metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- bigcgen
metrics:
- wer
model-index:
- name: whisper-medium-bigcgen-baseline-42
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: bigcgen
type: bigcgen
metrics:
- name: Wer
type: wer
value: 0.526129108536297
whisper-medium-bigcgen-baseline-42
This model is a fine-tuned version of openai/whisper-medium on the bigcgen dataset. It achieves the following results on the evaluation set:
- Loss: 0.6970
- Wer: 0.5261
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 OptimizerNames.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
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0745 | 0.6102 | 200 | 0.9366 | 0.6486 |
| 0.6532 | 1.2197 | 400 | 0.7690 | 0.5467 |
| 0.6347 | 1.8299 | 600 | 0.7060 | 0.5129 |
| 0.4066 | 2.4394 | 800 | 0.6970 | 0.5261 |
| 0.2542 | 3.0488 | 1000 | 0.7140 | 0.5034 |
| 0.252 | 3.6590 | 1200 | 0.7221 | 0.4833 |
| 0.137 | 4.2685 | 1400 | 0.7573 | 0.4878 |
| 0.114 | 4.8787 | 1600 | 0.7934 | 0.4812 |
Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0