metadata
language:
- gl
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small gl
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: gl
split: test
args: gl
metrics:
- name: Wer
type: wer
value: 15.689798175283384
pipeline_tag: automatic-speech-recognition
Whisper Small gl
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3490
- Wer: 15.6898
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: 8
- 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 |
|---|---|---|---|---|
| 0.1839 | 1.8182 | 1000 | 0.2678 | 14.0494 |
| 0.0733 | 3.6364 | 2000 | 0.2725 | 13.9146 |
| 0.0356 | 5.4545 | 3000 | 0.3014 | 15.0055 |
| 0.0194 | 7.2727 | 4000 | 0.3319 | 14.6196 |
| 0.0141 | 9.0909 | 5000 | 0.3490 | 15.6898 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1