# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("chrisgg1/hubert-base-ls960-finetuned-ks-verbtest2")
model = AutoModelForAudioClassification.from_pretrained("chrisgg1/hubert-base-ls960-finetuned-ks-verbtest2", device_map="auto")Quick Links
hubert-base-ls960-finetuned-ks-verbtest2
This model is a fine-tuned version of facebook/hubert-base-ls960 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0180
- Accuracy: 0.9994
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4028 | 0.99 | 50 | 0.2129 | 0.9919 |
| 0.1071 | 2.0 | 101 | 0.0594 | 0.9944 |
| 0.0627 | 2.99 | 151 | 0.0248 | 0.9988 |
| 0.0423 | 4.0 | 202 | 0.0180 | 0.9994 |
| 0.0315 | 4.95 | 250 | 0.0165 | 0.9994 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for chrisgg1/hubert-base-ls960-finetuned-ks-verbtest2
Base model
facebook/hubert-base-ls960
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="chrisgg1/hubert-base-ls960-finetuned-ks-verbtest2")