Instructions to use MarioNapoli/DynamicWav2Vec_TEST_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarioNapoli/DynamicWav2Vec_TEST_13 with Transformers:
# Load model directly from transformers import AutoProcessor, MyWav2Vec2ForCTC processor = AutoProcessor.from_pretrained("MarioNapoli/DynamicWav2Vec_TEST_13") model = MyWav2Vec2ForCTC.from_pretrained("MarioNapoli/DynamicWav2Vec_TEST_13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
DynamicWav2Vec_TEST_13
This model is a fine-tuned version of joorock12/wav2vec2-large-xlsr-italian on the common_voice_1_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4088
- Wer: 0.2051
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5677 | 1.67 | 5 | 0.4480 | 0.2308 |
| 0.595 | 3.33 | 10 | 0.4149 | 0.2564 |
| 0.5867 | 5.0 | 15 | 0.4088 | 0.2051 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for MarioNapoli/DynamicWav2Vec_TEST_13
Base model
joaoalvarenga/wav2vec2-large-xlsr-italian