Whisper Small Indo Transcription
This model is a fine-tuned version of opeanai/whisper-small on Fleurs Dataset.
Source du modèle
- Type
- Text model
- Taille du fichier
- 922 MB
- Modèle de base
- openai/whisper-small
- Version
- vFP32
- Mis à jour
- 31 oct. 2026
- Téléchargements
- 30
- Source
- Hugging Face
Description de la source
Model Card for Model ID
Model Details
Model Description
This model is a fine-tuned version of opeanai/whisper-small on Fleurs Dataset.
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.
- 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 - 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}
}
Sources
1 source
Sources
1 sourceVérifié 31 oct.
Download files
Repository files
File inventory only; completeness is unverified.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactmodel.safetensors
safetensors · FP32 (unverified) · 922 MB · SHA-256 e75e16910d3f…a3b5 · Hugging Face
Download fileExtraits de sources
3 extraits
Extraits de sources
3 extraits--- 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} } ```
Source context: 30 downloads · 0 likes · Pipeline audio-text-to-text · Library transformers · Repo cobrayyxx/whisper-small-indo-transcription
Source context: 13 downloads · 0 likes · Pipeline audio-text-to-text · Library transformers · Repo cobrayyxx/whisper-small-indo-transcription
