metadata
language:
- eu
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Medium 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: 14.112716355356747
Whisper Medium Basque
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set:
- Loss: 0.3985
- Wer: 14.1127
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: 64
- eval_batch_size: 32
- 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: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1127 | 5.85 | 1000 | 0.2776 | 17.5623 |
| 0.0225 | 11.7 | 2000 | 0.3129 | 15.6320 |
| 0.0074 | 17.54 | 3000 | 0.3277 | 14.9530 |
| 0.0041 | 23.39 | 4000 | 0.3551 | 14.8018 |
| 0.0032 | 29.24 | 5000 | 0.3698 | 14.6245 |
| 0.0019 | 35.09 | 6000 | 0.3877 | 14.6084 |
| 0.0014 | 40.94 | 7000 | 0.3891 | 14.4976 |
| 0.0008 | 46.78 | 8000 | 0.3946 | 14.2759 |
| 0.0007 | 52.63 | 9000 | 0.3987 | 14.3182 |
| 0.0005 | 58.48 | 10000 | 0.3985 | 14.1127 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1