transformers safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer mn dataset:mozilla-foundation/common_voice_16_1 base_model:openai/whisper-large-v3 license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- mn
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: 'Whisper Small MN - Ankhbayasgalan Davaadorj '
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      config: mn
      split: test
      args: 'config: mn, split: test+validation'
    metrics:
    - name: Wer
      type: wer
      value: 67.84162771514984
---

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# Whisper Small MN - Ankhbayasgalan Davaadorj 

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5096
- Wer: 67.8416

## 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: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0832        | 3.94  | 1000 | 0.3988          | 73.6211 |
| 0.0051        | 7.87  | 2000 | 0.4563          | 66.0654 |
| 0.0004        | 11.81 | 3000 | 0.5096          | 67.8416 |


### Framework versions

- Transformers 4.37.2
- Pytorch 1.12.1+cu116
- Datasets 2.17.0
- Tokenizers 0.15.2
