metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- bsbigcgen
metrics:
- wer
model-index:
- name: whisper-medium-bsbigcgen-combined-model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: bsbigcgen
type: bsbigcgen
metrics:
- name: Wer
type: wer
value: 0.46739257698161807
whisper-medium-bsbigcgen-combined-model
This model is a fine-tuned version of openai/whisper-medium on the bsbigcgen dataset. It achieves the following results on the evaluation set:
- Loss: 0.5866
- Wer: 0.4674
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 |
|---|---|---|---|---|
| 4.2954 | 0.3146 | 200 | 0.9741 | 0.6849 |
| 3.2141 | 0.6292 | 400 | 0.7601 | 0.5826 |
| 2.7215 | 0.9438 | 600 | 0.6713 | 0.4974 |
| 2.1639 | 1.2595 | 800 | 0.6447 | 0.5168 |
| 1.9576 | 1.5741 | 1000 | 0.6139 | 0.4731 |
| 2.0274 | 1.8887 | 1200 | 0.5895 | 0.4721 |
| 1.1246 | 2.2045 | 1400 | 0.5959 | 0.4556 |
| 1.1317 | 2.5191 | 1600 | 0.6155 | 0.4656 |
| 1.0119 | 2.8337 | 1800 | 0.5866 | 0.4674 |
| 0.7011 | 3.1494 | 2000 | 0.6415 | 0.4684 |
| 0.5972 | 3.4640 | 2200 | 0.6469 | 0.4687 |
| 0.6218 | 3.7786 | 2400 | 0.6497 | 0.4226 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0