metadata
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 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