Instructions to use YuanWellspring/wav2vec2-nsc-final_1-google-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YuanWellspring/wav2vec2-nsc-final_1-google-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="YuanWellspring/wav2vec2-nsc-final_1-google-colab")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("YuanWellspring/wav2vec2-nsc-final_1-google-colab") model = AutoModelForCTC.from_pretrained("YuanWellspring/wav2vec2-nsc-final_1-google-colab") - Notebooks
- Google Colab
- Kaggle
wav2vec2-nsc-final_1-google-colab
This model was trained from scratch on the None dataset.
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: 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: 2
Training results
Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.10.3
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