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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: 6.286946013912929
---
<!-- 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: 0.1269
- Wer: 6.2869
## 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: 18
- 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: 1100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.2361 | 0.2825 | 100 | 1.0425 | 10.4870 |
| 0.6631 | 0.5650 | 200 | 0.6451 | 9.4908 |
| 0.419 | 0.8475 | 300 | 0.3854 | 8.5535 |
| 0.1538 | 1.1299 | 400 | 0.1895 | 7.2635 |
| 0.1234 | 1.4124 | 500 | 0.1644 | 6.8454 |
| 0.1134 | 1.6949 | 600 | 0.1470 | 6.6201 |
| 0.1071 | 1.9774 | 700 | 0.1358 | 6.0289 |
| 0.0721 | 2.2599 | 800 | 0.1329 | 6.1302 |
| 0.0693 | 2.5424 | 900 | 0.1299 | 6.3065 |
| 0.0635 | 2.8249 | 1000 | 0.1275 | 6.5025 |
| 0.0441 | 3.1073 | 1100 | 0.1269 | 6.2869 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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