---
library_name: transformers
language:
- dv
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
model-index:
- name: Whisper Small - DV - Marlhex
  results: []
metrics:
- wer
---

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# Whisper Small - DV - Marlhex

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.

## Model description

Whisper fine tunned for dv language (Maldivan language, Divehi) from Maldives

## Intended uses & limitations

part of the AI portfolio to show to companies some of the work I've done in the Audio pilar.

## Training and evaluation data

WER normalized.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 5
- training_steps: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer   |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-----:|
| No log        | 0.0326 | 10   | 1.9577          | 100.0     | 100.0 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0

### Next Steps
- Looking forward to training more languages that require more GB of storage, but my setup is limited.