README.md

metadata

library_name: transformers
language:
  - aeb
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - AT_ENT
metrics:
  - wer
model-index:
  - name: Whisper medium AT
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: AT_ENT
          type: AT_ENT
          args: 'config: aeb, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 66.82967723906664

Whisper medium AT

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

• Loss: 1.2100
• Wer: 66.8297

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: 32
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	1.0	111	1.0527	63.9374
No log	2.0	222	1.0961	65.8796
No log	3.0	333	1.1626	67.5283
No log	4.0	444	1.2100	66.8297

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.0