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Whisper Small Indo Transcription

This model is a fine-tuned version of opeanai/whisper-small on Fleurs Dataset.

TypeText modelModèle de baseopenai/whisper-smallVersionFP32CClasse

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.

  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}
}

Sources

1 source
Hugging FacePrincipalVersion bba729a64bf23b741b62c01247c29f1e716a0ac0
huggingface.co/cobrayyxx/whisper-small-indo-transcription/tree/bba729a64bf23b741b62c01247c29f1e716a0ac0

Vérifié 31 oct.

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Artefacts du modèle

1 artefact

model.safetensors

safetensors · FP32 (unverified) · 922 MB · SHA-256 e75e16910d3f…a3b5 · Hugging Face

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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