metadata
language:
- hu
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: Whisper medium Hu CV18
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 18.0
type: fleurs
config: hu_hu
split: None
args: hu_hu
metrics:
- name: Wer
type: wer
value: 20.222211012182512
Whisper medium Hu CV18
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 18.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4280
- Wer Ortho: 26.6073
- Wer: 20.2222
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: 6.25e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.195 | 0.2757 | 200 | 0.4397 | 29.9304 | 25.2390 |
| 0.1548 | 0.5513 | 400 | 0.4146 | 28.6874 | 23.6903 |
| 0.126 | 0.8270 | 600 | 0.4022 | 28.3332 | 22.4632 |
| 0.077 | 1.1027 | 800 | 0.4045 | 27.5831 | 21.3673 |
| 0.0744 | 1.3784 | 1000 | 0.4096 | 27.8566 | 21.4102 |
| 0.0718 | 1.6540 | 1200 | 0.3955 | 26.5619 | 20.7733 |
| 0.0681 | 1.9297 | 1400 | 0.3990 | 26.5267 | 20.6207 |
| 0.032 | 2.2054 | 1600 | 0.4056 | 25.8913 | 20.1680 |
| 0.0323 | 2.4810 | 1800 | 0.4232 | 26.1182 | 20.2878 |
| 0.0356 | 2.7567 | 2000 | 0.4280 | 26.6073 | 20.2222 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.3.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1