--- language: - mn license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_16_0 metrics: - wer base_model: openai/whisper-large-v2 model-index: - name: Whisper Large Mongolian results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 16.0 type: mozilla-foundation/common_voice_16_0 config: mn split: None args: 'config: mn, split: test' metrics: - type: wer value: 37.23357981731187 name: Wer --- # Whisper Large Mongolian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4028 - Wer: 37.2336 ## 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: 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: 50 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3446 | 0.99 | 1000 | 0.4391 | 51.4572 | | 0.1481 | 1.98 | 2000 | 0.3765 | 42.2412 | | 0.076 | 2.97 | 3000 | 0.3830 | 39.0822 | | 0.0149 | 3.96 | 4000 | 0.4028 | 37.2336 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2