Instructions to use specialv/whisper-small-hi-local with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use specialv/whisper-small-hi-local with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="specialv/whisper-small-hi-local")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("specialv/whisper-small-hi-local") model = AutoModelForSpeechSeq2Seq.from_pretrained("specialv/whisper-small-hi-local") - Notebooks
- Google Colab
- Kaggle
whisper-small-hi-local
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: 0.2302
- Wer: 26.0234
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.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: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.8407 | 0.8368 | 100 | 0.3808 | 34.6979 |
| 0.4310 | 1.6695 | 200 | 0.2684 | 29.4152 |
| 0.2566 | 2.5021 | 300 | 0.2381 | 27.2125 |
| 0.1728 | 3.3347 | 400 | 0.2330 | 26.7251 |
| 0.1393 | 4.1674 | 500 | 0.2302 | 26.0234 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for specialv/whisper-small-hi-local
Base model
openai/whisper-small