metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- wer
model-index:
- name: whisper-small_ro-80mel
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: generator
type: generator
config: default
split: None
args: default
metrics:
- name: Wer
type: wer
value: 2.6589
whisper-small_ro-80mel
This model is a fine-tuned version of openai/whisper-small on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 0.1116
- Wer: 2.6589
- Cer: 3.1776
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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 192
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1600
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.1771 | 1.8525 | 2000 | 0.1116 | 3.1124 | 3.2257 |
| 0.0966 | 3.7043 | 4000 | 0.0668 | 3.196 | 3.3828 |
| 0.0675 | 5.5560 | 6000 | 0.0635 | 3.7906 | 3.8308 |
"eval_runtime": 19430.1447, "eval_samples": 27174, "eval_samples_per_second": 1.399, "eval_steps_per_second": 0.117, "test_samples": 12987, "train_samples": 207181
Framework versions
- Transformers 4.57.0
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1