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