Instructions to use EJAD1/wav2vec2-mina-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EJAD1/wav2vec2-mina-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EJAD1/wav2vec2-mina-ft")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("EJAD1/wav2vec2-mina-ft") model = AutoModelForCTC.from_pretrained("EJAD1/wav2vec2-mina-ft") - Notebooks
- Google Colab
- Kaggle
wav2vec2-mina-ft
This model is a fine-tuned version of facebook/mms-1b-all 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_8BIT 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
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.5
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for EJAD1/wav2vec2-mina-ft
Base model
facebook/mms-1b-all