---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: hubert-base-ls960-speech-commands-h
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: speech_commands
      type: speech_commands
      config: v0.02
      split: None
      args: v0.02
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7594424460431655
---

<!-- 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. -->

# hubert-base-ls960-speech-commands-h

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3148
- Accuracy: 0.7594

## 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: 5e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.743         | 1.0   | 824   | 3.4107          | 0.1781   |
| 2.3383        | 2.0   | 1648  | 3.4632          | 0.1862   |
| 2.2702        | 3.0   | 2472  | 3.5701          | 0.0787   |
| 2.3059        | 4.0   | 3296  | 3.5742          | 0.0971   |
| 2.2574        | 5.0   | 4120  | 3.5457          | 0.1493   |
| 2.0617        | 6.0   | 4944  | 2.8490          | 0.3453   |
| 2.0289        | 7.0   | 5768  | 2.7607          | 0.3215   |
| 1.7807        | 8.0   | 6592  | 2.5721          | 0.4681   |
| 1.8188        | 9.0   | 7416  | 2.5625          | 0.5301   |
| 1.3812        | 10.0  | 8240  | 2.4258          | 0.6942   |
| 1.3136        | 11.0  | 9064  | 2.2087          | 0.6884   |
| 1.2867        | 12.0  | 9888  | 1.8347          | 0.7221   |
| 1.1036        | 13.0  | 10712 | 1.6731          | 0.7383   |
| 0.9534        | 14.0  | 11536 | 1.8732          | 0.7307   |
| 0.9289        | 15.0  | 12360 | 1.5742          | 0.7415   |
| 1.0973        | 16.0  | 13184 | 1.3693          | 0.7365   |
| 0.989         | 17.0  | 14008 | 1.2718          | 0.7455   |
| 0.8876        | 18.0  | 14832 | 1.3148          | 0.7594   |
| 0.814         | 19.0  | 15656 | 1.2231          | 0.7558   |
| 0.9899        | 20.0  | 16480 | 1.2349          | 0.7522   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1