---
language:
- gn
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Common Voice 16 - Guarani
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16
      type: mozilla-foundation/common_voice_16_1
      config: gn
      split: None
      args: gn
    metrics:
    - name: Wer
      type: wer
      value: 58.17978782802904
---

<!-- 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. -->

# Common Voice 16 - Guarani

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 16 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5857
- Wer: 58.1798

## 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: 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: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 2.3905        | 1.0101 | 100  | 0.9598          | 81.9654 |
| 0.5779        | 2.0202 | 200  | 0.6883          | 68.6767 |
| 0.3116        | 3.0303 | 300  | 0.5997          | 62.5349 |
| 0.1741        | 4.0404 | 400  | 0.5750          | 59.5757 |
| 0.0955        | 5.0505 | 500  | 0.5857          | 58.1798 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1