---
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
---

<!-- 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 Medium 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: 0.0918
- 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