---
base_model: openai/whisper-small
datasets:
- arrow
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: dialect
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: arrow
      type: arrow
      config: default
      split: train
      args: default
    metrics:
    - type: wer
      value: 0.0
      name: Wer
---

<!-- 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. -->

# dialect

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the arrow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Wer: 0.0

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.0444        | 0.2404 | 1000  | 0.0239          | 3.75   |
| 0.0115        | 0.4808 | 2000  | 0.0157          | 1.5385 |
| 0.0047        | 0.7212 | 3000  | 0.0844          | 4.4231 |
| 0.0153        | 0.9615 | 4000  | 0.0050          | 0.6731 |
| 0.0192        | 1.2019 | 5000  | 0.0017          | 0.1923 |
| 0.0           | 1.4423 | 6000  | 0.0075          | 0.8654 |
| 0.0           | 1.6827 | 7000  | 0.0002          | 0.0962 |
| 0.0274        | 1.9231 | 8000  | 0.0195          | 2.2115 |
| 0.0           | 2.1635 | 9000  | 0.0179          | 1.0577 |
| 0.0402        | 2.4038 | 10000 | 0.0020          | 0.0962 |
| 0.0161        | 2.6442 | 11000 | 0.0050          | 0.3846 |
| 0.0009        | 2.8846 | 12000 | 0.0048          | 0.2885 |
| 0.0           | 3.125  | 13000 | 0.0031          | 0.1923 |
| 0.0           | 3.3654 | 14000 | 0.0029          | 0.1923 |
| 0.0           | 3.6058 | 15000 | 0.0029          | 0.1923 |
| 0.0           | 3.8462 | 16000 | 0.0043          | 0.1923 |
| 0.0           | 4.0865 | 17000 | 0.0008          | 0.0962 |
| 0.0           | 4.3269 | 18000 | 0.0000          | 0.0    |
| 0.0           | 4.5673 | 19000 | 0.0000          | 0.0    |
| 0.0           | 4.8077 | 20000 | 0.0001          | 0.0    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1