Instructions to use TigrulyaCat/whisper-small-hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TigrulyaCat/whisper-small-hi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TigrulyaCat/whisper-small-hi")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("TigrulyaCat/whisper-small-hi") model = AutoModelForSpeechSeq2Seq.from_pretrained("TigrulyaCat/whisper-small-hi") - Notebooks
- Google Colab
- Kaggle
whisper-small-hi
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2437
- Wer: 47.6190
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 OptimizerNames.ADAMW_TORCH 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: 5
- training_steps: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.1802 | 0.2174 | 10 | 0.6252 | 77.3810 |
| 0.2073 | 0.4348 | 20 | 0.2437 | 47.6190 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for TigrulyaCat/whisper-small-hi
Base model
openai/whisper-small