metadata
library_name: transformers
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- DereAbdulhameed/Pharma-Speak
metrics:
- wer
model-index:
- name: 'Whisper Small Medication Corpus '
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Pharma-Speak
type: DereAbdulhameed/Pharma-Speak
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 20
Whisper Small Medication Corpus
This model is a fine-tuned version of openai/whisper-small on the Pharma-Speak dataset. It achieves the following results on the evaluation set:
- Loss: 0.6189
- Wer: 20.0
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: 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0 | 500.0 | 1000 | 0.5205 | 18.6047 |
| 0.0 | 1000.0 | 2000 | 0.5735 | 20.9302 |
| 0.0 | 1500.0 | 3000 | 0.6033 | 21.8605 |
| 0.0 | 2000.0 | 4000 | 0.6189 | 20.0 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1