Instructions to use harriskr14/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harriskr14/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="harriskr14/audio_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("harriskr14/audio_classification") model = AutoModelForAudioClassification.from_pretrained("harriskr14/audio_classification") - Notebooks
- Google Colab
- Kaggle
audio_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:
- Loss: 2.6446
- Accuracy: 0.0531
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 4 | 2.6385 | 0.0442 |
| No log | 2.0 | 8 | 2.6355 | 0.0531 |
| 2.5056 | 3.0 | 12 | 2.6399 | 0.0619 |
| 2.5056 | 4.0 | 16 | 2.6434 | 0.0354 |
| 2.4294 | 5.0 | 20 | 2.6446 | 0.0531 |
| 2.4294 | 6.0 | 24 | 2.6372 | 0.0531 |
| 2.4294 | 7.0 | 28 | 2.6398 | 0.0619 |
| 2.4893 | 8.0 | 32 | 2.6445 | 0.0619 |
| 2.4893 | 9.0 | 36 | 2.6445 | 0.0442 |
| 2.4195 | 10.0 | 40 | 2.6446 | 0.0531 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for harriskr14/audio_classification
Base model
facebook/wav2vec2-base