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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- yashtiwari/PaulMooney-Medical-ASR-Data
metrics:
- wer
model-index:
- name: Whisper Medium Medical
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Medical ASR
type: yashtiwari/PaulMooney-Medical-ASR-Data
metrics:
- name: Wer
type: wer
value: 12.406468742457157
---
<!-- 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. -->
# Whisper Medium Medical
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Medical ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0995
- Wer: 12.4065
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.7532 | 0.1357 | 100 | 0.2695 | 13.0099 |
| 0.2155 | 0.2714 | 200 | 0.2053 | 9.8238 |
| 0.2392 | 0.4071 | 300 | 0.1567 | 8.9549 |
| 0.151 | 0.5427 | 400 | 0.1159 | 6.9273 |
| 0.1439 | 0.6784 | 500 | 0.0995 | 12.4065 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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