metadata
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-tiny-finetune-hindi-fleurs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: google/fleurs
config: hi_in
split: train+test
args: hi_in
metrics:
- name: Wer
type: wer
value: 0.42621638924455824
whisper-tiny-finetune-hindi-fleurs
This model is a fine-tuned version of openai/whisper-tiny on the google/fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.8315
- Wer Ortho: 0.4313
- Wer: 0.4262
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 1.8112 | 1.39 | 100 | 1.7274 | 0.6323 | 0.6258 |
| 1.0387 | 2.78 | 200 | 1.1194 | 0.5130 | 0.5072 |
| 0.7671 | 4.17 | 300 | 0.9671 | 0.4665 | 0.4613 |
| 0.5283 | 5.56 | 400 | 0.8840 | 0.4494 | 0.4440 |
| 0.4458 | 6.94 | 500 | 0.8315 | 0.4313 | 0.4262 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0