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: 18.5864896524795
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: 0.3821
- Wer: 18.5865
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: 15000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0669 | 3.3898 | 1000 | 0.2078 | 20.8512 |
| 0.0131 | 6.7797 | 2000 | 0.2584 | 20.0312 |
| 0.002 | 10.1695 | 3000 | 0.3048 | 19.2698 |
| 0.0024 | 13.5593 | 4000 | 0.3192 | 19.1429 |
| 0.0025 | 16.9492 | 5000 | 0.3127 | 19.0941 |
| 0.0008 | 20.3390 | 6000 | 0.3412 | 19.1429 |
| 0.0008 | 23.7288 | 7000 | 0.3438 | 18.3913 |
| 0.0011 | 27.1186 | 8000 | 0.3465 | 18.8501 |
| 0.001 | 30.5085 | 9000 | 0.3549 | 18.5377 |
| 0.0002 | 33.8983 | 10000 | 0.3551 | 18.0594 |
| 0.0 | 37.2881 | 11000 | 0.3689 | 18.3522 |
| 0.0 | 40.6780 | 12000 | 0.3721 | 18.3229 |
| 0.0 | 44.0678 | 13000 | 0.3821 | 18.5865 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1