metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- common_voice_22_0
metrics:
- wer
model-index:
- name: openai/whisper-large
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_22_0
type: common_voice_22_0
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 11.503482317939008
openai/whisper-large
This model is a fine-tuned version of openai/whisper-large on the common_voice_22_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3446
- Wer: 11.5035
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 |
|---|---|---|---|---|
| 0.0153 | 10.8234 | 5000 | 0.2641 | 10.2881 |
| 0.0077 | 21.6457 | 10000 | 0.2892 | 10.5315 |
| 0.0054 | 32.4680 | 15000 | 0.3138 | 10.5839 |
| 0.0058 | 43.2904 | 20000 | 0.3198 | 11.0335 |
| 0.003 | 54.1127 | 25000 | 0.3352 | 10.7326 |
| 0.0047 | 64.9361 | 30000 | 0.3446 | 11.5035 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1