Instructions to use Angeriod/in_car_commands_26_mdl__tiny_ver2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angeriod/in_car_commands_26_mdl__tiny_ver2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Angeriod/in_car_commands_26_mdl__tiny_ver2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Angeriod/in_car_commands_26_mdl__tiny_ver2") model = AutoModelForSpeechSeq2Seq.from_pretrained("Angeriod/in_car_commands_26_mdl__tiny_ver2") - Notebooks
- Google Colab
- Kaggle
in_car_commands_26_mdl__tiny_ver2
This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0446
- Cer: 6.5094
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: 3.75e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.153 | 1.0 | 2000 | 0.1292 | 88.5166 |
| 0.0606 | 2.0 | 4000 | 0.0773 | 52.4909 |
| 0.0376 | 3.0 | 6000 | 0.0593 | 10.0489 |
| 0.0225 | 4.0 | 8000 | 0.0511 | 7.8623 |
| 0.0139 | 5.0 | 10000 | 0.0491 | 8.0876 |
| 0.005 | 6.0 | 12000 | 0.0479 | 7.3742 |
| 0.0023 | 7.0 | 14000 | 0.0463 | 8.0034 |
| 0.0009 | 8.0 | 16000 | 0.0452 | 7.4157 |
| 0.0002 | 9.0 | 18000 | 0.0446 | 6.5094 |
| 0.0001 | 10.0 | 20000 | 0.0446 | 6.5094 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Angeriod/in_car_commands_26_mdl__tiny_ver2
Base model
openai/whisper-tiny