Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2-bert
Generated from Trainer
Instructions to use HamdanXI/w2v2_uclass_clipped_10_seconds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HamdanXI/w2v2_uclass_clipped_10_seconds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="HamdanXI/w2v2_uclass_clipped_10_seconds")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("HamdanXI/w2v2_uclass_clipped_10_seconds") model = AutoModelForCTC.from_pretrained("HamdanXI/w2v2_uclass_clipped_10_seconds") - Notebooks
- Google Colab
- Kaggle
w2v2_uclass_clipped_10_seconds
This model is a fine-tuned version of facebook/w2v-bert-2.0 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for HamdanXI/w2v2_uclass_clipped_10_seconds
Base model
facebook/w2v-bert-2.0