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Librarian Bot: Add base_model information to model (#1)
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - PolyAI/minds14
metrics:
  - wer
base_model: openai/whisper-tiny
model-index:
  - name: whisper-tiny-finetuned-minds14
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: MINDS14
          type: PolyAI/minds14
        metrics:
          - type: wer
            value: 0.34993849938499383
            name: Wer

whisper-tiny-finetuned-minds14

This model is a fine-tuned version of openai/whisper-tiny on the MINDS14 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6435
  • Wer Ortho: 0.3797
  • Wer: 0.3499

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
4.995 1.0 29 2.9879 0.5425 0.4127
2.1634 2.0 58 0.8084 0.4382 0.3936
0.6659 3.0 87 0.6268 0.4144 0.3678
0.3865 4.0 116 0.5987 0.3880 0.3561
0.2428 5.0 145 0.6005 0.3990 0.3659
0.1734 6.0 174 0.6162 0.3906 0.3573
0.0965 7.0 203 0.6221 0.3893 0.3561
0.0682 8.0 232 0.6320 0.3803 0.3493
0.0473 9.0 261 0.6411 0.3797 0.3493
0.0476 10.0 290 0.6435 0.3797 0.3499

Framework versions

  • Transformers 4.30.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3