transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer id dataset:mozilla-foundation/common_voice_16_0 base_model:openai/whisper-base license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- id
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base Indonesian
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_0 id
      type: mozilla-foundation/common_voice_16_0
      config: id
      split: test
      args: id
    metrics:
    - name: Wer
      type: wer
      value: 26.607783604747446
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Base Indonesian

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4198
- Wer: 26.6078

## 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: 5e-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.8238        | 4.01  | 500   | 0.6400          | 37.9750 |
| 0.6348        | 9.01  | 1000  | 0.5193          | 32.2477 |
| 0.4879        | 14.0  | 1500  | 0.4829          | 30.7250 |
| 0.4518        | 19.0  | 2000  | 0.4645          | 29.7037 |
| 0.4253        | 23.01 | 2500  | 0.4513          | 28.8757 |
| 0.4471        | 28.01 | 3000  | 0.4409          | 28.0937 |
| 0.3713        | 33.01 | 3500  | 0.4347          | 27.7854 |
| 0.3233        | 38.0  | 4000  | 0.4307          | 27.6382 |
| 0.3152        | 43.0  | 4500  | 0.4280          | 27.5324 |
| 0.3152        | 47.01 | 5000  | 0.4245          | 27.2196 |
| 0.333         | 52.01 | 5500  | 0.4227          | 26.9942 |
| 0.257         | 57.0  | 6000  | 0.4217          | 26.9620 |
| 0.25          | 62.0  | 6500  | 0.4214          | 26.8148 |
| 0.2587        | 66.01 | 7000  | 0.4206          | 26.7550 |
| 0.2765        | 71.01 | 7500  | 0.4198          | 26.6998 |
| 0.2664        | 76.01 | 8000  | 0.4198          | 26.6216 |
| 0.223         | 81.0  | 8500  | 0.4199          | 26.6446 |
| 0.2309        | 86.0  | 9000  | 0.4199          | 26.6538 |
| 0.233         | 90.01 | 9500  | 0.4198          | 26.6078 |
| 0.2647        | 95.01 | 10000 | 0.4198          | 26.6216 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
