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---
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 Small 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: 9.378407851690294
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small MN with custom data - Zagi
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0917
- Wer: 9.3784
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0653 | 0.61 | 500 | 0.1102 | 13.5820 |
| 0.054 | 1.21 | 1000 | 0.1002 | 11.9380 |
| 0.0523 | 1.82 | 1500 | 0.0966 | 11.5903 |
| 0.0366 | 2.43 | 2000 | 0.0954 | 10.9710 |
| 0.0168 | 3.03 | 2500 | 0.0909 | 10.3866 |
| 0.0204 | 3.64 | 3000 | 0.0912 | 9.7817 |
| 0.0067 | 4.25 | 3500 | 0.0910 | 9.4936 |
| 0.0078 | 4.85 | 4000 | 0.0917 | 9.3784 |
### Framework versions
- Transformers 4.39.0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
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