Whisper-small-uz-V2

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2628
  • Wer: 23.1694

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: 1e-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: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.4646 0.6720 1000 0.3688 32.9186
0.268 1.3441 2000 0.2925 26.4408
0.1436 2.0161 3000 0.2646 23.4813
0.1436 2.6882 4000 0.2628 23.1694

Using the Model

Use the model from the Hugging Face platform, you can use the following code:

from transformers import pipeline

# Load the model
pipe = pipeline("automatic-speech-recognition", model="tukhtashevshohruh/whisper-small-uz")

# Convert the audio file to text
audio_file = "my_audio.wav"  # Replace with the name of your own file
text = pipe(audio_file)

# Print the result
print("Text:", text['text'])

Framework versions

  • Transformers 4.49.0
  • Pytorch 2.5.1+cu121
  • Datasets 3.3.2
  • Tokenizers 0.21.0
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Dataset used to train tukhtashevshohruh/whisper-small-uz

Evaluation results