---
library_name: transformers
datasets:
- cobrayyxx/FLEURS_INDO-ENG_Speech_Translation
language:
- id
- en
base_model:
- openai/whisper-small
pipeline_tag: audio-text-to-text
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description
This model is a fine-tuned version of [opeanai/whisper-small](https://huggingface.co/openai/whisper-small) on [Fleurs Dataset](https://huggingface.co/datasets/cobrayyxx/FLEURS_INDO-ENG_Speech_Translation). 

## Uses
This model is used to predict the transcription of indonesian audio.

## How to Get Started with the Model

Use the code below to get started with the model.

1. Convert to ct2 first
   ```!ct2-transformers-converter --model cobrayyxx/whisper-small-indo-transcription --output_dir cobrayyxx/whisper-small-indo-transcription-ct2 --copy_files tokenizer.json preprocessor_config.json --quantization float16```
2. Load the ct2 model
   ```
   from faster_whisper import WhisperModel
   model_transcribe = WhisperModel(model_transcribe, device="cpu", compute_type="float32")
   ```


## Training Details

## Model Details
### Model Overview
- Framework: Hugging Face Transformers
- Training Steps: 100 steps
- Epochs: Approximately 0.56
- Training Loss: 0.3916
- Model Purpose: [Specify your task here, e.g., text classification, summarization, etc.]
- Performance Metrics
- Train Runtime: 458.31 seconds
- Train Samples per Second: 3.491
- Train Steps per Second: 0.218
- Total Floating Point Operations (FLOPs): 4.62 × 10^17

## Next Steps
- Doing evaluation for this model

## Citation
```
@misc{radford2022whisper,
  doi = {10.48550/ARXIV.2212.04356},
  url = {https://arxiv.org/abs/2212.04356},
  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```