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hamsa-pretrained

Automatic Speech Recognition
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metadata
base_model: nadsoft/Hamsa-large-v0.1-beta
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - nadsoft/QASR-Speech-Resource
metrics:
  - wer
model-index:
  - name: hamsa-large-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: 29.205723913714138

hamsa-large-pretrained

This model is a fine-tuned version of nadsoft/Hamsa-large-v0.1-beta on the nadsoft/QASR-Speech-Resource default dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4344
  • Wer: 29.2057

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: 0.00025
  • train_batch_size: 8
  • eval_batch_size: 4
  • 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: 35000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.895 0.01 1000 1.8765 86.7012
1.6569 0.01 2000 1.5809 84.0907
1.3312 0.02 3000 1.3458 75.7090
1.2369 0.02 4000 1.2389 73.1365
1.1518 0.03 5000 1.1097 66.8170
1.0135 0.03 6000 1.0616 65.1843
1.0965 0.04 7000 1.0084 65.8582
0.867 0.04 8000 0.9305 57.6093
0.9425 0.05 9000 0.8907 55.4854
0.9501 0.05 10000 0.8393 54.0212
0.8602 0.06 11000 0.8096 53.4968
0.7596 0.06 12000 0.7761 51.9305
0.7334 0.07 13000 0.7694 49.4411
0.708 0.07 14000 0.7336 47.0040
0.7112 0.08 15000 0.7149 47.5783
0.6989 0.08 16000 0.6713 44.2986
0.7025 0.09 17000 0.6639 43.7481
0.6127 0.09 18000 0.6477 42.9127
0.6342 0.1 19000 0.6298 42.6826
0.6174 0.1 20000 0.6080 40.1172
0.5551 0.11 21000 0.5896 39.0398
0.5353 0.11 22000 0.5753 39.1253
0.5528 0.12 23000 0.5588 40.2881
0.5423 0.12 24000 0.5445 35.6606
0.5069 0.13 25000 0.5304 35.9358
0.4356 0.13 26000 0.5187 34.4930
0.5111 0.14 27000 0.5035 33.4227
0.5613 0.14 28000 0.4912 33.0952
0.4165 0.15 29000 0.4825 32.0155
0.4736 0.15 30000 0.4716 32.0914
0.4213 0.16 31000 0.4618 31.6026
0.4242 0.16 32000 0.4514 30.3757
0.3837 0.17 33000 0.4448 30.3116
0.4321 0.17 34000 0.4377 29.4691
0.4268 0.18 35000 0.4344 29.2057

Framework versions

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