samolusola-personal
whisper-small-combined-lin-sna-lug-olusola
Available on FriendliAI
Dedicated EndpointsRun this model inference on single tenant GPU with unmatched speed and reliability at scale.
ContainerRun this model inference with full control and performance in your environment.
Model Details
Supported Functionality
Dedicated EndpointsContainer
README
License: apache-2.0
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
Training results
Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer, Cer| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|
| 1.2661 | 0.5020 | 500 | 0.6305 | 0.4220 | 0.1551 |
| 1.0202 | 1.0040 | 1000 | 0.5507 | 0.4619 | 0.1931 |
| 0.8280 | 1.5060 | 1500 | 0.5235 | 0.3643 | 0.1377 |
| 0.6772 | 2.0080 | 2000 | 0.5089 | 0.3957 | 0.1495 |
| 0.6185 | 2.5100 | 2500 | 0.5049 | 0.3441 | 0.1230 |
| 0.4801 | 3.0120 | 3000 | 0.5298 | 0.3308 | 0.1152 |
| 0.4279 | 3.5141 | 3500 | 0.5320 | 0.3468 | 0.1252 |
| 0.3562 | 4.0161 | 4000 | 0.5546 | 0.3443 | 0.1257 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2