metadata
base_model: openai/whisper-base
datasets:
- fleurs
language:
- ru
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base Russian 8000 - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: ru_ru
split: None
args: 'config: ru split: test'
metrics:
- type: wer
value: 25.55451630144308
name: Wer
Whisper Base Russian 8000 - Chee Li
This model is a fine-tuned version of openai/whisper-base on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.4957
- Wer: 25.5545
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 850
- training_steps: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0635 | 5.4645 | 1000 | 0.3433 | 22.5882 |
| 0.0051 | 10.9290 | 2000 | 0.3879 | 23.0492 |
| 0.0019 | 16.3934 | 3000 | 0.4186 | 23.8976 |
| 0.0011 | 21.8579 | 4000 | 0.4422 | 24.4522 |
| 0.0007 | 27.3224 | 5000 | 0.4613 | 25.0 |
| 0.0005 | 32.7869 | 6000 | 0.4781 | 25.3140 |
| 0.0004 | 38.2514 | 7000 | 0.4907 | 25.4209 |
| 0.0003 | 43.7158 | 8000 | 0.4957 | 25.5545 |
Framework versions
- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1