metadata

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
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Large MN - Ankhbayasgalan Davaadorj
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 16.1 & FLEURS
          type: mozilla-foundation/common_voice_16_1
          config: mn
          split: None
          args: 'config: mn, split: test+validation'
        metrics:
          - name: Wer
            type: wer
            value: 37.049667235025574

Whisper Large MN - Ankhbayasgalan Davaadorj

This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 16.1 & FLEURS dataset. It achieves the following results on the evaluation set:

Loss: 0.3245
Wer: 37.0497

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: 40
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4691	0.3	100	0.5472	57.2191
0.3191	0.6	200	0.4417	49.0237
0.2677	0.9	300	0.3791	43.3530
0.1486	1.2	400	0.3560	40.1188
0.1387	1.5	500	0.3430	37.8912
0.1396	1.8	600	0.3245	37.0497

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2