whisper-small-sc / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
model-index:
  - name: whisper-small-sc
    results: []
datasets:
  - janaab/supreme-court-speech
language:
  - en
metrics:
  - wer
pipeline_tag: automatic-speech-recognition

whisper-small-sc

This model is a fine-tuned version of openai/whisper-small on janaab/supreme-court-speech dataset. It achieves the following results on the evaluation set:

  • eval_loss: 0.3354
  • eval_wer_ortho: 11.0780
  • eval_wer: 10.5653
  • eval_runtime: 1059.0881
  • eval_samples_per_second: 4.216
  • eval_steps_per_second: 0.264
  • epoch: 6.9337
  • step: 2250

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: 5e-06
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_steps: 50
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1