whisper-small-uz

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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

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