metadata
license: apache-2.0
base_model: arun100/whisper-base-hi-2
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Base Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs hi_in
type: google/fleurs
config: hi_in
split: test
args: hi_in
metrics:
- name: Wer
type: wer
value: 27.72060783790989
Whisper Base Hindi
This model is a fine-tuned version of arun100/whisper-base-hi-2 on the google/fleurs hi_in dataset. It achieves the following results on the evaluation set:
- Loss: 0.4468
- Wer: 27.7206
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-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4805 | 33.0 | 250 | 0.4868 | 30.4186 |
| 0.3559 | 66.0 | 500 | 0.4417 | 29.0909 |
| 0.2655 | 99.0 | 750 | 0.4307 | 28.2165 |
| 0.1987 | 133.0 | 1000 | 0.4350 | 27.8326 |
| 0.1472 | 166.0 | 1250 | 0.4468 | 27.7206 |
| 0.1061 | 199.0 | 1500 | 0.4640 | 28.0992 |
| 0.0767 | 233.0 | 1750 | 0.4835 | 28.5737 |
| 0.0541 | 266.0 | 2000 | 0.5032 | 28.6857 |
| 0.0396 | 299.0 | 2250 | 0.5202 | 28.7763 |
| 0.03 | 333.0 | 2500 | 0.5353 | 29.2029 |
| 0.0237 | 366.0 | 2750 | 0.5479 | 28.9096 |
| 0.0195 | 399.0 | 3000 | 0.5587 | 28.9096 |
| 0.0163 | 433.0 | 3250 | 0.5683 | 28.9469 |
| 0.014 | 466.0 | 3500 | 0.5767 | 29.1336 |
| 0.0121 | 499.0 | 3750 | 0.5838 | 29.3415 |
| 0.0108 | 533.0 | 4000 | 0.5900 | 29.2775 |
| 0.01 | 566.0 | 4250 | 0.5951 | 29.6081 |
| 0.0093 | 599.0 | 4500 | 0.5988 | 29.4855 |
| 0.0088 | 633.0 | 4750 | 0.6012 | 29.5281 |
| 0.0087 | 666.0 | 5000 | 0.6020 | 29.4268 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0