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---
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
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