Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Leonel-Maia/whisper-small-bible-splt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Leonel-Maia/whisper-small-bible-splt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Leonel-Maia/whisper-small-bible-splt")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Leonel-Maia/whisper-small-bible-splt") model = AutoModelForSpeechSeq2Seq.from_pretrained("Leonel-Maia/whisper-small-bible-splt") - Notebooks
- Google Colab
- Kaggle
whisper-small-bible-splt
This model is a fine-tuned version of openai/whisper-small on the Leonel-Maia/ewe_dataset_splitted dataset. It achieves the following results on the evaluation set:
- Loss: 0.2267
- Wer: 0.2095
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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use adamw_torch 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: 60.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3942 | 0.6033 | 500 | 0.3759 | 0.3573 |
| 0.2466 | 1.2075 | 1000 | 0.2798 | 0.2675 |
| 0.245 | 1.8109 | 1500 | 0.2472 | 0.2347 |
| 0.2 | 2.4151 | 2000 | 0.2358 | 0.2308 |
| 0.167 | 3.0193 | 2500 | 0.2292 | 0.2115 |
| 0.1541 | 3.6226 | 3000 | 0.2267 | 0.2095 |
| 0.1292 | 4.2268 | 3500 | 0.2319 | 0.2073 |
| 0.1247 | 4.8302 | 4000 | 0.2295 | 0.2097 |
| 0.0974 | 5.4344 | 4500 | 0.2396 | 0.2109 |
| 0.0707 | 6.0386 | 5000 | 0.2479 | 0.2072 |
| 0.0774 | 6.6419 | 5500 | 0.2554 | 0.2080 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for Leonel-Maia/whisper-small-bible-splt
Base model
openai/whisper-smallEvaluation results
- Wer on Leonel-Maia/ewe_dataset_splittedself-reported0.209