metadata
language:
- kn
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Whisper Base Kn - Bharat Ramanathan
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: kn_in
split: test
metrics:
- type: wer
value: 32.51
name: WER
Whisper Base Kn - Bharat Ramanathan
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1974
- Wer: 30.8790
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: 96
- eval_batch_size: 64
- 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.572 | 0.1 | 500 | 0.3198 | 50.3005 |
| 0.3153 | 0.2 | 1000 | 0.2464 | 37.2652 |
| 0.2533 | 0.3 | 1500 | 0.2298 | 36.5515 |
| 0.2212 | 1.04 | 2000 | 0.2157 | 34.5229 |
| 0.2013 | 1.14 | 2500 | 0.2090 | 32.6071 |
| 0.1881 | 1.24 | 3000 | 0.2043 | 32.7198 |
| 0.1784 | 1.34 | 3500 | 0.2014 | 30.8039 |
| 0.1715 | 2.08 | 4000 | 0.2014 | 31.5928 |
| 0.166 | 2.18 | 4500 | 0.1991 | 31.2547 |
| 0.1616 | 2.28 | 5000 | 0.1974 | 30.8790 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2