metadata

language:
  - eng
base_model: openai/whisper-small-2000
tags:
  - generated_from_trainer
datasets:
  - Kaggle/transcription_audio
metrics:
  - wer
model-index:
  - name: Whisper Small Eng - noursene
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: medical audio trascription
          type: Kaggle/transcription_audio
          args: 'config: eng'
        metrics:
          - name: Wer
            type: wer
            value: 10.536550234065539

Whisper Small Eng - noursene

This model is a fine-tuned version of openai/whisper-small-2000 on the medical audio trascription dataset. It achieves the following results on the evaluation set:

Loss: 0.1612
Wer: 10.5366

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.191	3.0257	500	0.2308	14.0727
0.0375	6.0514	1000	0.1570	10.9975
0.0045	9.0772	1500	0.1594	10.7598
0.0029	12.1029	2000	0.1612	10.5366

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1