metadata
base_model: openai/whisper-large-v3
datasets:
- google/fleurs
language:
- hi
library_name: peft
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Large-v3 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: 18.4303006638032
name: Wer
Whisper Large-v3 Hindi -megha sharma
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: 0.1607
- Wer: 18.4303
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: 2000
- training_steps: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1781 | 6.7797 | 2000 | 0.1785 | 21.1734 |
| 0.1519 | 13.5593 | 4000 | 0.1621 | 19.2405 |
| 0.1286 | 20.3390 | 6000 | 0.1577 | 18.7427 |
| 0.1259 | 27.1186 | 8000 | 0.1564 | 18.2058 |
| 0.111 | 33.8983 | 10000 | 0.1568 | 17.9032 |
| 0.1067 | 40.6780 | 12000 | 0.1582 | 17.8153 |
| 0.1034 | 47.4576 | 14000 | 0.1591 | 18.8403 |
| 0.0995 | 54.2373 | 16000 | 0.1603 | 18.8598 |
| 0.0929 | 61.0169 | 18000 | 0.1607 | 18.4303 |
Framework versions
- PEFT 0.12.1.dev0
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1