metadata
base_model: openai/whisper-large-v3
datasets:
- fleurs
language:
- pl
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Large V3 pl preprocessed - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: pl_pl
split: None
args: 'config: pl split: test'
metrics:
- type: wer
value: 340.8656818962556
name: Wer
Whisper Large V3 pl preprocessed - Chee Li
This model is a fine-tuned version of openai/whisper-large-v3 on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.1210
- Wer: 340.8657
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: 250
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0131 | 2.5126 | 500 | 0.1070 | 30.9447 |
| 0.001 | 5.0251 | 1000 | 0.1158 | 186.2041 |
| 0.0011 | 7.5377 | 1500 | 0.1201 | 375.3281 |
| 0.0002 | 10.0503 | 2000 | 0.1210 | 340.8657 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1