---
library_name: transformers
license: apache-2.0
base_model: imrajeshkr/distilhubert-finetuned-speech_commands
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- precision
- recall
- f1
model-index:
- name: distilhubert-finetuned-speech_commands-finetuned-gtzan
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: test
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.9759184555734861
    - name: Recall
      type: recall
      value: 0.9749126053876208
    - name: F1
      type: f1
      value: 0.9749296122020006
---

<!-- 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. -->

# distilhubert-finetuned-speech_commands-finetuned-gtzan

This model is a fine-tuned version of [imrajeshkr/distilhubert-finetuned-speech_commands](https://huggingface.co/imrajeshkr/distilhubert-finetuned-speech_commands) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0934
- Precision: 0.9759
- Recall: 0.9749
- F1: 0.9749

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.2713        | 1.0   | 1216 | 0.2523          | 0.9172    | 0.9267 | 0.9166 |
| 0.137         | 2.0   | 2432 | 0.1119          | 0.9685    | 0.9667 | 0.9664 |
| 0.0295        | 3.0   | 3648 | 0.0977          | 0.9726    | 0.9703 | 0.9701 |
| 0.0037        | 4.0   | 4864 | 0.0956          | 0.9743    | 0.9733 | 0.9732 |
| 0.052         | 5.0   | 6080 | 0.0934          | 0.9759    | 0.9749 | 0.9749 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0