---
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
  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: 0.8057553956834532
---

<!-- 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

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: 1.0829
- Accuracy: 0.8058

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.8285        | 1.0   | 824   | 1.9509          | 0.7167   |
| 0.5292        | 2.0   | 1648  | 1.3813          | 0.7909   |
| 0.3554        | 3.0   | 2472  | 1.1773          | 0.7941   |
| 0.2873        | 4.0   | 3296  | 1.2437          | 0.7981   |
| 0.2525        | 5.0   | 4120  | 1.2514          | 0.8004   |
| 0.2941        | 6.0   | 4944  | 1.2243          | 0.7995   |
| 0.1809        | 7.0   | 5768  | 1.1965          | 0.8008   |
| 0.2313        | 8.0   | 6592  | 1.0694          | 0.8022   |
| 0.1917        | 9.0   | 7416  | 1.0618          | 0.7995   |
| 0.1212        | 10.0  | 8240  | 1.0972          | 0.8026   |
| 0.185         | 11.0  | 9064  | 1.0868          | 0.8017   |
| 0.143         | 12.0  | 9888  | 1.1558          | 0.8031   |
| 0.2227        | 13.0  | 10712 | 1.0550          | 0.8040   |
| 0.1884        | 14.0  | 11536 | 1.0384          | 0.8022   |
| 0.1183        | 15.0  | 12360 | 1.0169          | 0.8035   |
| 0.1849        | 16.0  | 13184 | 1.0061          | 0.8035   |
| 0.141         | 17.0  | 14008 | 1.0337          | 0.8053   |
| 0.1328        | 18.0  | 14832 | 1.0829          | 0.8058   |
| 0.1238        | 19.0  | 15656 | 1.0576          | 0.8053   |
| 0.0932        | 20.0  | 16480 | 1.0641          | 0.8053   |


### Framework versions

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