metadata

library_name: transformers
language:
  - uz
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: 'Whisper-small-uz-V2 '
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: uz
          split: None
          args: 'config: uz, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 23.16938470534728

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