Instructions to use lejonck/xlsr53-coraa-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lejonck/xlsr53-coraa-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lejonck/xlsr53-coraa-3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lejonck/xlsr53-coraa-3") model = AutoModelForCTC.from_pretrained("lejonck/xlsr53-coraa-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlsr53-coraa-3
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1673
- Wer: 0.4168
- Cer: 0.1949
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 |
|---|---|---|---|---|---|
| 1.1566 | 1.0 | 4000 | 1.0638 | 0.4452 | 0.2119 |
| 1.0404 | 2.0 | 8000 | 1.0209 | 0.4344 | 0.2078 |
| 1.2722 | 3.0 | 12000 | 1.1016 | 0.4304 | 0.2054 |
| 0.8781 | 4.0 | 16000 | 1.1164 | 0.4258 | 0.2012 |
| 0.8323 | 5.0 | 20000 | 1.0932 | 0.4248 | 0.2007 |
| 0.8823 | 6.0 | 24000 | 1.1292 | 0.4234 | 0.2017 |
| 1.01 | 7.0 | 28000 | 1.1186 | 0.4171 | 0.1981 |
| 0.8778 | 8.0 | 32000 | 1.1122 | 0.4183 | 0.1973 |
| 1.1185 | 9.0 | 36000 | 1.1264 | 0.4180 | 0.1972 |
| 0.6096 | 10.0 | 40000 | 1.1972 | 0.4180 | 0.1961 |
| 0.7837 | 11.0 | 44000 | 1.1673 | 0.4170 | 0.1949 |
| 0.7726 | 12.0 | 48000 | 1.1752 | 0.4176 | 0.1947 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.7.0+cu126
- Datasets 2.19.1
- Tokenizers 0.21.4
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