---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: AST_speechcommandsV2_final
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: speech_commands
      type: speech_commands
      config: v0.02
      split: test
      args: v0.02
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8889570552147239
---

<!-- 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. -->

# AST_speechcommandsV2_final

This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4825
- Accuracy: 0.8890

## 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: 72
- eval_batch_size: 72
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 288
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3557        | 1.0   | 294  | 0.7017          | 0.8354   |
| 0.1948        | 2.0   | 589  | 0.6838          | 0.8397   |
| 0.1219        | 3.0   | 884  | 0.5752          | 0.8699   |
| 0.0704        | 4.0   | 1179 | 0.5554          | 0.8675   |
| 0.0404        | 5.0   | 1473 | 0.5437          | 0.8663   |
| 0.0136        | 6.0   | 1768 | 0.5247          | 0.8759   |
| 0.0072        | 7.0   | 2063 | 0.5235          | 0.8759   |
| 0.0026        | 8.0   | 2358 | 0.5035          | 0.8859   |
| 0.0007        | 9.0   | 2652 | 0.4800          | 0.8896   |
| 0.0005        | 9.97  | 2940 | 0.4825          | 0.8890   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1