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whisper-sm-el-intlv-xl

Automatic Speech Recognition
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PyTorch
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Greek
whisper
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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-sm-el-intlv-xl
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0
          type: mozilla-foundation/common_voice_11_0
          config: el
          split: test
        metrics:
          - name: Wer
            type: wer
            value: 19.48365527488856

whisper-sm-el-intlv-xl

This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_11_0 (el) and the google/fleurs (el_gr) datasets. It achieves the following results on the evaluation set:

  • Loss: 0.4725
  • Wer: 19.4837

Model description

The model was trained over 10000 steps on translation from Greek to English.

Intended uses & limitations

This model was part of the Whisper Finetuning Event (Dec 2022) and was used primarily to compare relative improvements between transcription and translation tasks.

Training and evaluation data

The training datasets combined examples from both train and evaluation splits and use the train split of the mozilla-foundation/common_voice_11_0 (el) dataset for evaluation and selection of the best checkpoint.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 8.5e-06
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0545 2.49 1000 0.2891 22.4926
0.0093 4.98 2000 0.3927 20.1337
0.0018 7.46 3000 0.4031 20.1616
0.001 9.95 4000 0.4209 19.6880
0.0008 12.44 5000 0.4498 20.0966
0.0005 14.93 6000 0.4725 19.4837
0.0002 17.41 7000 0.4917 19.5951
0.0001 19.9 8000 0.5050 19.6230
0.0001 22.39 9000 0.5146 19.5672
0.0001 24.88 10000 0.5186 19.4837

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0
  • Datasets 2.7.1.dev0
  • Tokenizers 0.12.1