metadata
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-czech-colab-cv16
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_0
type: common_voice_16_0
config: cs
split: test
args: cs
metrics:
- name: Wer
type: wer
value: 0.05733702722973076
w2v-bert-2.0-czech-colab-cv16
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1023
- Wer: 0.0573
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: 64
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.5297 | 0.66 | 300 | 0.1448 | 0.1299 |
| 0.0886 | 1.32 | 600 | 0.1353 | 0.1051 |
| 0.0717 | 1.98 | 900 | 0.1157 | 0.0861 |
| 0.0463 | 2.64 | 1200 | 0.0994 | 0.0759 |
| 0.0404 | 3.3 | 1500 | 0.1054 | 0.0724 |
| 0.0314 | 3.96 | 1800 | 0.0915 | 0.0694 |
| 0.0227 | 4.63 | 2100 | 0.0926 | 0.0664 |
| 0.0205 | 5.29 | 2400 | 0.0992 | 0.0652 |
| 0.0161 | 5.95 | 2700 | 0.0932 | 0.0654 |
| 0.0124 | 6.61 | 3000 | 0.0902 | 0.0629 |
| 0.0097 | 7.27 | 3300 | 0.0970 | 0.0612 |
| 0.0081 | 7.93 | 3600 | 0.0946 | 0.0602 |
| 0.0054 | 8.59 | 3900 | 0.0962 | 0.0588 |
| 0.0048 | 9.25 | 4200 | 0.1029 | 0.0579 |
| 0.0034 | 9.91 | 4500 | 0.1023 | 0.0573 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.1