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---
base_model: facebook/w2v-bert-2.0
datasets:
- common_voice_17_0
library_name: transformers
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-2_6_datasets
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: ml
split: validation
args: ml
metrics:
- type: wer
value: 0.43922053819981444
name: Wer
---
<!-- 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_6_datasets
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: 0.5077
- Wer: 0.4392
## 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: 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 |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 1.1114 | 0.4038 | 600 | 0.6364 | 0.6514 |
| 0.1782 | 0.8075 | 1200 | 0.5620 | 0.6127 |
| 0.1374 | 1.2113 | 1800 | 0.4943 | 0.5654 |
| 0.1156 | 1.6151 | 2400 | 0.4415 | 0.5376 |
| 0.1068 | 2.0188 | 3000 | 0.4187 | 0.5249 |
| 0.0838 | 2.4226 | 3600 | 0.4778 | 0.5320 |
| 0.0834 | 2.8264 | 4200 | 0.4186 | 0.5091 |
| 0.0703 | 3.2301 | 4800 | 0.4538 | 0.5363 |
| 0.0636 | 3.6339 | 5400 | 0.4287 | 0.5314 |
| 0.0609 | 4.0377 | 6000 | 0.4013 | 0.4989 |
| 0.0462 | 4.4415 | 6600 | 0.4053 | 0.4964 |
| 0.047 | 4.8452 | 7200 | 0.4289 | 0.4766 |
| 0.0377 | 5.2490 | 7800 | 0.3875 | 0.4933 |
| 0.0352 | 5.6528 | 8400 | 0.3906 | 0.4881 |
| 0.033 | 6.0565 | 9000 | 0.4192 | 0.4667 |
| 0.0243 | 6.4603 | 9600 | 0.4113 | 0.4723 |
| 0.0244 | 6.8641 | 10200 | 0.4393 | 0.4708 |
| 0.0189 | 7.2678 | 10800 | 0.4255 | 0.4630 |
| 0.0167 | 7.6716 | 11400 | 0.4219 | 0.4646 |
| 0.0157 | 8.0754 | 12000 | 0.4398 | 0.4429 |
| 0.0107 | 8.4791 | 12600 | 0.4546 | 0.4507 |
| 0.0095 | 8.8829 | 13200 | 0.4949 | 0.4426 |
| 0.0072 | 9.2867 | 13800 | 0.4972 | 0.4473 |
| 0.0059 | 9.6904 | 14400 | 0.5077 | 0.4392 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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