metadata
language:
- mn
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: Whisper Medium MN with custom data - Zagi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: None
args: 'config: mn, split: test'
metrics:
- name: Wer
type: wer
value: 10.835168000658422
Whisper Medium MN with custom data - Zagi
This model is a fine-tuned version of openai/whisper-tiny on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0918
- Wer: 10.8352
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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5144 | 0.15 | 500 | 0.3790 | 43.5855 |
| 0.3922 | 0.3 | 1000 | 0.2215 | 26.4686 |
| 0.2435 | 0.46 | 1500 | 0.1774 | 21.2074 |
| 0.2275 | 0.61 | 2000 | 0.1451 | 18.1786 |
| 0.1447 | 0.76 | 2500 | 0.1279 | 15.7240 |
| 0.2028 | 0.91 | 3000 | 0.1065 | 13.0327 |
| 0.1068 | 1.06 | 3500 | 0.1002 | 12.2796 |
| 0.087 | 1.21 | 4000 | 0.0918 | 10.8352 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2