---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: whisper-small-keyword-spotting-m
  results: []
---

<!-- 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-small-keyword-spotting-m

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the kw-spotting-fsc-sl-agv dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0015
- Accuracy: 0.9999

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0146        | 1.0   | 356  | 0.0231          | 0.9925   |
| 0.0124        | 2.0   | 712  | 0.0105          | 0.9977   |
| 0.0091        | 3.0   | 1068 | 0.0015          | 0.9999   |
| 0.0101        | 4.0   | 1425 | 0.0028          | 0.9994   |
| 0.0094        | 5.0   | 1780 | 0.0022          | 0.9995   |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2