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whisper-sm-farsipal-e5

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Greek
whisper
whisper-event
Generated from Trainer
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metadata
language:
  - el
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0,google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper small Greek Farsipal and  El Greco
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr
          type: mozilla-foundation/common_voice_11_0,google/fleurs
          config: el
          split: None
        metrics:
          - name: Wer
            type: wer
            value: 17.199108469539375

Whisper small Greek Farsioal and El Greco

This model is a fine-tuned version of emilios/whisper-sm-el-farsipal-e4 on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4871
  • Wer: 17.1991

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-06
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 20000

Training results

Training Loss Epoch Step Validation Loss Wer
0.1259 2.49 1000 0.4834 18.3692
0.1002 4.49 2000 0.4604 17.8027
0.1096 6.98 3000 0.4553 17.8770
0.0885 9.46 4000 0.4551 17.9606
0.0675 11.95 5000 0.4631 17.9049
0.0675 14.44 6000 0.4619 17.9049
0.0645 16.93 7000 0.4678 17.6727
0.0535 19.41 8000 0.4685 17.6634
0.039 21.49 9000 0.4746 17.6727
0.0447 23.98 10000 0.4761 17.6634
0.0393 26.46 11000 0.4792 17.7656
0.0308 28.95 12000 0.4851 17.8678
0.0301 31.44 13000 0.4846 17.4499
0.031 33.93 14000 0.4849 17.8306
0.0263 36.41 15000 0.4880 17.6170
0.0256 38.9 16000 0.4871 17.1991
0.0236 41.39 17000 0.4883 17.2641
0.0195 43.88 18000 0.4880 17.5706
0.0193 46.36 19000 0.4993 17.7285
0.0161 48.85 20000 0.4968 17.8306

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 2.0.0.dev20221216+cu116
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2