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---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-common_voice_13_0-id
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 0.4316463864306785
language:
- id
library_name: transformers
---
<!-- 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-large-xlsr-common_voice_13_0-id
> **Note:** do not recommended to try the model through this model card
>
> Alternatively, try it through the available space [click here](https://huggingface.co/spaces/arifagustyawan/wav2vec2-large-xlsr-53-id)
> Then you can addapt the inference method available in the gradio app script. Or you can checkout at my github repository [click here](https://github.com/agustyawan-arif/wav2vec2-large-xlsr-53-id)
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_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4115
- Wer: 0.4316
## Model description
The model is based on the facebook/wav2vec2-large-xlsr-53 architecture and fine-tuned for Automatic Speech Recognition on the common_voice_13_0 dataset in Indonesian (id). It is designed to transcribe spoken language into written text.
## Intended uses & limitations
**Intended Uses:**
- Automatic Speech Recognition for Indonesian speech data.
- Transcription of spoken content in common_voice_13_0 dataset.
**Limitations:**
- The model's performance may vary on speech data outside the common_voice_13_0 dataset.
- It may not perform well on languages other than Indonesian.
## Training and evaluation data
The model was trained on the common_voice_13_0 dataset, specifically using the Indonesian (id) split for evaluation.
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0656 | 2.88 | 400 | 2.7637 | 1.0 |
| 1.1404 | 5.76 | 800 | 0.4483 | 0.6088 |
| 0.3698 | 8.63 | 1200 | 0.4029 | 0.5278 |
| 0.2695 | 11.51 | 1600 | 0.3976 | 0.5036 |
| 0.2074 | 14.39 | 2000 | 0.3988 | 0.4793 |
| 0.1796 | 17.27 | 2400 | 0.3952 | 0.4590 |
| 0.1523 | 20.14 | 2800 | 0.3986 | 0.4463 |
| 0.1352 | 23.02 | 3200 | 0.4143 | 0.4374 |
| 0.121 | 25.9 | 3600 | 0.4022 | 0.4337 |
| 0.1085 | 28.78 | 4000 | 0.4115 | 0.4316 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 |