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---
license: apache-2.0
base_model: facebook/wav2vec2-large
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: Check_Model_1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 0.37479022934924483
---
<!-- 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. -->
# Check_Model_1
This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5522
- Wer: 0.3748
- Cer: 0.1158
## 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: 0.0003
- 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 2.1839 | 3.23 | 400 | 0.8796 | 0.7306 | 0.2332 |
| 0.6388 | 6.45 | 800 | 0.8702 | 0.6410 | 0.2200 |
| 0.4695 | 9.68 | 1200 | 0.7064 | 0.5360 | 0.1632 |
| 0.3659 | 12.9 | 1600 | 0.5814 | 0.5211 | 0.1662 |
| 0.285 | 16.13 | 2000 | 0.6394 | 0.5041 | 0.1663 |
| 0.2254 | 19.35 | 2400 | 0.5889 | 0.4428 | 0.1405 |
| 0.1801 | 22.58 | 2800 | 0.5712 | 0.4013 | 0.1182 |
| 0.1392 | 25.81 | 3200 | 0.5914 | 0.3934 | 0.1177 |
| 0.1051 | 29.03 | 3600 | 0.5522 | 0.3748 | 0.1158 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 1.18.3
- Tokenizers 0.13.3
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