---
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