Instructions to use lejonck/xlsr53-ptbr-mupe-final2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lejonck/xlsr53-ptbr-mupe-final2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lejonck/xlsr53-ptbr-mupe-final2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lejonck/xlsr53-ptbr-mupe-final2") model = AutoModelForCTC.from_pretrained("lejonck/xlsr53-ptbr-mupe-final2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlsr53-ptbr-mupe-final2
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6446
- Wer: 0.5318
- Cer: 0.2984
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.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: 100
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.3239 | 1.0 | 1500 | 1.4519 | 0.5926 | 0.3194 |
| 1.2579 | 2.0 | 3000 | 1.5037 | 0.5830 | 0.3204 |
| 1.0581 | 3.0 | 4500 | 1.5501 | 0.5754 | 0.3114 |
| 0.7861 | 4.0 | 6000 | 1.5535 | 0.5662 | 0.3092 |
| 0.7467 | 5.0 | 7500 | 1.5458 | 0.5518 | 0.3066 |
| 1.3773 | 6.0 | 9000 | 1.6330 | 0.5534 | 0.3023 |
| 0.8379 | 7.0 | 10500 | 1.6314 | 0.5478 | 0.2960 |
| 1.0531 | 8.0 | 12000 | 1.6187 | 0.5502 | 0.3027 |
| 0.8891 | 9.0 | 13500 | 1.6227 | 0.5386 | 0.3004 |
| 0.7721 | 10.0 | 15000 | 1.6331 | 0.5346 | 0.3001 |
| 0.8561 | 11.0 | 16500 | 1.6355 | 0.5318 | 0.2988 |
| 0.3882 | 12.0 | 18000 | 1.6446 | 0.5306 | 0.2982 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
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