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UDA-LIDI-Whisper-large-v3-ECU-911

Automatic Speech Recognition
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Safetensors
whisper
Generated from Trainer
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UDA-LIDI-Whisper-large-v3-ECU-911 / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: UDA-LIDI-Whisper-large-v3-ECU-911
    results: []

UDA-LIDI-Whisper-large-v3-ECU-911

This model is a fine-tuned version of openai/whisper-large-v3 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8777
  • Wer: 37.9051

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: constant
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.6583 1.0 91 0.5713 39.8617
0.3725 2.0 182 0.5667 37.7866
0.2317 3.0 273 0.6098 37.6285
0.1397 4.0 364 0.6432 37.1937
0.0841 5.0 455 0.7177 39.4466
0.0539 6.0 546 0.7817 39.1700
0.036 7.0 637 0.8725 38.7747
0.0281 8.0 728 0.8485 39.6245
0.0228 9.0 819 0.8553 37.9051
0.0181 9.8950 900 0.8777 37.9051

Framework versions

  • Transformers 4.47.0
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0