---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- stillerman/libristutter-4.7k
metrics:
- wer
model-index:
- name: Whisper Small Stutter - Ariel Cerda
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Libristutter 4.7k
      type: stillerman/libristutter-4.7k
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 31.702812202097235
---

<!-- 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 Stutter - Ariel Cerda

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Libristutter 4.7k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4843
- Wer: 31.7028

## 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0595        | 3.7453  | 1000 | 0.3199          | 17.1830 |
| 0.0046        | 7.4906  | 2000 | 0.4093          | 18.1542 |
| 0.0008        | 11.2360 | 3000 | 0.4562          | 24.3625 |
| 0.0006        | 14.9813 | 4000 | 0.4754          | 33.2340 |
| 0.0005        | 18.7266 | 5000 | 0.4843          | 31.7028 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1