---
language:
- mn
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
base_model: zagibest/whisper-small-custom-data
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small MN with custom data + Common voice - Zagi
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: mn
      split: None
      args: 'config: mn, split: test'
    metrics:
    - type: wer
      value: 43.3431629532547
      name: Wer
---

<!-- 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 + Common voice - Zagi

This model is a fine-tuned version of [zagibest/whisper-small-custom-data](https://huggingface.co/zagibest/whisper-small-custom-data) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6134
- Wer: 43.3432

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2751        | 1.98  | 500  | 0.4451          | 47.4170 |
| 0.0614        | 3.97  | 1000 | 0.4734          | 45.0579 |
| 0.0141        | 5.95  | 1500 | 0.5313          | 44.3370 |
| 0.0033        | 7.94  | 2000 | 0.5615          | 43.6490 |
| 0.0011        | 9.92  | 2500 | 0.5826          | 43.8565 |
| 0.0011        | 11.9  | 3000 | 0.6012          | 43.3705 |
| 0.0004        | 13.89 | 3500 | 0.6094          | 43.3486 |
| 0.0004        | 15.87 | 4000 | 0.6134          | 43.3432 |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2