--- language: - mn license: apache-2.0 tags: - whisper-event - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 - google/fleurs - bayartsogt/ulaanbal-v0 metrics: - wer model-index: - name: whisper-medium-mn-5 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: mn split: test metrics: - name: Wer type: wer value: 24.7268953462967 --- # whisper-medium-mn-4 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3396 - Wer: 24.7268 - Cer: 8.6712 ## 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: 32 - eval_batch_size: 16 - 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: 12000 - mixed_precision_training: Native AMP ### Training results ``` {'eval_loss': 0.3396347761154175, 'eval_wer': 24.7268953462967, 'eval_cer': 8.671234994074913, 'eval_runtime': 2202.1539, 'eval_samples_per_second': 0.856, 'eval_steps_per_second': 0.027, 'epoch': 7 .3} ``` ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2