|
|
--- |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- audiofolder |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: wav2vec2-base-random-stop-classification-5 |
|
|
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. --> |
|
|
|
|
|
# wav2vec2-base-random-stop-classification-5 |
|
|
|
|
|
This model is a fine-tuned version of [](https://huggingface.co/) on the audiofolder dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.4239 |
|
|
- Accuracy: 0.8631 |
|
|
|
|
|
## 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 | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
|
| 0.6916 | 0.99 | 18 | 0.6503 | 0.6362 | |
|
|
| 0.6628 | 1.97 | 36 | 0.5354 | 0.7391 | |
|
|
| 0.5922 | 2.96 | 54 | 0.4775 | 0.7786 | |
|
|
| 0.5158 | 4.0 | 73 | 0.4559 | 0.8072 | |
|
|
| 0.4733 | 4.99 | 91 | 0.4308 | 0.8188 | |
|
|
| 0.4935 | 5.97 | 109 | 0.5186 | 0.7888 | |
|
|
| 0.4512 | 6.96 | 127 | 0.4108 | 0.8358 | |
|
|
| 0.4397 | 8.0 | 146 | 0.4692 | 0.8270 | |
|
|
| 0.4037 | 8.99 | 164 | 0.4049 | 0.8304 | |
|
|
| 0.4053 | 9.97 | 182 | 0.4054 | 0.8379 | |
|
|
| 0.3774 | 10.96 | 200 | 0.4330 | 0.8379 | |
|
|
| 0.3624 | 12.0 | 219 | 0.3800 | 0.8495 | |
|
|
| 0.376 | 12.99 | 237 | 0.5123 | 0.8263 | |
|
|
| 0.3908 | 13.97 | 255 | 0.4049 | 0.8386 | |
|
|
| 0.3405 | 14.96 | 273 | 0.4200 | 0.8529 | |
|
|
| 0.3542 | 16.0 | 292 | 0.4040 | 0.8569 | |
|
|
| 0.3284 | 16.99 | 310 | 0.4578 | 0.8474 | |
|
|
| 0.3094 | 17.97 | 328 | 0.4465 | 0.8522 | |
|
|
| 0.2999 | 18.96 | 346 | 0.4126 | 0.8569 | |
|
|
| 0.3059 | 20.0 | 365 | 0.4139 | 0.8529 | |
|
|
| 0.2891 | 20.99 | 383 | 0.4101 | 0.8624 | |
|
|
| 0.2968 | 21.97 | 401 | 0.4589 | 0.8501 | |
|
|
| 0.2764 | 22.96 | 419 | 0.4263 | 0.8522 | |
|
|
| 0.2841 | 24.0 | 438 | 0.4350 | 0.8597 | |
|
|
| 0.2805 | 24.66 | 450 | 0.4239 | 0.8631 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.27.4 |
|
|
- Pytorch 1.13.0 |
|
|
- Datasets 2.7.1 |
|
|
- Tokenizers 0.13.2 |
|
|
|
