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: test
args: eu
metrics:
- name: Wer
type: wer
value: 25.977155818380655
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: 0.5520
- Wer: 25.9772
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0174 | 9.01 | 1000 | 0.4597 | 27.3097 |
| 0.0016 | 19.01 | 2000 | 0.5160 | 26.0197 |
| 0.0007 | 29.0 | 3000 | 0.5520 | 25.9772 |
| 0.0005 | 38.02 | 4000 | 0.5728 | 26.1452 |
| 0.0004 | 48.01 | 5000 | 0.5818 | 26.2202 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3