transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer mn dataset:mozilla-foundation/common_voice_16_1 dataset:google/fleurs base_model:openai/whisper-large-v2 license:apache-2.0 endpoints_compatible region:us
---
language:
- mn
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
- google/fleurs
model-index:
- name: Whisper Large V2 MN - Ankhbayasgalan Davaadorj
  results: []
---

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# Whisper Large V2 MN - Ankhbayasgalan Davaadorj

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 16.1 & FLEURS dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2431
- eval_wer: 19.6462
- eval_runtime: 212.4702
- eval_samples_per_second: 0.48
- eval_steps_per_second: 0.122
- epoch: 10.99
- step: 1000

## 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: 8
- eval_batch_size: 4
- 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: 2000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
