metadata
language:
- eu
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Base Basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_13_0 eu
type: mozilla-foundation/common_voice_13_0
config: eu
split: validation
args: eu
metrics:
- name: Wer
type: wer
value: 26.59332245259828
Whisper Base Basque
This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set:
- Loss: 0.5071
- Wer: 26.5933
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: 2.5e-05
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|---|---|---|---|---|
| 0.3263 | 11.63 | 1000 | 0.5019 | 33.1701 |
| 0.1194 | 23.26 | 2000 | 0.4647 | 28.2859 |
| 0.0576 | 34.88 | 3000 | 0.4863 | 27.2401 |
| 0.0337 | 46.51 | 4000 | 0.5028 | 26.6135 |
| 0.0276 | 58.14 | 5000 | 0.5071 | 26.5933 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1