metadata

library_name: transformers
language:
  - en
license: apache-2.0
base_model: openai/whisper-base
tags:
  - wft
  - whisper
  - automatic-speech-recognition
  - audio
  - speech
  - generated_from_trainer
datasets:
  - Shamus/multimed_short
metrics:
  - wer
model-index:
  - name: whisper-BASE-LORA-med
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Shamus/multimed_short
          type: Shamus/multimed_short
        metrics:
          - type: wer
            value: 21.47240659965864
            name: Wer

whisper-BASE-LORA-med

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

Loss: 0.5848
Wer: 21.4724
Cer: 12.0240

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: 0.0005
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.2829	1.0	7077	0.6521	24.4961	14.2032
0.8621	2.0	14154	0.6147	24.9126	14.5952
0.3164	3.0	21231	0.5794	22.2598	12.4891
0.3039	4.0	28308	0.5678	22.0960	12.5164
0.5835	5.0	35385	0.5848	21.4724	12.0240

Framework versions

PEFT 0.15.2
Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1