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---
library_name: transformers
language:
- id
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- octava/indonesian-voice-transcription-1.4.9a-cv-fl-slrjv-md
metrics:
- wer
model-index:
- name: Optimized Whisper Small Id for Inspirasi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Extracted Youtube with CommonVoice11, Fleurs, OpenSLR, and MagicData
type: octava/indonesian-voice-transcription-1.4.9a-cv-fl-slrjv-md
args: 'config: id, split: train'
metrics:
- name: Wer
type: wer
value: 19.96201329534663
---
<!-- 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. -->
# Optimized Whisper Small Id for Inspirasi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Extracted Youtube with CommonVoice11, Fleurs, OpenSLR, and MagicData dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3376
- Wer: 19.9620
## 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: 32
- eval_batch_size: 16
- 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4122 | 0.1686 | 500 | 0.3999 | 24.8908 |
| 0.2737 | 0.3373 | 1000 | 0.3655 | 22.4691 |
| 0.2311 | 0.5059 | 1500 | 0.3491 | 21.5195 |
| 0.1947 | 0.6745 | 2000 | 0.3339 | 21.5100 |
| 0.169 | 0.8432 | 2500 | 0.3408 | 20.6363 |
| 0.0875 | 1.0118 | 3000 | 0.3429 | 21.2726 |
| 0.0877 | 1.1804 | 3500 | 0.3430 | 20.4748 |
| 0.0726 | 1.3491 | 4000 | 0.3396 | 20.2469 |
| 0.0741 | 1.5177 | 4500 | 0.3378 | 20.2754 |
| 0.0675 | 1.6863 | 5000 | 0.3376 | 19.9620 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.2.0a0+81ea7a4
- Datasets 3.3.2
- Tokenizers 0.21.0
|