pytorch tensorboard wav2vec2 audio-classification dataset:mir_st500 transformers generated_from_trainer license:apache-2.0 model-index
---
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
