metadata
library_name: transformers
language:
- eu
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- whisper-event
- generated_from_trainer
datasets:
- common_voice_21_0_eu
metrics:
- wer
model-index:
- name: Whisper Large-V2 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: 8.682307652291525
Whisper Large-V2 Basque
This model is a fine-tuned version of openai/whisper-large-v2 on the common_voice_21_0_eu dataset. It achieves the following results on the evaluation set:
- Loss: 0.2048
- Wer: 8.6823
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 |
|---|---|---|---|---|
| 0.0086 | 11.1112 | 5000 | 0.2048 | 8.6823 |
| 0.0049 | 22.2225 | 10000 | 0.2296 | 9.1852 |
| 0.0026 | 33.3337 | 15000 | 0.2459 | 9.0196 |
| 0.004 | 44.4449 | 20000 | 0.2476 | 9.1453 |
| 0.0029 | 55.5562 | 25000 | 0.2631 | 9.7765 |
| 0.0017 | 66.6674 | 30000 | 0.2687 | 9.0057 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1