---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small en - Stan
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: google/fleurs
      config: en_us
      split: None
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 8.637757947573899
---

<!-- 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 en - Stan

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3236
- Wer: 8.6378

## 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: 200
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5755        | 0.61  | 100  | 0.5716          | 8.6029 |
| 0.1769        | 1.23  | 200  | 0.2722          | 8.3659 |
| 0.1153        | 1.84  | 300  | 0.2791          | 8.7842 |
| 0.0356        | 2.45  | 400  | 0.2852          | 8.7981 |
| 0.0208        | 3.07  | 500  | 0.2923          | 8.6866 |
| 0.0105        | 3.68  | 600  | 0.3050          | 8.6517 |
| 0.0032        | 4.29  | 700  | 0.3126          | 8.6238 |
| 0.0033        | 4.91  | 800  | 0.3174          | 8.6308 |
| 0.0028        | 5.52  | 900  | 0.3227          | 8.5611 |
| 0.0017        | 6.13  | 1000 | 0.3236          | 8.6378 |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2