Instructions to use AleksanderObuchowski/whisper-large-v3-turbo-med-pl-lora-r64-enc-dec-lr2e-04-ep5-whisper_bigos20k_fair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AleksanderObuchowski/whisper-large-v3-turbo-med-pl-lora-r64-enc-dec-lr2e-04-ep5-whisper_bigos20k_fair with PEFT:
Task type is invalid.
- Transformers
How to use AleksanderObuchowski/whisper-large-v3-turbo-med-pl-lora-r64-enc-dec-lr2e-04-ep5-whisper_bigos20k_fair with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AleksanderObuchowski/whisper-large-v3-turbo-med-pl-lora-r64-enc-dec-lr2e-04-ep5-whisper_bigos20k_fair", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
whisper-large-v3-turbo-med-pl-lora-r64-enc-dec-lr2e-04-ep5-whisper_bigos20k_fair
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6192
- Model Preparation Time: 0.0248
- Wer: 13.3546
- Cer: 4.9427
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Wer | Cer |
|---|---|---|---|---|---|---|
| 1.6519 | 1.0 | 1827 | 1.6468 | 0.0248 | 13.8643 | 4.5834 |
| 1.6185 | 2.0 | 3654 | 1.6287 | 0.0248 | 15.2108 | 5.9196 |
| 1.5836 | 3.0 | 5481 | 1.6204 | 0.0248 | 13.8388 | 5.3899 |
| 1.5633 | 4.0 | 7308 | 1.6201 | 0.0248 | 13.4162 | 4.8643 |
| 1.543 | 5.0 | 9135 | 1.6192 | 0.0248 | 13.3546 | 4.9427 |
Framework versions
- PEFT 0.18.1
- Transformers 4.57.6
- Pytorch 2.8.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
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