---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- speech-commands
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-speech_commands
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: Speech_command_RK
      type: speech-commands
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9975728155339806
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilhubert-finetuned-speech_commands

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the Speech_command_RK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1895
- Accuracy: 0.9976

## 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: 264
- eval_batch_size: 264
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1911        | 1.0   | 25   | 1.9352          | 0.8859   |
| 1.0366        | 2.0   | 50   | 0.8334          | 0.9915   |
| 0.4879        | 3.0   | 75   | 0.3774          | 0.9939   |
| 0.297         | 4.0   | 100  | 0.2254          | 0.9951   |
| 0.2422        | 5.0   | 125  | 0.1895          | 0.9976   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1