metadata

base_model: openai/whisper-small
datasets:
  - lord-reso/inbrowser-proctor-dataset
language:
  - en
library_name: peft
license: apache-2.0
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: Whisper-Small-Inbrowser-Proctor-LORA
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Inbrowser Procotor Dataset
          type: lord-reso/inbrowser-proctor-dataset
          args: 'config: en, split: test'
        metrics:
          - type: wer
            value: 18.158649251353935
            name: Wer

Whisper-Small-Inbrowser-Proctor-LORA

This model is a fine-tuned version of openai/whisper-small on the Inbrowser Procotor Dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.3646
Wer: 18.1586

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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 250
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.7817	0.8929	25	0.7456	31.6502
0.3905	1.7857	50	0.4646	29.4043
0.2194	2.6786	75	0.3988	20.3090
0.1697	3.5714	100	0.3776	16.1357
0.1246	4.4643	125	0.3744	18.7639
0.1062	5.3571	150	0.3698	19.9267
0.0862	6.25	175	0.3698	19.9108
0.0701	7.1429	200	0.3651	18.0153
0.0647	8.0357	225	0.3659	18.4613
0.056	8.9286	250	0.3646	18.1586

Framework versions

PEFT 0.12.1.dev0
Transformers 4.45.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1