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---
language:
- vi
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-vi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: COMMON_VOICE - VI
type: common_voice
config: vi
split: train+validation
args: 'Config: vi, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- 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. -->
# wav2vec2-common_voice-vi
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 - VI dataset.
It achieves the following results on the evaluation set:
- Loss: 28.9053
- Wer: 1.0
## 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 200
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu116
- Datasets 2.6.1
- Tokenizers 0.13.1
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