metadata
base_model: openai/whisper-medium
datasets:
- google/fleurs
language:
- hi
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Medium Hindi -megha sharma
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: google/fleurs
config: hi_in
split: None
args: 'config: hi, split: test'
metrics:
- type: wer
value: 17.746973838344395
name: Wer
Whisper Medium Hindi -megha sharma
This model is a fine-tuned version of openai/whisper-medium on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.4008
- Wer: 17.7470
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: 5e-06
- train_batch_size: 8
- 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: 1000
- training_steps: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.067 | 3.3898 | 1000 | 0.2071 | 20.8024 |
| 0.0116 | 6.7797 | 2000 | 0.2594 | 19.6505 |
| 0.0032 | 10.1695 | 3000 | 0.2891 | 19.0062 |
| 0.0029 | 13.5593 | 4000 | 0.3075 | 18.9086 |
| 0.0026 | 16.9492 | 5000 | 0.3211 | 19.1722 |
| 0.0033 | 20.3390 | 6000 | 0.3254 | 18.6841 |
| 0.0014 | 23.7288 | 7000 | 0.3304 | 18.2546 |
| 0.0008 | 27.1186 | 8000 | 0.3422 | 18.4889 |
| 0.0023 | 30.5085 | 9000 | 0.3379 | 18.0886 |
| 0.0009 | 33.8983 | 10000 | 0.3525 | 18.4010 |
| 0.0006 | 37.2881 | 11000 | 0.3511 | 18.0301 |
| 0.0001 | 40.6780 | 12000 | 0.3651 | 18.1863 |
| 0.0001 | 44.0678 | 13000 | 0.3627 | 17.8446 |
| 0.0 | 47.4576 | 14000 | 0.3775 | 17.6982 |
| 0.0 | 50.8475 | 15000 | 0.3868 | 17.7079 |
| 0.0 | 54.2373 | 16000 | 0.3944 | 17.7079 |
| 0.0 | 57.6271 | 17000 | 0.4008 | 17.7470 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1