---
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: whisper-large-clinical
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: None
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 5.21215221530679
---

# whisper-large-clinical

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on a private audiofolder dataset of 18.96 hours of clinical notes text data and corresponding synthetic audio generated by a TTS API.
It achieves the following results on the evaluation set:
- Loss: 0.2757
- Wer: 5.2122

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-06
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0143        | 9.0090  | 1000 | 0.2275          | 5.2605 |
| 0.0009        | 18.0180 | 2000 | 0.2468          | 5.1724 |
| 0.0003        | 27.0270 | 3000 | 0.2641          | 5.2548 |
| 0.0002        | 36.0360 | 4000 | 0.2728          | 5.2264 |
| 0.0002        | 45.0450 | 5000 | 0.2757          | 5.2122 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1