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whisper_finetune

Automatic Speech Recognition
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metadata
library_name: transformers
language:
  - eng
license: mit
base_model: distil-whisper/distil-small.en
tags:
  - generated_from_trainer
datasets:
  - sabooor345/stt
model-index:
  - name: Distiled Whisper Finetune
    results: []

Distiled Whisper Finetune

This model is a fine-tuned version of distil-whisper/distil-small.en on the STT dataset.

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: 16
  • eval_batch_size: 8
  • seed: 42
  • 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_steps: 500
  • training_steps: 600
  • mixed_precision_training: Native AMP

Training results

Framework versions

  • Transformers 4.46.1
  • Pytorch 2.5.0+cu121
  • Datasets 3.0.2
  • Tokenizers 0.20.1