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---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: Model_G_2
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: 0.9848965131456274
---
<!-- 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. -->
# Model_G_2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0374
- Wer: 0.9849
- Cer: 0.7098
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.6149 | 1.07 | 400 | 0.4672 | 1.0114 | 0.7588 |
| 0.4341 | 2.15 | 800 | 0.2008 | 0.9972 | 0.7369 |
| 0.2665 | 3.22 | 1200 | 0.1283 | 0.9986 | 0.7180 |
| 0.2114 | 4.3 | 1600 | 0.1016 | 0.9995 | 0.7135 |
| 0.1768 | 5.37 | 2000 | 0.0774 | 0.9950 | 0.7208 |
| 0.1531 | 6.44 | 2400 | 0.0682 | 0.9933 | 0.7137 |
| 0.1352 | 7.52 | 2800 | 0.0690 | 0.9883 | 0.7022 |
| 0.1252 | 8.59 | 3200 | 0.0656 | 0.9925 | 0.7091 |
| 0.1144 | 9.66 | 3600 | 0.0521 | 0.9888 | 0.7124 |
| 0.0986 | 10.74 | 4000 | 0.0527 | 0.9915 | 0.7067 |
| 0.0875 | 11.81 | 4400 | 0.0531 | 0.9902 | 0.7057 |
| 0.0883 | 12.89 | 4800 | 0.0488 | 0.9888 | 0.7136 |
| 0.0812 | 13.96 | 5200 | 0.0461 | 0.9884 | 0.7122 |
| 0.0721 | 15.03 | 5600 | 0.0474 | 0.9884 | 0.7128 |
| 0.0681 | 16.11 | 6000 | 0.0469 | 0.9869 | 0.7243 |
| 0.0671 | 17.18 | 6400 | 0.0450 | 0.9878 | 0.7086 |
| 0.0613 | 18.26 | 6800 | 0.0492 | 0.9852 | 0.7171 |
| 0.0573 | 19.33 | 7200 | 0.0435 | 0.9852 | 0.7209 |
| 0.0531 | 20.4 | 7600 | 0.0389 | 0.9908 | 0.7071 |
| 0.0493 | 21.48 | 8000 | 0.0423 | 0.9871 | 0.7166 |
| 0.0477 | 22.55 | 8400 | 0.0416 | 0.9843 | 0.7127 |
| 0.0441 | 23.62 | 8800 | 0.0372 | 0.9864 | 0.7075 |
| 0.0412 | 24.7 | 9200 | 0.0408 | 0.9857 | 0.7118 |
| 0.0392 | 25.77 | 9600 | 0.0407 | 0.9851 | 0.7152 |
| 0.0359 | 26.85 | 10000 | 0.0383 | 0.9861 | 0.7086 |
| 0.0347 | 27.92 | 10400 | 0.0373 | 0.9852 | 0.7066 |
| 0.0327 | 28.99 | 10800 | 0.0374 | 0.9849 | 0.7098 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 1.18.3
- Tokenizers 0.13.3
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