Instructions to use phi0108/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phi0108/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="phi0108/audio_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("phi0108/audio_classification") model = AutoModelForAudioClassification.from_pretrained("phi0108/audio_classification") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - minds14 | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: audio_classification | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # audio_classification | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.6385 | |
| - Accuracy: 0.0708 | |
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 0.8 | 3 | 2.6440 | 0.0442 | | |
| | No log | 1.87 | 7 | 2.6566 | 0.0531 | | |
| | 2.6406 | 2.93 | 11 | 2.6527 | 0.0354 | | |
| | 2.6406 | 4.0 | 15 | 2.6533 | 0.0619 | | |
| | 2.6406 | 4.8 | 18 | 2.6503 | 0.0796 | | |
| | 2.6412 | 5.87 | 22 | 2.6402 | 0.0885 | | |
| | 2.6412 | 6.93 | 26 | 2.6394 | 0.0619 | | |
| | 2.6389 | 8.0 | 30 | 2.6385 | 0.0708 | | |
| ### Framework versions | |
| - Transformers 4.27.4 | |
| - Pytorch 2.0.0 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.13.3 | |