Instructions to use pdabo1607/Vietnamese_Wav2Vec_Finetune_round2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pdabo1607/Vietnamese_Wav2Vec_Finetune_round2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pdabo1607/Vietnamese_Wav2Vec_Finetune_round2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("pdabo1607/Vietnamese_Wav2Vec_Finetune_round2") model = AutoModelForCTC.from_pretrained("pdabo1607/Vietnamese_Wav2Vec_Finetune_round2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Vietnamese_Wav2Vec_Finetune_round2
This model is a fine-tuned version of pdabo1607/Vietnamese_Wav2Vec_Finetune on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6419
- Wer: 0.3930
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0061 | 0.9434 | 400 | 0.7866 | 0.4910 |
| 0.9727 | 1.8868 | 800 | 0.7485 | 0.4661 |
| 0.9473 | 2.8302 | 1200 | 0.7261 | 0.4480 |
| 0.9033 | 3.7736 | 1600 | 0.6793 | 0.4289 |
| 0.8801 | 4.7170 | 2000 | 0.6727 | 0.4187 |
| 0.841 | 5.6604 | 2400 | 0.6516 | 0.4098 |
| 0.8444 | 6.6038 | 2800 | 0.6555 | 0.4033 |
| 0.8071 | 7.5472 | 3200 | 0.6455 | 0.3991 |
| 0.8118 | 8.4906 | 3600 | 0.6454 | 0.3944 |
| 0.8058 | 9.4340 | 4000 | 0.6419 | 0.3930 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 4.4.1
- Tokenizers 0.20.3
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