Instructions to use DangHuuTrang/blue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DangHuuTrang/blue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DangHuuTrang/blue")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DangHuuTrang/blue") model = AutoModelForCTC.from_pretrained("DangHuuTrang/blue") - Notebooks
- Google Colab
- Kaggle
metadata
license: cc-by-nc-4.0
tags:
- generated_from_trainer
model-index:
- name: blue
results: []
blue
This model is a fine-tuned version of nguyenvulebinh/wav2vec2-base-vietnamese-250h 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: 32
- 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: 5
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.17.0
- Pytorch 2.2.1+cu121
- Datasets 2.7.1
- Tokenizers 0.19.1