metadata
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Base Tagalog
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs fil_ph
type: google/fleurs
config: fil_ph
split: test
args: fil_ph
metrics:
- name: Wer
type: wer
value: 30.810565352304547
Whisper Base Tagalog
This model is a fine-tuned version of openai/whisper-base on the google/fleurs fil_ph dataset. It achieves the following results on the evaluation set:
- Loss: 0.7222
- Wer: 30.8106
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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 0.5804 | 38.0 | 500 | 0.7836 | 36.0478 |
| 0.1934 | 76.0 | 1000 | 0.6861 | 31.5220 |
| 0.0589 | 115.0 | 1500 | 0.7040 | 32.4415 |
| 0.0251 | 153.0 | 2000 | 0.7222 | 30.8106 |
| 0.0154 | 192.0 | 2500 | 0.7362 | 31.3593 |
| 0.0109 | 230.0 | 3000 | 0.7470 | 31.7604 |
| 0.0085 | 269.0 | 3500 | 0.7562 | 31.7112 |
| 0.0071 | 307.0 | 4000 | 0.7630 | 31.9874 |
| 0.0064 | 346.0 | 4500 | 0.7675 | 32.0064 |
| 0.0061 | 384.0 | 5000 | 0.7692 | 32.0669 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0