metadata
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Breeze DSW Kannada - base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs kn_in
type: google/fleurs
config: kn_in
split: test
args: kn_in
metrics:
- name: Wer
type: wer
value: 30.612702366127024
Breeze DSW Kannada - base
This model is a fine-tuned version of openai/whisper-base on the google/fleurs kn_in dataset. It achieves the following results on the evaluation set:
- Loss: 0.2258
- Wer: 30.6127
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: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.7196 | 1.03 | 100 | 0.5166 | 55.2130 |
| 0.2769 | 2.06 | 200 | 0.2532 | 36.1594 |
| 0.1896 | 4.02 | 300 | 0.2167 | 32.7298 |
| 0.1384 | 5.04 | 400 | 0.2037 | 31.8356 |
| 0.1099 | 7.0 | 500 | 0.2030 | 31.0560 |
| 0.0707 | 8.03 | 600 | 0.2153 | 31.2453 |
| 0.052 | 9.06 | 700 | 0.2258 | 30.6127 |
| 0.0375 | 11.02 | 800 | 0.2413 | 31.2204 |
| 0.0256 | 12.05 | 900 | 0.2507 | 31.0635 |
| 0.0245 | 14.01 | 1000 | 0.2549 | 31.1059 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0