Instructions to use MihirRPatil/nptel-asr-phoneme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MihirRPatil/nptel-asr-phoneme with Transformers:
# Load model directly from transformers import AutoProcessor, Wav2Vec2PhonemeEmbedder processor = AutoProcessor.from_pretrained("MihirRPatil/nptel-asr-phoneme") model = Wav2Vec2PhonemeEmbedder.from_pretrained("MihirRPatil/nptel-asr-phoneme") - Notebooks
- Google Colab
- Kaggle
nptel-asr-phoneme
This model was trained from scratch on an unknown 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch 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: 1000
- training_steps: 20000
Training results
Framework versions
- Transformers 5.5.0
- Pytorch 2.4.0+cu118
- Datasets 4.8.4
- Tokenizers 0.22.2
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