Instructions to use DoctorLego2003/w2v2-libri-10min with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DoctorLego2003/w2v2-libri-10min with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DoctorLego2003/w2v2-libri-10min")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DoctorLego2003/w2v2-libri-10min") model = AutoModelForCTC.from_pretrained("DoctorLego2003/w2v2-libri-10min") - Notebooks
- Google Colab
- Kaggle
w2v2-libri-10min
This model is a fine-tuned version of facebook/wav2vec2-base 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: 0.0003
- train_batch_size: 16
- 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: 500
- training_steps: 2500
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for DoctorLego2003/w2v2-libri-10min
Base model
facebook/wav2vec2-base