metadata
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_17_0
language:
- uz
metrics:
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
language: - uz license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: Whisper Mediu, UZB - AISHA results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 config: uz split: None args: 'config: uz, split: test' metrics: - name: Wer type: wer value: 31.77905998468049
Whisper Medium UZB - AISHA
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2859
- Wer: 31.7790
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|---|---|---|---|---|
| 0.5187 | 0.5392 | 1000 | 0.4935 | 44.1403 |
| 0.3423 | 1.0785 | 2000 | 0.4008 | 37.6948 |
| 0.3018 | 1.6177 | 3000 | 0.3739 | 36.3575 |
| 0.2401 | 2.1569 | 4000 | 0.2821 | 31.7791 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1