metadata
language:
- uk
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Small Ukrainian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: uk
split: test
args: 'config: uk, split: test'
metrics:
- name: Wer
type: wer
value: 20.106509860483175
whisper-medium-uk
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3673
- Wer: 20.1065
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: 6e-06
- 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: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1947 | 0.94 | 1000 | 0.2269 | 22.7263 |
| 0.1034 | 1.89 | 2000 | 0.2102 | 20.6058 |
| 0.0572 | 2.83 | 3000 | 0.2192 | 20.3908 |
| 0.0261 | 3.77 | 4000 | 0.2483 | 21.0204 |
| 0.0112 | 4.72 | 5000 | 0.2758 | 21.1480 |
| 0.0058 | 5.66 | 6000 | 0.3166 | 20.3270 |
| 0.0026 | 6.6 | 7000 | 0.3268 | 20.5877 |
| 0.0017 | 7.55 | 8000 | 0.3483 | 20.0455 |
| 0.0006 | 8.49 | 9000 | 0.3635 | 20.0996 |
| 0.0005 | 9.43 | 10000 | 0.3673 | 20.1065 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2