metadata
library_name: transformers
language:
- sr
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- espnet/yodas
metrics:
- wer
model-index:
- name: Whisper Large v3 Turbo Sr Test
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Yodas
type: espnet/yodas
config: sr
split: None
args: sr
metrics:
- name: Wer
type: wer
value: 0.1377668019050979
Whisper Large v3 Turbo Sr Test
This model is in test phase DO NOT USE IT ... YET
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Yodas dataset. It achieves the following results on the evaluation set:
- Loss: 0.1195
- Wer: 0.1378
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: 16
- 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.6455 | 0.2439 | 500 | 0.1869 | 0.1928 |
| 0.5858 | 0.4878 | 1000 | 0.1694 | 0.1870 |
| 0.5608 | 0.7317 | 1500 | 0.1507 | 0.1641 |
| 0.4547 | 0.9756 | 2000 | 0.1388 | 0.1542 |
| 0.3905 | 1.2195 | 2500 | 0.1341 | 0.1461 |
| 0.3857 | 1.4634 | 3000 | 0.1291 | 0.1450 |
| 0.3656 | 1.7073 | 3500 | 0.1243 | 0.1415 |
| 0.3369 | 1.9512 | 4000 | 0.1195 | 0.1378 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.20.3