Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Igbo
wav2vec2-bert
Generated from Trainer
Instructions to use oyemade/w2v-bert-2.0-igbo-CV17.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oyemade/w2v-bert-2.0-igbo-CV17.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="oyemade/w2v-bert-2.0-igbo-CV17.0")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("oyemade/w2v-bert-2.0-igbo-CV17.0") model = AutoModelForCTC.from_pretrained("oyemade/w2v-bert-2.0-igbo-CV17.0") - Notebooks
- Google Colab
- Kaggle
w2v-bert-2.0-igbo-CV17.0
This model is a fine-tuned version of oyemade/w2v-bert-2.0-igbo-CV17.0 on the common_voice_17_0 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: 2
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Downloads last month
- 3
Model tree for oyemade/w2v-bert-2.0-igbo-CV17.0
Unable to build the model tree, the base model loops to the model itself. Learn more.