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hamsa-tiny-pretrained

Automatic Speech Recognition
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metadata
base_model: nadsoft/Hamsa-tiny
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - nadsoft/QASR-Speech-Resource
metrics:
  - wer
model-index:
  - name: hamsa-tiny-pretrained
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: nadsoft/QASR-Speech-Resource default
          type: nadsoft/QASR-Speech-Resource
        metrics:
          - name: Wer
            type: wer
            value: 28.726358005647974

hamsa-tiny-pretrained

This model is a fine-tuned version of nadsoft/Hamsa-tiny on the nadsoft/QASR-Speech-Resource default dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3795
  • Wer: 28.7264

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: 1e-05
  • train_batch_size: 64
  • 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: 500
  • training_steps: 50000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.6597 0.1 2500 0.6394 48.8384
0.5442 0.2 5000 0.5455 41.8543
0.4954 0.3 7500 0.5018 39.8609
0.474 0.4 10000 0.4770 38.5534
0.4696 0.5 12500 0.4566 36.2515
0.4312 0.6 15000 0.4433 36.8780
0.4208 0.7 17500 0.4308 32.3714
0.4089 0.8 20000 0.4229 33.4109
0.4163 0.9 22500 0.4143 32.5423
0.3831 1.0 25000 0.4077 31.6951
0.3842 1.1 27500 0.4023 33.6316
0.3848 1.2 30000 0.3984 30.1099
0.3774 1.3 32500 0.3948 29.2864
0.3667 1.4 35000 0.3912 29.5166
0.3674 1.5 37500 0.3881 29.6115
0.3721 1.6 40000 0.3851 30.4065
0.3533 1.7 42500 0.3834 27.9693
0.3594 1.8 45000 0.3815 28.8569
0.3628 1.9 47500 0.3802 28.1260
0.3392 2.0 50000 0.3795 28.7264

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.2.dev0
  • Tokenizers 0.15.0