metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-Vietnamese-colab-CV17.0
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: vi
split: test
args: vi
metrics:
- name: Wer
type: wer
value: 0.2728716645489199
w2v-bert-2.0-Vietnamese-colab-CV17.0
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.0607
- Wer: 0.2729
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.8799 | 3.2609 | 300 | 0.7434 | 0.3899 |
| 0.1626 | 6.5217 | 600 | 0.8157 | 0.3578 |
| 0.0823 | 9.7826 | 900 | 0.8759 | 0.3704 |
| 0.04 | 13.0435 | 1200 | 0.9129 | 0.3195 |
| 0.0169 | 16.3043 | 1500 | 0.9113 | 0.2904 |
| 0.0056 | 19.5652 | 1800 | 0.9906 | 0.2809 |
| 0.0016 | 22.8261 | 2100 | 1.0506 | 0.2848 |
| 0.0005 | 26.0870 | 2400 | 1.0502 | 0.2730 |
| 0.0002 | 29.3478 | 2700 | 1.0607 | 0.2729 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1