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---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: w2v-bert-2.0-lg-CV-Fleurs-filtered-100hrs-v12
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: lg_ug
split: test
args: lg_ug
metrics:
- name: Wer
type: wer
value: 0.43848396501457726
---
<!-- 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-lg-CV-Fleurs-filtered-100hrs-v12
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4980
- Wer: 0.4385
- Cer: 0.0852
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use 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_ratio: 0.1
- num_epochs: 70
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 0.9834 | 1.0 | 7125 | 0.3827 | 0.4584 | 0.0921 |
| 0.1914 | 2.0 | 14250 | 0.3460 | 0.4394 | 0.0837 |
| 0.165 | 3.0 | 21375 | 0.3377 | 0.4375 | 0.0827 |
| 0.1519 | 4.0 | 28500 | 0.3337 | 0.4246 | 0.0805 |
| 0.1458 | 5.0 | 35625 | 0.3242 | 0.4234 | 0.0789 |
| 0.1413 | 6.0 | 42750 | 0.3294 | 0.4329 | 0.0816 |
| 0.1395 | 7.0 | 49875 | 0.3441 | 0.4431 | 0.0866 |
| 0.1325 | 8.0 | 57000 | 0.3263 | 0.4332 | 0.0867 |
| 0.1191 | 9.0 | 64125 | 0.3278 | 0.4065 | 0.0788 |
| 0.1075 | 10.0 | 71250 | 0.3203 | 0.4418 | 0.0808 |
| 0.0974 | 11.0 | 78375 | 0.3304 | 0.4036 | 0.0771 |
| 0.0892 | 12.0 | 85500 | 0.3307 | 0.4263 | 0.0819 |
| 0.0802 | 13.0 | 92625 | 0.3530 | 0.4107 | 0.0785 |
| 0.0728 | 14.0 | 99750 | 0.3478 | 0.4156 | 0.0795 |
| 0.0632 | 15.0 | 106875 | 0.3620 | 0.4052 | 0.0787 |
| 0.0567 | 16.0 | 114000 | 0.3620 | 0.4219 | 0.0796 |
| 0.0484 | 17.0 | 121125 | 0.4135 | 0.4114 | 0.0787 |
| 0.0423 | 18.0 | 128250 | 0.4220 | 0.4186 | 0.0814 |
| 0.0358 | 19.0 | 135375 | 0.4476 | 0.4303 | 0.0825 |
| 0.0311 | 20.0 | 142500 | 0.4913 | 0.4134 | 0.0806 |
| 0.0277 | 21.0 | 149625 | 0.4910 | 0.4411 | 0.0850 |
| 0.0238 | 22.0 | 156750 | 0.5097 | 0.4269 | 0.0821 |
| 0.0214 | 23.0 | 163875 | 0.4755 | 0.4248 | 0.0837 |
| 0.0194 | 24.0 | 171000 | 0.4839 | 0.4249 | 0.0826 |
| 0.0178 | 25.0 | 178125 | 0.5302 | 0.4294 | 0.0828 |
| 0.016 | 26.0 | 185250 | 0.4980 | 0.4385 | 0.0852 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.1.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
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