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whisper-medium-eu

Automatic Speech Recognition
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Basque
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whisper-event
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whisper-medium-eu / README.md
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metadata
language:
  - eu
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
base_model: openai/whisper-medium
model-index:
  - name: Whisper Small Basque
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_13_0 eu
          type: mozilla-foundation/common_voice_13_0
          config: eu
          split: test
          args: eu
        metrics:
          - type: wer
            value: 12.839726193851513
            name: Wer

Whisper Small Basque

This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2287
  • Wer: 12.8397

If you need to use this model with whisper.cpp, you can download the ggml file: ggml-medium-eu.bin

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 8000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.4415 0.06 500 0.5092 36.9699
0.4206 0.12 1000 0.4144 28.3365
0.272 0.19 1500 0.3554 24.7438
0.2681 0.25 2000 0.3271 22.1414
0.2099 0.31 2500 0.2973 19.5350
0.2283 0.38 3000 0.2760 18.5042
0.1477 1.03 3500 0.2637 17.1493
0.1008 1.09 4000 0.2592 16.3939
0.0866 1.15 4500 0.2561 15.8066
0.0915 1.21 5000 0.2411 15.0310
0.0803 1.28 5500 0.2330 14.7616
0.0674 1.34 6000 0.2325 13.8462
0.0679 1.4 6500 0.2299 13.5809
0.027 2.05 7000 0.2304 13.3805
0.0231 2.11 7500 0.2287 12.8397
0.0285 2.18 8000 0.2304 12.8883

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.1+cu117
  • Datasets 2.8.1.dev0
  • Tokenizers 0.13.2