metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- fsicoli/cv19-fleurs
metrics:
- wer
model-index:
- name: whisper-large-v3-pt-cv19-fleurs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fsicoli/cv19-fleurs default
type: fsicoli/cv19-fleurs
args: default
metrics:
- name: Wer
type: wer
value: 0.0756
whisper-large-v3-pt-cv19-fleurs-ct2
This model is a fine-tuned version of openai/whisper-large-v3 on the fsicoli/cv19-fleurs default dataset converted to CT2. It achieves the following results on the evaluation set:
- Loss: 0.1823
- Wer: 0.0756
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 50000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0559 | 2.2883 | 5000 | 0.1096 | 0.0730 |
| 0.0581 | 4.5767 | 10000 | 0.1326 | 0.0829 |
| 0.0225 | 6.8650 | 15000 | 0.1570 | 0.0849 |
| 0.0088 | 9.1533 | 20000 | 0.1704 | 0.0840 |
| 0.0065 | 11.4416 | 25000 | 0.1823 | 0.0849 |
| 0.006 | 13.7300 | 30000 | 0.1808 | 0.0809 |
| 0.0055 | 16.0183 | 35000 | 0.1811 | 0.0790 |
| 0.0031 | 18.3066 | 40000 | 0.1907 | 0.0784 |
| 0.0011 | 20.5950 | 45000 | 0.1852 | 0.0771 |
| 0.0003 | 22.8833 | 50000 | 0.1848 | 0.0756 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1
- Datasets 2.21.0
- Tokenizers 0.19.1