---
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Base Tagalog
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs fil_ph
      type: google/fleurs
      config: fil_ph
      split: test
      args: fil_ph
    metrics:
    - name: Wer
      type: wer
      value: 30.810565352304547
---

<!-- 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 Base Tagalog

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs fil_ph dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7222
- Wer: 30.8106

## 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-06
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5804        | 38.0  | 500  | 0.7836          | 36.0478 |
| 0.1934        | 76.0  | 1000 | 0.6861          | 31.5220 |
| 0.0589        | 115.0 | 1500 | 0.7040          | 32.4415 |
| 0.0251        | 153.0 | 2000 | 0.7222          | 30.8106 |
| 0.0154        | 192.0 | 2500 | 0.7362          | 31.3593 |
| 0.0109        | 230.0 | 3000 | 0.7470          | 31.7604 |
| 0.0085        | 269.0 | 3500 | 0.7562          | 31.7112 |
| 0.0071        | 307.0 | 4000 | 0.7630          | 31.9874 |
| 0.0064        | 346.0 | 4500 | 0.7675          | 32.0064 |
| 0.0061        | 384.0 | 5000 | 0.7692          | 32.0669 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0