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metadata
language:
  - en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - generated_from_trainer
datasets:
  - PolyAI/minds14
metrics:
  - wer
model-index:
  - name: fine-tuned-Whisper-Tiny-en-US
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: minds14 - en(US)
          type: PolyAI/minds14
          config: en-US
          split: train
          args: 'config: en-US, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.3247210804462713

fine-tuned-Whisper-Tiny-en-US

This model is a fine-tuned version of openai/whisper-tiny on the minds14 - en(US) dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7793
  • Wer Ortho: 0.3222
  • Wer: 0.3247

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: constant_with_warmup
  • lr_scheduler_warmup_steps: 400
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.0014 17.24 500 0.5901 0.3210 0.3188
0.0003 34.48 1000 0.6579 0.3124 0.3142
0.0002 51.72 1500 0.6892 0.3143 0.3165
0.0001 68.97 2000 0.7129 0.3167 0.3194
0.0001 86.21 2500 0.7330 0.3179 0.3206
0.0 103.45 3000 0.7511 0.3191 0.3218
0.0 120.69 3500 0.7653 0.3179 0.3206
0.0 137.93 4000 0.7793 0.3222 0.3247

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2