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---
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-czech-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: cs
split: test
args: cs
metrics:
- name: Wer
type: wer
value: 0.05608571906914448
---
<!-- 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. -->
# w2v-bert-2.0-czech-colab-CV17.0
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1030
- Wer: 0.0561
## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.6496 | 0.6579 | 300 | 0.1459 | 0.1211 |
| 0.0878 | 1.3158 | 600 | 0.1426 | 0.1034 |
| 0.0713 | 1.9737 | 900 | 0.1136 | 0.0925 |
| 0.0478 | 2.6316 | 1200 | 0.1084 | 0.0815 |
| 0.04 | 3.2895 | 1500 | 0.0980 | 0.0778 |
| 0.0309 | 3.9474 | 1800 | 0.0973 | 0.0723 |
| 0.0218 | 4.6053 | 2100 | 0.1035 | 0.0681 |
| 0.0218 | 5.2632 | 2400 | 0.0997 | 0.0658 |
| 0.0157 | 5.9211 | 2700 | 0.0924 | 0.0693 |
| 0.012 | 6.5789 | 3000 | 0.0957 | 0.0621 |
| 0.0103 | 7.2368 | 3300 | 0.0985 | 0.0623 |
| 0.0082 | 7.8947 | 3600 | 0.0942 | 0.0594 |
| 0.0051 | 8.5526 | 3900 | 0.1028 | 0.0569 |
| 0.0042 | 9.2105 | 4200 | 0.1021 | 0.0567 |
| 0.0031 | 9.8684 | 4500 | 0.1030 | 0.0561 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.20.1
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