| | --- |
| | base_model: openai/whisper-base |
| | language: |
| | - vi |
| | license: apache-2.0 |
| | metrics: |
| | - wer |
| | tags: |
| | - hf-asr-leaderboard |
| | - generated_from_trainer |
| | model-index: |
| | - name: Whisper Base 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 Base Mnong |
| |
|
| | This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the MnongAudio-v2 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.7611 |
| | - Wer: 77.7127 |
| |
|
| | ## 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 | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | | 2.7389 | 0.2915 | 200 | 2.6665 | 373.6628 | |
| | | 2.2233 | 0.5831 | 400 | 2.2426 | 189.4549 | |
| | | 1.8164 | 0.8746 | 600 | 1.8990 | 131.6353 | |
| | | 1.5731 | 1.1662 | 800 | 1.6678 | 124.3760 | |
| | | 1.4459 | 1.4577 | 1000 | 1.4828 | 95.8227 | |
| | | 1.3009 | 1.7493 | 1200 | 1.3453 | 96.9689 | |
| | | 1.0242 | 2.0408 | 1400 | 1.2264 | 89.9898 | |
| | | 0.9227 | 2.3324 | 1600 | 1.1492 | 80.0815 | |
| | | 0.9111 | 2.6239 | 1800 | 1.0539 | 83.2399 | |
| | | 0.8831 | 2.9155 | 2000 | 0.9899 | 88.1814 | |
| | | 0.5906 | 3.2070 | 2200 | 0.9452 | 84.5899 | |
| | | 0.54 | 3.4985 | 2400 | 0.9017 | 79.6740 | |
| | | 0.542 | 3.7901 | 2600 | 0.8713 | 72.2364 | |
| | | 0.4606 | 4.0816 | 2800 | 0.8320 | 72.9241 | |
| | | 0.4879 | 4.3732 | 3000 | 0.8172 | 75.4712 | |
| | | 0.4033 | 4.6647 | 3200 | 0.7940 | 75.9552 | |
| | | 0.4235 | 4.9563 | 3400 | 0.7737 | 73.2552 | |
| | | 0.3638 | 5.2478 | 3600 | 0.7704 | 79.2155 | |
| | | 0.383 | 5.5394 | 3800 | 0.7641 | 77.7382 | |
| | | 0.3714 | 5.8309 | 4000 | 0.7611 | 77.7127 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.42.4 |
| | - Pytorch 2.4.0+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| | |
