2311 0.0389
0.1106 13.0 49010 0.1808 0.2289 0.0378
0.1041 14.0 52780 0.1838 0.2280 0.0381
0.0982 15.0 56550 0.1970 0.2274 0.0380
0.0916 16.0 60320 0.1861 0.2275 0.0376
0.0838 17.0 64090 0.1960 0.2306 0.0386
0.0781 18.0 67860 0.2029 0.2294 0.0380

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.1.0+cu118
  • Datasets 3.0.1
  • Tokenizers 0.20.1