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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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metrics: |
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- wer |
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model-index: |
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- name: asr_skripsi_colab_common_voice |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice |
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type: common_voice |
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config: id |
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split: test |
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args: id |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.36856617647058826 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# asr_skripsi_colab_common_voice |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3839 |
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- Wer: 0.3686 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 4.4354 | 3.64 | 400 | 1.9595 | 1.0 | |
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| 0.7227 | 7.27 | 800 | 0.4532 | 0.5039 | |
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| 0.3293 | 10.91 | 1200 | 0.4277 | 0.4425 | |
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| 0.2298 | 14.55 | 1600 | 0.3947 | 0.4182 | |
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| 0.1789 | 18.18 | 2000 | 0.3960 | 0.4009 | |
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| 0.1496 | 21.82 | 2400 | 0.3793 | 0.3848 | |
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| 0.122 | 25.45 | 2800 | 0.3794 | 0.3795 | |
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| 0.1056 | 29.09 | 3200 | 0.3839 | 0.3686 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.0 |
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- Tokenizers 0.13.2 |
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