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README.md
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---
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license: cc-by-sa-4.0
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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: wav2vec2-detect-toxic-th
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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: th
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split: validation
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args: th
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metrics:
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- name: Wer
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type: wer
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value: 0.4550641940085592
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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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# wav2vec2-detect-toxic-th
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This model is a fine-tuned version of [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th) on the common_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.5867
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- Wer: 0.4551
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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: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 16
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- seed: 42
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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: 30
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- num_epochs: 100
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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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| 5.4333 | 3.23 | 100 | 3.3662 | 1.0 |
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| 3.3254 | 6.45 | 200 | 3.2575 | 1.0 |
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| 2.5091 | 9.68 | 300 | 1.2965 | 0.5571 |
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| 1.1749 | 12.9 | 400 | 1.0687 | 0.5464 |
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| 0.9091 | 16.13 | 500 | 1.0564 | 0.4872 |
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| 0.756 | 19.35 | 600 | 1.0998 | 0.4757 |
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| 0.6527 | 22.58 | 700 | 1.1492 | 0.4829 |
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| 0.5879 | 25.81 | 800 | 1.1916 | 0.4786 |
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| 0.5184 | 29.03 | 900 | 1.2662 | 0.4815 |
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| 0.4688 | 32.26 | 1000 | 1.2109 | 0.4864 |
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| 0.4587 | 35.48 | 1100 | 1.3144 | 0.4722 |
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| 0.4005 | 38.71 | 1200 | 1.3111 | 0.4686 |
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| 0.3851 | 41.94 | 1300 | 1.3420 | 0.4786 |
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| 0.3563 | 45.16 | 1400 | 1.3679 | 0.4743 |
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| 0.3591 | 48.39 | 1500 | 1.4444 | 0.4643 |
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| 0.325 | 51.61 | 1600 | 1.4076 | 0.4722 |
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| 0.3409 | 54.84 | 1700 | 1.4586 | 0.4629 |
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| 0.3019 | 58.06 | 1800 | 1.4579 | 0.4529 |
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| 0.292 | 61.29 | 1900 | 1.4887 | 0.4522 |
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| 0.2729 | 64.52 | 2000 | 1.4966 | 0.4608 |
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| 0.2656 | 67.74 | 2100 | 1.5232 | 0.4593 |
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| 0.2575 | 70.97 | 2200 | 1.4984 | 0.4508 |
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| 0.2532 | 74.19 | 2300 | 1.5332 | 0.4544 |
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| 0.2474 | 77.42 | 2400 | 1.5301 | 0.4529 |
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| 0.2539 | 80.65 | 2500 | 1.5214 | 0.4601 |
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| 0.2526 | 83.87 | 2600 | 1.5413 | 0.4572 |
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| 0.2601 | 87.1 | 2700 | 1.5553 | 0.4608 |
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| 0.2315 | 90.32 | 2800 | 1.5768 | 0.4515 |
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| 0.2477 | 93.55 | 2900 | 1.5787 | 0.4650 |
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| 0.2363 | 96.77 | 3000 | 1.5900 | 0.4565 |
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| 0.242 | 100.0 | 3100 | 1.5867 | 0.4551 |
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### Framework versions
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- Transformers 4.28.0
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- Pytorch 2.0.1+cu118
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- Datasets 1.16.1
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- Tokenizers 0.13.3
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