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