metadata
language:
- sr
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium Sr Fleurs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Google Fleurs
type: google/fleurs
config: sr_rs
split: test
args: sr_rs
metrics:
- name: Wer
type: wer
value: 0.17942107976725344
Whisper Medium Sr Fleurs
This model is a fine-tuned version of openai/whisper-medium on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.3577
- Wer Ortho: 0.2072
- Wer: 0.1794
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: 16
- 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: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0341 | 2.49 | 500 | 0.2704 | 0.2074 | 0.1789 |
| 0.0109 | 4.98 | 1000 | 0.3091 | 0.2075 | 0.1774 |
| 0.006 | 7.46 | 1500 | 0.3143 | 0.2031 | 0.1713 |
| 0.0081 | 9.95 | 2000 | 0.3284 | 0.2070 | 0.1754 |
| 0.0038 | 12.44 | 2500 | 0.3426 | 0.2099 | 0.1805 |
| 0.0042 | 14.93 | 3000 | 0.3630 | 0.2113 | 0.1821 |
| 0.0032 | 17.41 | 3500 | 0.3659 | 0.2089 | 0.1791 |
| 0.0046 | 19.9 | 4000 | 0.3577 | 0.2072 | 0.1794 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3