metadata
language:
- ka
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Ka -Tripti
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Fleurs
type: google/fleurs
config: kn_in
split: None
args: 'config: ka, split: test'
metrics:
- name: Wer
type: wer
value: 42.97683977551181
Whisper Small Ka -Tripti
This model is a fine-tuned version of openai/whisper-small on the Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.2298
- Wer: 42.9768
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: 16
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0238 | 6.0606 | 1000 | 0.1512 | 45.2296 |
| 0.0021 | 12.1212 | 2000 | 0.2023 | 43.6566 |
| 0.0001 | 18.1818 | 3000 | 0.2234 | 43.0085 |
| 0.0001 | 24.2424 | 4000 | 0.2298 | 42.9768 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1