Instructions to use pnparam/loso_M12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pnparam/loso_M12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pnparam/loso_M12")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("pnparam/loso_M12") model = AutoModelForCTC.from_pretrained("pnparam/loso_M12") - Notebooks
- Google Colab
- Kaggle
loso_M12
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0778
- Wer: 1.4221
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 8.3422 | 0.95 | 500 | 3.5867 | 1.0 |
| 2.553 | 1.89 | 1000 | 1.4240 | 2.6174 |
| 0.8451 | 2.84 | 1500 | 0.2177 | 1.6943 |
| 0.2944 | 3.79 | 2000 | 0.1941 | 1.7673 |
| 0.1312 | 4.73 | 2500 | 0.1034 | 1.3669 |
| 0.079 | 5.68 | 3000 | 0.0695 | 1.2722 |
| 0.0457 | 6.63 | 3500 | 0.0778 | 1.4221 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
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