---
library_name: transformers
language:
- qve
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- cportoca/Quechua_dataset
metrics:
- wer
model-index:
- name: Whisper Large Ja-Qve - cportoca
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Quechua_dataset
      type: cportoca/Quechua_dataset
      args: 'config: Qve, split: train/test'
    metrics:
    - name: Wer
      type: wer
      value: 17.79102604330091
---

<!-- 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 Large Ja-Qve - cportoca

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Quechua_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2409
- Wer: 17.7910

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.2791        | 1.3550  | 1000 | 0.3439          | 32.1619 |
| 0.137         | 2.7100  | 2000 | 0.2366          | 26.9532 |
| 0.0305        | 4.0650  | 3000 | 0.2266          | 21.3367 |
| 0.0142        | 5.4201  | 4000 | 0.2322          | 18.5441 |
| 0.0048        | 6.7751  | 5000 | 0.2285          | 18.4500 |
| 0.0014        | 8.1301  | 6000 | 0.2378          | 18.1362 |
| 0.0007        | 9.4851  | 7000 | 0.2394          | 17.6969 |
| 0.0004        | 10.8401 | 8000 | 0.2409          | 17.7910 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3