asr2_aug_IT_v4 / README.md
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metadata
library_name: peft
language:
  - it
license: apache-2.0
base_model: miosipof/whisper-medium-it-luigisaetta
tags:
  - generated_from_trainer
datasets:
  - b-brave-balanced-augmented
metrics:
  - wer
model-index:
  - name: Whisper Medium IT
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: b-brave-balanced-augmented
          type: b-brave-balanced-augmented
        metrics:
          - type: wer
            value: 33.11081441922563
            name: Wer

Whisper Medium IT

This model is a fine-tuned version of openai/whisper-medium on the b-brave-balanced-augmented dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2996
  • Wer: 33.1108
  • Cer: 21.3778
  • Lr: 0.0000

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: 0.0003
  • train_batch_size: 50
  • eval_batch_size: 50
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 100
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.3
  • num_epochs: 4
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer Lr
12.0253 1.0 83 1.0195 72.8972 40.7434 0.0002
1.5523 2.0 166 0.5319 50.0668 31.6288 0.0002
1.0161 3.0 249 0.3637 40.1869 25.7102 0.0001
0.5605 4.0 332 0.2996 33.1108 21.3778 0.0000

Framework versions

  • PEFT 0.15.1
  • Transformers 4.47.1
  • Pytorch 2.2.0
  • Datasets 3.5.0
  • Tokenizers 0.21.1