whisper-large-v2-ca / README.md
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metadata
language:
  - ca
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: Whisper Large-V2 Catalan
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_13_0 ca
          type: mozilla-foundation/common_voice_13_0
          config: ca
          split: test
          args: ca
        metrics:
          - name: Wer
            type: wer
            value: 4.671620462989425

Whisper Large-V2 Catalan

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_13_0 ca dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1494
  • Wer: 4.6716

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: 32
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • 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
0.1072 1.02 1000 0.1637 7.0329
0.0239 3.02 2000 0.1784 7.0277
0.0507 5.02 3000 0.1754 6.5773
0.0571 7.02 4000 0.1620 6.5047
0.0193 9.02 5000 0.1821 6.4887
0.0625 11.02 6000 0.1443 6.7585
0.0752 13.02 7000 0.1653 5.9097
0.0359 15.02 8000 0.1406 5.8760
0.0565 17.01 9000 0.1496 5.9680
0.0196 19.01 10000 0.1788 5.2746
0.0215 21.01 11000 0.1539 5.3895
0.0178 23.01 12000 0.1800 5.3764
0.0114 25.01 13000 0.1709 5.2078
0.0123 27.01 14000 0.1827 5.2003
0.0337 29.01 15000 0.1553 5.3655
0.0108 31.01 16000 0.1476 4.9151
0.0194 33.01 17000 0.1396 4.8477
0.0472 35.0 18000 0.1202 4.8717
0.0401 37.0 19000 0.1494 4.6716
0.0127 39.0 20000 0.1187 4.7276

Framework versions

  • Transformers 4.33.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3