---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: whisper-tiny-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: 0.8039568345323741
---

<!-- 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-tiny-speech-commands

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3232
- Accuracy: 0.8040

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4229        | 1.0   | 412  | 1.1286          | 0.7936   |
| 0.1396        | 2.0   | 824  | 1.0506          | 0.7995   |
| 0.1323        | 3.0   | 1236 | 1.1224          | 0.7977   |
| 0.0528        | 4.0   | 1648 | 1.0810          | 0.8004   |
| 0.0889        | 5.0   | 2060 | 0.9224          | 0.8022   |
| 0.076         | 6.0   | 2472 | 1.0393          | 0.7981   |
| 0.0429        | 7.0   | 2884 | 1.1115          | 0.7990   |
| 0.0007        | 8.0   | 3296 | 1.1706          | 0.8026   |
| 0.0129        | 9.0   | 3708 | 1.0661          | 0.8013   |
| 0.0161        | 10.0  | 4120 | 1.0114          | 0.7990   |
| 0.0205        | 11.0  | 4532 | 1.2129          | 0.8031   |
| 0.0107        | 12.0  | 4944 | 1.1118          | 0.8026   |
| 0.0099        | 13.0  | 5356 | 0.9145          | 0.8031   |
| 0.0002        | 14.0  | 5768 | 1.1582          | 0.7999   |
| 0.0001        | 15.0  | 6180 | 1.2959          | 0.8035   |
| 0.0163        | 16.0  | 6592 | 1.0992          | 0.8026   |
| 0.0001        | 17.0  | 7004 | 1.2913          | 0.8035   |
| 0.0003        | 18.0  | 7416 | 1.3232          | 0.8040   |
| 0.0001        | 19.0  | 7828 | 1.3720          | 0.8040   |
| 0.0001        | 20.0  | 8240 | 1.3889          | 0.8040   |


### Framework versions

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