---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- mir_st500
metrics:
- accuracy
model-index:
- name: wav2vec2-base-mirst500
  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

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_AUDIO_CLASSIFICATION.py dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8678
- Accuracy: 0.7017

## 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: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1999        | 1.0   | 1304 | 1.1029          | 0.5877   |
| 1.0779        | 2.0   | 2608 | 0.9455          | 0.6555   |
| 0.9775        | 3.0   | 3912 | 0.9670          | 0.6523   |
| 0.9542        | 4.0   | 5216 | 0.8810          | 0.6946   |
| 0.9403        | 5.0   | 6520 | 0.8678          | 0.7017   |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.9.1+cu102
- Datasets 2.0.0
- Tokenizers 0.10.3