Instructions to use annie0072/whisper-small-hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use annie0072/whisper-small-hi with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("annie0072/whisper-small-hi", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Whisper Small hi
This model is a fine-tuned version of openai/whisper-small on the None dataset.
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused 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: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1
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Base model
openai/whisper-small