---
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- Dev372/Medical_STT_Dataset_1.1
metrics:
- wer
model-index:
- name: OutcomesAI-Whisper-tiny-v1.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Medical_STT_Dataset_1.1
      type: Dev372/Medical_STT_Dataset_1.1
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 7.224272510532676
---

<!-- 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. -->

# OutcomesAI-Whisper-tiny-v1.0

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Medical_STT_Dataset_1.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1675
- Wer: 7.2243

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.1067        | 2.5126  | 1000 | 0.1600          | 7.2308 |
| 0.0329        | 5.0251  | 2000 | 0.1479          | 6.5809 |
| 0.0131        | 7.5377  | 3000 | 0.1596          | 7.4104 |
| 0.0192        | 10.0503 | 4000 | 0.1675          | 7.2243 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1