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Erfan97
/
whisper-tiny_fa_fleur

Automatic Speech Recognition
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Persian
whisper
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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="Erfan97/whisper-tiny_fa_fleur")
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Erfan97/whisper-tiny_fa_fleur")
model = AutoModelForMultimodalLM.from_pretrained("Erfan97/whisper-tiny_fa_fleur")
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Erfan97/whisper-tiny_fa_fleurs

This model is a fine-tuned version of hajar817/whisper_tiny_fa_cv13 on the google/fleurs 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: 6.15044e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch 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
  • training_steps: 6000
  • mixed_precision_training: Native AMP

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu121
  • Tokenizers 0.21.0
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Dataset used to train Erfan97/whisper-tiny_fa_fleur