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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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widget: |
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- example_title: SOICT 2023 - SLU public test 1 |
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src: https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/055R7BruAa333g9teFfamQH.wav |
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- example_title: SOICT 2023 - SLU public test 2 |
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src: https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/0BLHhoJexE8THB8BrsZxWbh.wav |
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- example_title: SOICT 2023 - SLU public test 3 |
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src: https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/1ArUTGWJQ9YALH2xaNhU6GV.wav |
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base_model: nguyenvulebinh/wav2vec2-base-vietnamese-250h |
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model-index: |
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- name: wav2vec2-base-finetune-vi-v2 |
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results: [] |
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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-base-finetune-vi-v2 |
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This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2188 |
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- Wer: 0.1391 |
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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: 8 |
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- eval_batch_size: 8 |
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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: 1000 |
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- num_epochs: 24 |
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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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| 4.3873 | 0.67 | 500 | 2.4321 | 0.9719 | |
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| 1.4812 | 1.34 | 1000 | 0.5449 | 0.3062 | |
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| 0.7731 | 2.0 | 1500 | 0.3793 | 0.2263 | |
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| 0.542 | 2.67 | 2000 | 0.3021 | 0.2002 | |
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| 0.4461 | 3.34 | 2500 | 0.2905 | 0.1862 | |
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| 0.4175 | 4.01 | 3000 | 0.2687 | 0.1771 | |
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| 0.3878 | 4.67 | 3500 | 0.2958 | 0.1751 | |
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| 0.3373 | 5.34 | 4000 | 0.2713 | 0.1721 | |
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| 0.3046 | 6.01 | 4500 | 0.2505 | 0.1616 | |
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| 0.2933 | 6.68 | 5000 | 0.2561 | 0.1611 | |
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| 0.285 | 7.34 | 5500 | 0.2405 | 0.1617 | |
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| 0.2998 | 8.01 | 6000 | 0.2363 | 0.1578 | |
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| 0.2486 | 8.68 | 6500 | 0.2254 | 0.1570 | |
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| 0.2682 | 9.35 | 7000 | 0.2306 | 0.1547 | |
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| 0.2327 | 10.01 | 7500 | 0.2289 | 0.1537 | |
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| 0.2141 | 10.68 | 8000 | 0.2383 | 0.1499 | |
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| 0.2124 | 11.35 | 8500 | 0.2261 | 0.15 | |
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| 0.2156 | 12.02 | 9000 | 0.2142 | 0.1511 | |
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| 0.2082 | 12.68 | 9500 | 0.2386 | 0.1467 | |
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| 0.1814 | 13.35 | 10000 | 0.2301 | 0.1448 | |
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| 0.1836 | 14.02 | 10500 | 0.2302 | 0.1446 | |
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| 0.18 | 14.69 | 11000 | 0.2244 | 0.1445 | |
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| 0.1756 | 15.35 | 11500 | 0.2280 | 0.1439 | |
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| 0.1693 | 16.02 | 12000 | 0.2307 | 0.1426 | |
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| 0.1588 | 16.69 | 12500 | 0.2164 | 0.1422 | |
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| 0.1587 | 17.36 | 13000 | 0.2198 | 0.1417 | |
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| 0.1738 | 18.02 | 13500 | 0.2282 | 0.1411 | |
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| 0.1524 | 18.69 | 14000 | 0.2274 | 0.1394 | |
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| 0.1569 | 19.36 | 14500 | 0.2178 | 0.1396 | |
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| 0.1433 | 20.03 | 15000 | 0.2200 | 0.1413 | |
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| 0.1512 | 20.69 | 15500 | 0.2193 | 0.1382 | |
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| 0.1375 | 21.36 | 16000 | 0.2174 | 0.1393 | |
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| 0.1302 | 22.03 | 16500 | 0.2246 | 0.1391 | |
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| 0.146 | 22.7 | 17000 | 0.2222 | 0.1392 | |
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| 0.1265 | 23.36 | 17500 | 0.2188 | 0.1391 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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