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wav2vec2-base-random-stop-classification-1
suyuanliu/wav2vec2-base-random-stop-classification-1
wav2vec2-base-random-stop-classification-1 is a audio classification model from suyuanliu. Use it for the audio classification task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Audio Classification
- Library
- transformers
- Model type
- wav2vec2
- Access
- Public
- Created
- Apr 16, 2023
- Updated
- Apr 16, 2023
- SHA
- 2a2ec5ee
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
wav2vec2-base-random-stop-classification-1
This model is a fine-tuned version of on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4066
- Accuracy: 0.8651
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6949 | 0.99 | 18 | 0.6706 | 0.5906 |
| 0.6753 | 1.97 | 36 | 0.6470 | 0.6383 |
| 0.6231 | 2.96 | 54 | 0.5590 | 0.7302 |
| 0.544 | 4.0 | 73 | 0.4623 | 0.7977 |
| 0.4806 | 4.99 | 91 | 0.4061 | 0.8317 |
| 0.4543 | 5.97 | 109 | 0.5891 | 0.7643 |
| 0.4947 | 6.96 | 127 | 0.3944 | 0.8386 |
| 0.4431 | 8.0 | 146 | 0.4528 | 0.8093 |
| 0.4147 | 8.99 | 164 | 0.4560 | 0.8222 |
| 0.4094 | 9.97 | 182 | 0.4193 | 0.8447 |
| 0.3906 | 10.96 | 200 | 0.3846 | 0.8549 |
| 0.3835 | 12.0 | 219 | 0.3845 | 0.8569 |
| 0.3632 | 12.99 | 237 | 0.3660 | 0.8644 |
| 0.3622 | 13.97 | 255 | 0.4107 | 0.8617 |
| 0.3472 | 14.96 | 273 | 0.3733 | 0.8685 |
| 0.3419 | 16.0 | 292 | 0.4496 | 0.8467 |
| 0.3074 | 16.99 | 310 | 0.3987 | 0.8638 |
| 0.3278 | 17.97 | 328 | 0.3740 | 0.8665 |
| 0.2841 | 18.96 | 346 | 0.3999 | 0.8651 |
| 0.2837 | 20.0 | 365 | 0.3954 | 0.8604 |
| 0.2928 | 20.99 | 383 | 0.3871 | 0.8644 |
| 0.3002 | 21.97 | 401 | 0.4978 | 0.8386 |
| 0.2783 | 22.96 | 419 | 0.4079 | 0.8692 |
| 0.2703 | 24.0 | 438 | 0.3977 | 0.8713 |
| 0.2816 | 24.66 | 450 | 0.4066 | 0.8651 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
- Task
- Audio Classification
- Library
- transformers
- Type
- wav2vec2
- Created
- Apr 16, 2023
- Updated
- Apr 16, 2023