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Cafet
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w2v-bert-version-final

Automatic Speech Recognition
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Mongolian
wav2vec2-bert
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How to use from the
Use from the
Transformers library
# 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")
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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
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