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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Base model
openai/whisper-small