metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- swagen
metrics:
- wer
model-index:
- name: whisper-medium-swagen-combined-10hrs-model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: swagen
type: swagen
metrics:
- name: Wer
type: wer
value: 0.3075245365321701
whisper-medium-swagen-combined-10hrs-model
This model is a fine-tuned version of openai/whisper-medium on the swagen dataset. It achieves the following results on the evaluation set:
- Loss: 0.4674
- Wer: 0.3075
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.6418 | 0.2385 | 200 | 0.8312 | 0.4835 |
| 1.9381 | 0.4769 | 400 | 0.6574 | 0.4050 |
| 1.7528 | 0.7154 | 600 | 0.5706 | 0.3545 |
| 1.7421 | 0.9538 | 800 | 0.5140 | 0.3504 |
| 0.9259 | 1.1931 | 1000 | 0.5175 | 0.3262 |
| 0.8254 | 1.4316 | 1200 | 0.4978 | 0.3096 |
| 0.9616 | 1.6700 | 1400 | 0.4755 | 0.2999 |
| 0.7893 | 1.9085 | 1600 | 0.4674 | 0.3075 |
| 0.3547 | 2.1478 | 1800 | 0.4848 | 0.3158 |
| 0.3568 | 2.3863 | 2000 | 0.4913 | 0.2616 |
| 0.3759 | 2.6247 | 2200 | 0.4685 | 0.3104 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0