---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-meduim-mongolian
  results: []
datasets:
- Cafet/whisper-mongolian-final
language:
- mn
library_name: transformers
pipeline_tag: automatic-speech-recognition
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# whisper-meduim-mongolian

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on custom.
It achieves the following results on the evaluation set:
- Loss: 0.3098
- Wer: 26.8664

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- 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: 2000
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.3034        | 0.9398 | 2000  | 0.4135          | 45.1152 |
| 0.1443        | 1.8797 | 4000  | 0.3127          | 35.3290 |
| 0.0618        | 2.8195 | 6000  | 0.3038          | 31.0534 |
| 0.0179        | 3.7594 | 8000  | 0.3042          | 28.3673 |
| 0.0028        | 4.6992 | 10000 | 0.3098          | 26.8664 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0
- Datasets 2.19.0
- Tokenizers 0.19.1