Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Mongolian
wav2vec2-bert
Generated from Trainer
Instructions to use Cafet/w2v-bert-version-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cafet/w2v-bert-version-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Cafet/w2v-bert-version-final")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Cafet/w2v-bert-version-final") model = AutoModelForCTC.from_pretrained("Cafet/w2v-bert-version-final") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-version-final
results: []
pipeline_tag: automatic-speech-recognition
language:
- mn
metrics:
- wer
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 8
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.0
- Datasets 2.19.0
- Tokenizers 0.19.1