Instructions to use 007arbab/wav2vec2-rapnic-example-google-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 007arbab/wav2vec2-rapnic-example-google-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="007arbab/wav2vec2-rapnic-example-google-colab")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("007arbab/wav2vec2-rapnic-example-google-colab") model = AutoModelForCTC.from_pretrained("007arbab/wav2vec2-rapnic-example-google-colab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec2-rapnic-example-google-colab
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.1076
- Wer: 1.0876
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 7.7457 | 9.6154 | 500 | 3.0582 | 1.0 |
| 2.9104 | 19.2308 | 1000 | 2.3466 | 1.0 |
| 1.3678 | 28.8462 | 1500 | 2.1135 | 1.0742 |
| 1.3678 | 30.0 | 1560 | 2.1076 | 1.0876 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for 007arbab/wav2vec2-rapnic-example-google-colab
Base model
facebook/wav2vec2-base