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---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- mir_st500
metrics:
- accuracy
model-index:
- name: wav2vec2-base-mirst500-ac
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-mirst500-ac
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the /workspace/datasets/datasets/MIR_ST500/MIR_ST500.py dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7566
- Accuracy: 0.7570
## 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: 16
- eval_batch_size: 1
- seed: 0
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3718 | 1.0 | 1304 | 1.4422 | 0.4255 |
| 1.1285 | 2.0 | 2608 | 1.1061 | 0.5869 |
| 1.0275 | 3.0 | 3912 | 0.8825 | 0.6724 |
| 0.9982 | 4.0 | 5216 | 0.9181 | 0.6713 |
| 0.9482 | 5.0 | 6520 | 0.8717 | 0.6971 |
| 0.8687 | 6.0 | 7824 | 0.8041 | 0.7164 |
| 0.8841 | 7.0 | 9128 | 0.8869 | 0.7034 |
| 0.8094 | 8.0 | 10432 | 0.8216 | 0.7172 |
| 0.7733 | 9.0 | 11736 | 0.8018 | 0.7298 |
| 0.7892 | 10.0 | 13040 | 0.7517 | 0.7426 |
| 0.8736 | 11.0 | 14344 | 0.7482 | 0.7482 |
| 0.7035 | 12.0 | 15648 | 0.7730 | 0.7488 |
| 0.7361 | 13.0 | 16952 | 0.7677 | 0.7510 |
| 0.7808 | 14.0 | 18256 | 0.7765 | 0.7512 |
| 0.7359 | 15.0 | 19560 | 0.7566 | 0.7570 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
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