---
language:
- mn
license: apache-2.0
base_model: zagibest/whisper-small-custom-data
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: Whisper Small MN with custom data + Common voice + Google Fluers - Zagi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: mn_mn
      split: None
      args: 'config: mn, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 34.56211146253681
---

<!-- 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 + Google Fluers - Zagi

This model is a fine-tuned version of [zagibest/whisper-small-custom-data](https://huggingface.co/zagibest/whisper-small-custom-data) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5343
- Wer: 34.5621

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1792        | 2.59  | 500  | 0.4204          | 38.2103 |
| 0.0121        | 5.18  | 1000 | 0.4673          | 36.5634 |
| 0.0034        | 7.77  | 1500 | 0.4964          | 35.6309 |
| 0.0009        | 10.36 | 2000 | 0.5044          | 34.7366 |
| 0.0007        | 12.95 | 2500 | 0.5166          | 34.7366 |
| 0.0004        | 15.54 | 3000 | 0.5271          | 34.5785 |
| 0.0003        | 18.13 | 3500 | 0.5323          | 34.5948 |
| 0.0003        | 20.73 | 4000 | 0.5343          | 34.5621 |


### Framework versions

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