Instructions to use Mompansy/whisperfinetune_modelcheckpointsv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mompansy/whisperfinetune_modelcheckpointsv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Mompansy/whisperfinetune_modelcheckpointsv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Mompansy/whisperfinetune_modelcheckpointsv2") model = AutoModelForSpeechSeq2Seq.from_pretrained("Mompansy/whisperfinetune_modelcheckpointsv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
whisperfinetune_modelcheckpointsv2
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1296
- Wer: 17.1313
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0583 | 1.63 | 1000 | 0.1125 | 10.2794 |
| 0.0113 | 3.26 | 2000 | 0.1196 | 15.8164 |
| 0.0049 | 4.89 | 3000 | 0.1249 | 17.3557 |
| 0.0011 | 6.53 | 4000 | 0.1296 | 17.1313 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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