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---
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny.en
tags:
- generated_from_trainer
datasets:
- Dev372/Medical_STT_Dataset_1.1
metrics:
- wer
model-index:
- name: English Whisper Model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Medical
type: Dev372/Medical_STT_Dataset_1.1
args: 'split: test'
metrics:
- name: Wer
type: wer
value: 6.580881152225743
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# English Whisper Model
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the Medical dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1386
- Wer: 6.5809
## 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: 36
- 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: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.1731 | 0.5650 | 100 | 0.9844 | 10.2812 |
| 0.6483 | 1.1299 | 200 | 0.6288 | 9.3047 |
| 0.3802 | 1.6949 | 300 | 0.3554 | 7.8938 |
| 0.1437 | 2.2599 | 400 | 0.1702 | 7.1230 |
| 0.1136 | 2.8249 | 500 | 0.1415 | 6.5841 |
| 0.0752 | 3.3898 | 600 | 0.1336 | 6.0616 |
| 0.0713 | 3.9548 | 700 | 0.1257 | 6.1236 |
| 0.0373 | 4.5198 | 800 | 0.1279 | 5.8526 |
| 0.0311 | 5.0847 | 900 | 0.1283 | 5.8003 |
| 0.03 | 5.6497 | 1000 | 0.1303 | 6.1171 |
| 0.0166 | 6.2147 | 1100 | 0.1314 | 6.0845 |
| 0.0241 | 6.7797 | 1200 | 0.1339 | 6.3588 |
| 0.0164 | 7.3446 | 1300 | 0.1368 | 6.3555 |
| 0.0178 | 7.9096 | 1400 | 0.1380 | 6.4764 |
| 0.0099 | 8.4746 | 1500 | 0.1386 | 6.5809 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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