---
language:
- id
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- magic_data
- TITML
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium Indonesian
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 id
      type: mozilla-foundation/common_voice_11_0
      config: id
      split: test
    metrics:
    - type: wer
      value: 3.8273540533062804
      name: Wer
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs id_id
      type: google/fleurs
      config: id_id
      split: test
    metrics:
    - type: wer
      value: 9.74
      name: Wer
---

# Whisper Medium Indonesian

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the 
Indonesian mozilla-foundation/common_voice_11_0, magic_data, titml and google/fleurs dataset. It achieves the following 
results:
### CV11 test split:
- Loss: 0.0698
- Wer: 3.8274
### Google/fleurs test split:
- Wer: 9.74

## Usage

```python
from transformers import pipeline
transcriber = pipeline(
  "automatic-speech-recognition", 
  model="cahya/whisper-medium-id"
)
transcriber.model.config.forced_decoder_ids = (
  transcriber.tokenizer.get_decoder_prompt_ids(
    language="id" 
    task="transcribe"
  )
)
transcription = transcriber("my_audio_file.mp3")
```

## 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: 16
- eval_batch_size: 16
- seed: 42
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0427        | 0.33  | 1000  | 0.0664          | 4.3807 |
| 0.042         | 0.66  | 2000  | 0.0658          | 3.9426 |
| 0.0265        | 0.99  | 3000  | 0.0657          | 3.8274 |
| 0.0211        | 1.32  | 4000  | 0.0679          | 3.8366 |
| 0.0212        | 1.66  | 5000  | 0.0682          | 3.8412 |
| 0.0206        | 1.99  | 6000  | 0.0683          | 3.8689 |
| 0.0166        | 2.32  | 7000  | 0.0711          | 3.9657 |
| 0.0095        | 2.65  | 8000  | 0.0717          | 3.9980 |
| 0.0122        | 2.98  | 9000  | 0.0714          | 3.9795 |
| 0.0049        | 3.31  | 10000 | 0.0720          | 3.9887 |

## Evaluation

We evaluated the model using the test split of two datasets, the [Common Voice 11](https://huggingface.co./datasets/mozilla-foundation/common_voice_11_0) 
and the [Google Fleurs](https://huggingface.co./datasets/google/fleurs). 
As Whisper can transcribe casing and punctuation, we also evaluate its performance using raw and normalized text.
(lowercase + removal of punctuations). The results are as follows:

### Common Voice 11

|                                                                           | WER  |
|---------------------------------------------------------------------------|------|
| [cahya/whisper-medium-id](https://huggingface.co./cahya/whisper-medium-id) | 3.83 |
| [openai/whisper-medium](https://huggingface.co./openai/whisper-medium)     | 12.62  |

### Google/Fleurs

|                                                                                                             | WER  |
|-------------------------------------------------------------------------------------------------------------|------|
| [cahya/whisper-medium-id](https://huggingface.co./cahya/whisper-medium-id)                      | 9.74 |
| [cahya/whisper-medium-id](https://huggingface.co./cahya/whisper-medium-id) + text normalization | tbc  |
| [openai/whisper-medium](https://huggingface.co./openai/whisper-medium)                                       | 10.2 |
| [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) + text normalization                  | tbc  |
| 
### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2