metadata
base_model: openai/whisper-large-v3
datasets:
- google/fleurs
language:
- fa
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Large V3 fa ft 2 - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: google/fleurs
config: fa_ir
split: None
args: 'config: fa split: test'
metrics:
- type: wer
value: 24.19210053859964
name: Wer
Whisper Large V3 fa ft 2 - 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: 0.1595
- Wer: 24.1921
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-06
- 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 |
|---|---|---|---|---|
| 0.1176 | 2.3041 | 500 | 0.1506 | 14.7516 |
| 0.0718 | 4.6083 | 1000 | 0.1432 | 20.4020 |
| 0.0501 | 6.9124 | 1500 | 0.1535 | 23.5787 |
| 0.0332 | 9.2166 | 2000 | 0.1595 | 24.1921 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1