---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: whisper-base-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: 0.8066546762589928
---

<!-- 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. -->

# whisper-base-speech-commands

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1307
- Accuracy: 0.8067

## 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: 96
- eval_batch_size: 96
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2604        | 1.0   | 412  | 1.0617          | 0.7977   |
| 0.1168        | 2.0   | 824  | 1.0024          | 0.8017   |
| 0.1527        | 3.0   | 1236 | 0.9757          | 0.8022   |
| 0.0637        | 4.0   | 1648 | 1.0066          | 0.8004   |
| 0.0631        | 5.0   | 2060 | 1.0504          | 0.8053   |
| 0.0554        | 6.0   | 2472 | 1.1307          | 0.8067   |
| 0.1075        | 7.0   | 2884 | 1.1664          | 0.8017   |
| 0.021         | 8.0   | 3296 | 1.4746          | 0.8044   |
| 0.0144        | 9.0   | 3708 | 1.3729          | 0.8044   |
| 0.0158        | 10.0  | 4120 | 1.3561          | 0.8040   |
| 0.0504        | 11.0  | 4532 | 1.3289          | 0.8053   |


### Framework versions

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