metadata
language:
- mn
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-tiny
model-index:
- name: Whisper TINY MN - Zagi
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: mn
split: test
args: 'config: mn, split: test'
metrics:
- type: wer
value: 72.00196592398427
name: Wer
Whisper TINY MN - Zagi
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8723
- Wer: 72.0020
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4952 | 3.97 | 1000 | 0.8053 | 76.0266 |
| 0.2221 | 7.94 | 2000 | 0.7763 | 71.9201 |
| 0.0978 | 11.9 | 3000 | 0.8360 | 71.1118 |
| 0.059 | 15.87 | 4000 | 0.8723 | 72.0020 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2