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---
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
|