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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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model-index: |
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- name: wav2vec2-base-finetune-vi-v2 |
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results: [] |
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widget: |
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- example_title: SOICT 2023 - SLU public test 1 |
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src: >- |
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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: >- |
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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: >- |
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https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/1ArUTGWJQ9YALH2xaNhU6GV.wav |
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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: 0.2294 |
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- Wer: 0.1457 |
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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: 20 |
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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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| 13.1354 | 0.67 | 500 | 3.0881 | 1.0186 | |
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| 2.2088 | 1.34 | 1000 | 0.9805 | 0.4257 | |
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| 1.122 | 2.0 | 1500 | 0.4928 | 0.2850 | |
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| 0.7567 | 2.67 | 2000 | 0.4217 | 0.2466 | |
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| 0.627 | 3.34 | 2500 | 0.3889 | 0.2212 | |
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| 0.5369 | 4.01 | 3000 | 0.3496 | 0.2131 | |
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| 0.4485 | 4.67 | 3500 | 0.3239 | 0.1994 | |
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| 0.4478 | 5.34 | 4000 | 0.3143 | 0.1944 | |
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| 0.4013 | 6.01 | 4500 | 0.2989 | 0.1871 | |
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| 0.4542 | 6.68 | 5000 | 0.2996 | 0.1871 | |
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| 0.351 | 7.34 | 5500 | 0.2719 | 0.1736 | |
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| 0.3236 | 8.01 | 6000 | 0.2865 | 0.1702 | |
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| 0.2954 | 8.68 | 6500 | 0.2708 | 0.1636 | |
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| 0.3533 | 9.35 | 7000 | 0.2712 | 0.1639 | |
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| 0.2996 | 10.01 | 7500 | 0.2609 | 0.1621 | |
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| 0.2595 | 10.68 | 8000 | 0.2450 | 0.1627 | |
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| 0.2914 | 11.35 | 8500 | 0.2748 | 0.1596 | |
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| 0.253 | 12.02 | 9000 | 0.2496 | 0.1552 | |
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| 0.2314 | 12.68 | 9500 | 0.2496 | 0.1549 | |
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| 0.2232 | 13.35 | 10000 | 0.2594 | 0.1557 | |
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| 0.2206 | 14.02 | 10500 | 0.2485 | 0.1529 | |
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| 0.2026 | 14.69 | 11000 | 0.2365 | 0.1522 | |
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| 0.2009 | 15.35 | 11500 | 0.2396 | 0.1513 | |
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| 0.205 | 16.02 | 12000 | 0.2433 | 0.1499 | |
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| 0.207 | 16.69 | 12500 | 0.2363 | 0.1496 | |
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| 0.1895 | 17.36 | 13000 | 0.2280 | 0.1481 | |
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| 0.1991 | 18.02 | 13500 | 0.2352 | 0.1481 | |
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| 0.2109 | 18.69 | 14000 | 0.2353 | 0.1477 | |
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| 0.1959 | 19.36 | 14500 | 0.2294 | 0.1457 | |
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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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