---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- FreeSound
metrics:
- wer
model-index:
- name: Whisper Tiny En - FreeSound based captions test
  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 Tiny En - FreeSound based captions test

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co./openai/whisper-tiny) on the FreeSound Audio dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8548
- Wer: 98.5500

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 5.2273        | 0.6098 | 25   | 4.9782          | 101.4246 |
| 4.0984        | 1.2195 | 50   | 4.1433          | 100.8904 |
| 3.8301        | 1.8293 | 75   | 3.9157          | 99.3132  |
| 3.7081        | 2.4390 | 100  | 3.8548          | 98.5500  |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.1.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1