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Librarian Bot: Add base_model information to model (#2)
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
base_model: openai/whisper-large-v2
model-index:
  - name: whisper-large-v2-spanish
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_13_0
          type: common_voice_13_0
          config: es
          split: test
          args: es
        metrics:
          - type: wer
            value: 0.09930265529872913
            name: Wer

whisper-large-v2-spanish

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

  • Loss: 0.2414
  • Wer Ortho: 0.1439
  • Wer: 0.0993

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: 3e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.2074 1.0 1752 0.2511 0.1628 0.1211
0.1323 2.0 3504 0.2414 0.1439 0.0993

Framework versions

  • Transformers 4.30.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3