Instructions to use julycodes/wav2vec2-base-timit-demo-colab-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use julycodes/wav2vec2-base-timit-demo-colab-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="julycodes/wav2vec2-base-timit-demo-colab-1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("julycodes/wav2vec2-base-timit-demo-colab-1") model = AutoModelForCTC.from_pretrained("julycodes/wav2vec2-base-timit-demo-colab-1") - Notebooks
- Google Colab
- Kaggle
wav2vec2-base-timit-demo-colab-1
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6513
- Wer: 0.5544
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: 10
- 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: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.6074 | 8.77 | 500 | 3.1529 | 1.0 |
| 1.3204 | 17.54 | 1000 | 0.6513 | 0.5544 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
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