Instructions to use Shubham09/wav2vec2_n700 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubham09/wav2vec2_n700 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Shubham09/wav2vec2_n700")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Shubham09/wav2vec2_n700") model = AutoModelForCTC.from_pretrained("Shubham09/wav2vec2_n700") - Notebooks
- Google Colab
- Kaggle
wav2vec2_n700
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m 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: 0.0003
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 15
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.13.2
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