metadata
library_name: transformers
language:
- eu
license: apache-2.0
base_model: openai/whisper-large
tags:
- whisper-event
- generated_from_trainer
datasets:
- common_voice_21_0_eu
metrics:
- wer
model-index:
- name: Whisper Large Basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_21_0_eu
type: common_voice_21_0_eu
config: default
split: test
args: default
metrics:
- name: Wer
type: wer
value: 9.7643447952973
Whisper Large Basque
This model is a fine-tuned version of openai/whisper-large on the common_voice_21_0_eu dataset. It achieves the following results on the evaluation set:
- Loss: 0.2974
- Wer: 9.7643
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: 3.75e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 100000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0114 | 11.1112 | 5000 | 0.2477 | 10.4770 |
| 0.0052 | 22.2225 | 10000 | 0.2739 | 9.9325 |
| 0.0043 | 33.3337 | 15000 | 0.2855 | 9.8441 |
| 0.0034 | 44.4449 | 20000 | 0.2974 | 9.7643 |
| 0.0031 | 55.5562 | 25000 | 0.3101 | 10.1424 |
| 0.0034 | 66.6674 | 30000 | 0.3182 | 10.3530 |
| 0.0026 | 77.7786 | 35000 | 0.3195 | 10.3609 |
| 0.0032 | 88.8899 | 40000 | 0.3279 | 10.4146 |
| 0.0022 | 100.0 | 45000 | 0.3253 | 9.9915 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1