Instructions to use bigmorning/whisper_charsplit_new_0002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigmorning/whisper_charsplit_new_0002 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bigmorning/whisper_charsplit_new_0002")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("bigmorning/whisper_charsplit_new_0002") model = AutoModelForMultimodalLM.from_pretrained("bigmorning/whisper_charsplit_new_0002") - Notebooks
- Google Colab
- Kaggle
whisper_charsplit_new_0002
This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.5740
- Train Accuracy: 0.0666
- Train Wermet: 12.7778
- Validation Loss: 0.5113
- Validation Accuracy: 0.0706
- Validation Wermet: 11.1022
- Epoch: 1
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|---|---|---|---|---|---|---|
| 0.8733 | 0.0602 | 13.0686 | 0.6470 | 0.0676 | 11.4066 | 0 |
| 0.5740 | 0.0666 | 12.7778 | 0.5113 | 0.0706 | 11.1022 | 1 |
Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
- Downloads last month
- 1
Model tree for bigmorning/whisper_charsplit_new_0002
Base model
openai/whisper-tiny