Instructions to use RobinsonNgeukeu237/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobinsonNgeukeu237/working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RobinsonNgeukeu237/working")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("RobinsonNgeukeu237/working") model = AutoModelForCTC.from_pretrained("RobinsonNgeukeu237/working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
facebook/wav2vec2-xls-r-300m
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: 0.1962
- Wer: 0.1449
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: 32
- eval_batch_size: 8
- seed: 42
- 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: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4984 | 1.0909 | 300 | 0.2046 | 0.1412 |
| 0.5009 | 2.1818 | 600 | 0.1962 | 0.1449 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
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Model tree for RobinsonNgeukeu237/working
Base model
facebook/wav2vec2-xls-r-300m