| | --- |
| | base_model: openai/whisper-medium |
| | language: |
| | - vi |
| | license: apache-2.0 |
| | metrics: |
| | - wer |
| | tags: |
| | - hf-asr-leaderboard |
| | - generated_from_trainer |
| | model-index: |
| | - name: Whisper Medium Mnong |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # Whisper Medium Mnong |
| |
|
| | This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the MnongAudio-v2 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2773 |
| | - Wer: 16.8874 |
| |
|
| | ## 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: 4000 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Wer | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | | 2.1592 | 0.2915 | 200 | 2.0293 | 117.3459 | |
| | | 1.3786 | 0.5831 | 400 | 1.3853 | 90.0408 | |
| | | 0.9096 | 0.8746 | 600 | 1.0002 | 77.8146 | |
| | | 0.5869 | 1.1662 | 800 | 0.7615 | 64.7733 | |
| | | 0.4996 | 1.4577 | 1000 | 0.5799 | 52.1141 | |
| | | 0.3741 | 1.7493 | 1200 | 0.4811 | 60.9781 | |
| | | 0.1899 | 2.0408 | 1400 | 0.4078 | 35.3031 | |
| | | 0.1792 | 2.3324 | 1600 | 0.3690 | 34.2588 | |
| | | 0.1514 | 2.6239 | 1800 | 0.3361 | 31.5079 | |
| | | 0.1758 | 2.9155 | 2000 | 0.3069 | 30.9730 | |
| | | 0.0619 | 3.2070 | 2200 | 0.3031 | 28.2731 | |
| | | 0.047 | 3.4985 | 2400 | 0.2952 | 22.1600 | |
| | | 0.0472 | 3.7901 | 2600 | 0.2914 | 24.8344 | |
| | | 0.0246 | 4.0816 | 2800 | 0.2799 | 20.0458 | |
| | | 0.0255 | 4.3732 | 3000 | 0.2849 | 23.4590 | |
| | | 0.0246 | 4.6647 | 3200 | 0.2773 | 19.5619 | |
| | | 0.0235 | 4.9563 | 3400 | 0.2736 | 20.4279 | |
| | | 0.0088 | 5.2478 | 3600 | 0.2795 | 19.9440 | |
| | | 0.0076 | 5.5394 | 3800 | 0.2786 | 17.0657 | |
| | | 0.0057 | 5.8309 | 4000 | 0.2773 | 16.8874 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.42.4 |
| | - Pytorch 2.4.0+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| | |
