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whisper-tiny-lb

Automatic Speech Recognition
Transformers
PyTorch
Luxembourgish
whisper
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
language:
  - lb
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper tiny LB - AKABI
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: lb_lu
          split: test
          args: lb_lu
        metrics:
          - name: Wer
            type: wer
            value: 60.18671593892832

Whisper tiny LB - AKABI

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

  • Loss: 1.4215
  • Wer Ortho: 62.8649
  • Wer: 60.1867

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
  • training_steps: 4000

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.9979 1.37 250 1.5394 73.1448 73.3298
0.6784 2.75 500 1.2998 66.9095 64.8060
0.3773 4.12 750 1.2317 63.9250 61.5385
0.2906 5.49 1000 1.2117 63.0759 60.3958
0.2052 6.87 1250 1.2157 64.1913 62.0685
0.1155 8.24 1500 1.2432 61.6791 59.6130
0.0912 9.62 1750 1.2684 63.0056 60.3229
0.0698 10.99 2000 1.2937 63.6788 60.9598
0.0396 12.36 2250 1.3224 62.7996 60.2451
0.0309 13.74 2500 1.3480 62.1514 59.4622
0.0205 15.11 2750 1.3696 62.1715 59.5303
0.017 16.48 3000 1.3895 62.0761 59.8074
0.0151 17.86 3250 1.4016 62.7745 60.0360
0.0125 19.23 3500 1.4126 62.8900 60.5952
0.012 20.6 3750 1.4202 63.0709 60.3909
0.0115 21.98 4000 1.4215 62.8649 60.1867

Framework versions

  • Transformers 4.32.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3