Instructions to use dmusingu/muk-sw-digits-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmusingu/muk-sw-digits-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dmusingu/muk-sw-digits-classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dmusingu/muk-sw-digits-classification") model = AutoModelForAudioClassification.from_pretrained("dmusingu/muk-sw-digits-classification") - Notebooks
- Google Colab
- Kaggle
muk-sw-digits-classification
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.4746
- eval_accuracy: 0.9333
- eval_runtime: 4.4354
- eval_samples_per_second: 67.637
- eval_steps_per_second: 67.637
- epoch: 10.0
- step: 12000
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: 3e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.1
- Datasets 4.4.1
- Tokenizers 0.22.1
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Model tree for dmusingu/muk-sw-digits-classification
Base model
facebook/wav2vec2-base