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callmesan
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vakyansh-wav2vec2-gujarati-gnm-100-audio-abuse-feature

Audio Classification
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Community
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+ ---
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+ base_model: Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: vakyansh-wav2vec2-gujarati-gnm-100-audio-abuse-feature
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+ results: []
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+ ---
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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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+
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+ # vakyansh-wav2vec2-gujarati-gnm-100-audio-abuse-feature
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+
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+ This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6313
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+ - Accuracy: 0.7403
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+ - Macro F1-score: 0.6830
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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_ratio: 0.1
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+ - num_epochs: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1-score |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------:|
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+ | 6.6694 | 0.77 | 10 | 6.6451 | 0.0387 | 0.0021 |
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+ | 6.6244 | 1.54 | 20 | 6.5275 | 0.6878 | 0.0694 |
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+ | 6.4955 | 2.31 | 30 | 6.2972 | 0.7044 | 0.4133 |
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+ | 6.2586 | 3.08 | 40 | 5.9826 | 0.7044 | 0.4133 |
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+ | 6.044 | 3.85 | 50 | 5.6760 | 0.7044 | 0.4133 |
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+ | 5.7859 | 4.62 | 60 | 5.3680 | 0.7044 | 0.4133 |
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+ | 5.506 | 5.38 | 70 | 5.0967 | 0.7044 | 0.4133 |
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+ | 5.2115 | 6.15 | 80 | 4.8565 | 0.7044 | 0.4133 |
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+ | 5.0439 | 6.92 | 90 | 4.6328 | 0.7044 | 0.4133 |
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+ | 4.924 | 7.69 | 100 | 4.4207 | 0.7044 | 0.4133 |
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+ | 4.5905 | 8.46 | 110 | 4.2046 | 0.7044 | 0.4133 |
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+ | 4.4629 | 9.23 | 120 | 3.9881 | 0.7044 | 0.4133 |
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+ | 4.2224 | 10.0 | 130 | 3.7741 | 0.7044 | 0.4133 |
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+ | 4.0429 | 10.77 | 140 | 3.5620 | 0.7044 | 0.4133 |
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+ | 3.8484 | 11.54 | 150 | 3.3434 | 0.7044 | 0.4133 |
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+ | 3.6943 | 12.31 | 160 | 3.1294 | 0.7044 | 0.4133 |
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+ | 3.4667 | 13.08 | 170 | 2.9148 | 0.7044 | 0.4133 |
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+ | 3.1164 | 13.85 | 180 | 2.7000 | 0.7044 | 0.4133 |
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+ | 2.9152 | 14.62 | 190 | 2.4912 | 0.7044 | 0.4133 |
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+ | 2.7946 | 15.38 | 200 | 2.2933 | 0.7044 | 0.4133 |
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+ | 2.5293 | 16.15 | 210 | 2.1013 | 0.7044 | 0.4133 |
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+ | 2.3488 | 16.92 | 220 | 1.9167 | 0.7044 | 0.4133 |
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+ | 2.2396 | 17.69 | 230 | 1.7418 | 0.7044 | 0.4133 |
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+ | 2.0293 | 18.46 | 240 | 1.5833 | 0.7044 | 0.4133 |
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+ | 1.8431 | 19.23 | 250 | 1.4364 | 0.7044 | 0.4133 |
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+ | 1.6658 | 20.0 | 260 | 1.3038 | 0.7044 | 0.4133 |
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+ | 1.5557 | 20.77 | 270 | 1.1904 | 0.7044 | 0.4133 |
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+ | 1.3412 | 21.54 | 280 | 1.0912 | 0.7044 | 0.4133 |
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+ | 1.2984 | 22.31 | 290 | 0.9999 | 0.7044 | 0.4133 |
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+ | 1.2517 | 23.08 | 300 | 0.9240 | 0.7044 | 0.4133 |
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+ | 1.2419 | 23.85 | 310 | 0.8693 | 0.7044 | 0.4133 |
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+ | 1.0371 | 24.62 | 320 | 0.8206 | 0.7044 | 0.4133 |
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+ | 0.922 | 25.38 | 330 | 0.7805 | 0.7044 | 0.4133 |
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+ | 0.8833 | 26.15 | 340 | 0.7281 | 0.7044 | 0.4133 |
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+ | 0.9064 | 26.92 | 350 | 0.6964 | 0.7210 | 0.4922 |
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+ | 0.7483 | 27.69 | 360 | 0.6807 | 0.7569 | 0.6771 |
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+ | 0.7677 | 28.46 | 370 | 0.6561 | 0.7762 | 0.6848 |
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+ | 0.7107 | 29.23 | 380 | 0.6450 | 0.7486 | 0.6847 |
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+ | 0.7144 | 30.0 | 390 | 0.6669 | 0.7182 | 0.6808 |
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+ | 0.6656 | 30.77 | 400 | 0.6288 | 0.7486 | 0.6764 |
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+ | 0.6896 | 31.54 | 410 | 0.6029 | 0.7652 | 0.6635 |
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+ | 0.6715 | 32.31 | 420 | 0.6152 | 0.7486 | 0.7021 |
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+ | 0.6375 | 33.08 | 430 | 0.6008 | 0.7597 | 0.6966 |
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+ | 0.6342 | 33.85 | 440 | 0.5941 | 0.7652 | 0.6892 |
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+ | 0.5992 | 34.62 | 450 | 0.6102 | 0.7459 | 0.6879 |
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+ | 0.623 | 35.38 | 460 | 0.5906 | 0.7652 | 0.6914 |
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+ | 0.5489 | 36.15 | 470 | 0.5970 | 0.7624 | 0.6610 |
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+ | 0.5553 | 36.92 | 480 | 0.6324 | 0.7320 | 0.6902 |
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+ | 0.5514 | 37.69 | 490 | 0.5974 | 0.7514 | 0.6852 |
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+ | 0.5342 | 38.46 | 500 | 0.6077 | 0.7541 | 0.6954 |
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+ | 0.5337 | 39.23 | 510 | 0.6081 | 0.7514 | 0.6872 |
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+ | 0.4809 | 40.0 | 520 | 0.6685 | 0.6961 | 0.6572 |
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+ | 0.4985 | 40.77 | 530 | 0.6262 | 0.7348 | 0.6798 |
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+ | 0.4888 | 41.54 | 540 | 0.6358 | 0.7403 | 0.6773 |
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+ | 0.4737 | 42.31 | 550 | 0.6137 | 0.7624 | 0.6911 |
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+ | 0.5249 | 43.08 | 560 | 0.6456 | 0.7293 | 0.6784 |
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+ | 0.5049 | 43.85 | 570 | 0.6503 | 0.7210 | 0.6694 |
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+ | 0.4927 | 44.62 | 580 | 0.6294 | 0.7348 | 0.6663 |
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+ | 0.4553 | 45.38 | 590 | 0.6130 | 0.7541 | 0.6835 |
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+ | 0.4631 | 46.15 | 600 | 0.6524 | 0.7238 | 0.6718 |
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+ | 0.5969 | 46.92 | 610 | 0.6233 | 0.7431 | 0.6817 |
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+ | 0.4679 | 47.69 | 620 | 0.6306 | 0.7403 | 0.6848 |
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+ | 0.4932 | 48.46 | 630 | 0.6245 | 0.7486 | 0.6922 |
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+ | 0.4723 | 49.23 | 640 | 0.6304 | 0.7431 | 0.6872 |
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+ | 0.4636 | 50.0 | 650 | 0.6313 | 0.7403 | 0.6830 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.0
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+ - Pytorch 2.0.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.13.3