--- license: apache-2.0 base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english datasets: - RAVDESS - SAVEE - TESS - CREMA-D tags: - generated_from_trainer - audio - speech - speech-emotion-recognition metrics: - accuracy model-index: - name: wav2vec2-large-xlsr-53-english-ser-cosine results: [] --- # wav2vec2-large-xlsr-53-english-ser-linear This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4643 - Accuracy: 0.8587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.8767 | 0.01 | 10 | 1.8078 | 0.1684 | | 1.7967 | 0.02 | 20 | 1.7544 | 0.2235 | | 1.8173 | 0.02 | 30 | 1.7072 | 0.3032 | | 1.7604 | 0.03 | 40 | 1.7162 | 0.2227 | | 1.7271 | 0.04 | 50 | 1.6655 | 0.3032 | | 1.764 | 0.05 | 60 | 1.5927 | 0.3599 | | 1.55 | 0.06 | 70 | 1.5354 | 0.3657 | | 1.5448 | 0.07 | 80 | 1.4057 | 0.4560 | | 1.5118 | 0.07 | 90 | 1.3551 | 0.4733 | | 1.354 | 0.08 | 100 | 1.2319 | 0.5596 | | 1.3675 | 0.09 | 110 | 1.1786 | 0.5735 | | 1.4058 | 0.1 | 120 | 1.0949 | 0.6105 | | 1.1595 | 0.11 | 130 | 1.0964 | 0.5908 | | 1.0444 | 0.12 | 140 | 1.1262 | 0.6212 | | 1.0483 | 0.12 | 150 | 1.0863 | 0.5982 | | 1.0439 | 0.13 | 160 | 1.0488 | 0.6491 | | 1.0129 | 0.14 | 170 | 0.9045 | 0.6549 | | 1.0171 | 0.15 | 180 | 1.0276 | 0.6270 | | 1.0867 | 0.16 | 190 | 1.0888 | 0.6023 | | 1.0646 | 0.16 | 200 | 0.9730 | 0.6311 | | 1.0403 | 0.17 | 210 | 0.9315 | 0.6582 | | 0.869 | 0.18 | 220 | 0.9686 | 0.6574 | | 0.9193 | 0.19 | 230 | 0.9076 | 0.6960 | | 1.0266 | 0.2 | 240 | 1.0796 | 0.6565 | | 0.8563 | 0.21 | 250 | 1.0173 | 0.6426 | | 0.8382 | 0.21 | 260 | 0.9155 | 0.6820 | | 0.9275 | 0.22 | 270 | 0.9397 | 0.6689 | | 0.9402 | 0.23 | 280 | 0.8919 | 0.6861 | | 0.8636 | 0.24 | 290 | 0.9795 | 0.6680 | | 1.2393 | 0.25 | 300 | 0.9872 | 0.6680 | | 0.9537 | 0.25 | 310 | 0.8181 | 0.7247 | | 0.7361 | 0.26 | 320 | 0.8470 | 0.7025 | | 0.8452 | 0.27 | 330 | 0.8045 | 0.7198 | | 0.9613 | 0.28 | 340 | 0.7530 | 0.7313 | | 0.9335 | 0.29 | 350 | 0.9019 | 0.6902 | | 0.9414 | 0.3 | 360 | 0.8981 | 0.6795 | | 0.7473 | 0.3 | 370 | 0.7532 | 0.7321 | | 0.8774 | 0.31 | 380 | 0.8953 | 0.7165 | | 0.6989 | 0.32 | 390 | 0.7381 | 0.7387 | | 0.9826 | 0.33 | 400 | 0.7128 | 0.7403 | | 0.783 | 0.34 | 410 | 0.8292 | 0.6952 | | 0.9668 | 0.35 | 420 | 0.7826 | 0.7239 | | 0.7935 | 0.35 | 430 | 0.7081 | 0.7510 | | 0.8284 | 0.36 | 440 | 0.7304 | 0.7264 | | 0.9404 | 0.37 | 450 | 0.6761 | 0.7650 | | 0.7735 | 0.38 | 460 | 0.6827 | 0.7469 | | 0.6811 | 0.39 | 470 | 0.7926 | 0.7132 | | 0.683 | 0.39 | 480 | 0.6883 | 0.7428 | | 0.6779 | 0.4 | 490 | 0.6608 | 0.7486 | | 0.6329 | 0.41 | 500 | 0.6578 | 0.7617 | | 0.5824 | 0.42 | 510 | 0.7696 | 0.7420 | | 0.6974 | 0.43 | 520 | 0.6755 | 0.7625 | | 0.7716 | 0.44 | 530 | 0.6453 | 0.7716 | | 0.7463 | 0.44 | 540 | 0.6644 | 0.7642 | | 0.7993 | 0.45 | 550 | 0.6059 | 0.7864 | | 0.606 | 0.46 | 560 | 0.6857 | 0.7461 | | 0.8619 | 0.47 | 570 | 0.6570 | 0.7560 | | 0.699 | 0.48 | 580 | 0.7400 | 0.7313 | | 0.6619 | 0.49 | 590 | 0.7014 | 0.7494 | | 0.7696 | 0.49 | 600 | 0.6621 | 0.7584 | | 0.6544 | 0.5 | 610 | 0.6826 | 0.7650 | | 0.5403 | 0.51 | 620 | 0.7464 | 0.7551 | | 0.746 | 0.52 | 630 | 0.7323 | 0.7551 | | 0.8129 | 0.53 | 640 | 0.7221 | 0.7634 | | 0.7245 | 0.53 | 650 | 0.6306 | 0.7790 | | 0.7062 | 0.54 | 660 | 0.6250 | 0.7896 | | 0.741 | 0.55 | 670 | 0.6129 | 0.7938 | | 0.7185 | 0.56 | 680 | 0.6332 | 0.7847 | | 0.7706 | 0.57 | 690 | 0.5988 | 0.7954 | | 0.8147 | 0.58 | 700 | 0.7032 | 0.7781 | | 0.5144 | 0.58 | 710 | 0.6849 | 0.7634 | | 0.9247 | 0.59 | 720 | 0.6088 | 0.7749 | | 0.629 | 0.6 | 730 | 0.6393 | 0.7806 | | 0.5908 | 0.61 | 740 | 0.5696 | 0.7913 | | 0.4951 | 0.62 | 750 | 0.6370 | 0.7765 | | 0.6358 | 0.62 | 760 | 0.6232 | 0.7979 | | 0.6396 | 0.63 | 770 | 0.6707 | 0.7905 | | 0.6947 | 0.64 | 780 | 0.6981 | 0.7683 | | 0.6748 | 0.65 | 790 | 0.6761 | 0.7765 | | 0.5607 | 0.66 | 800 | 0.6551 | 0.7921 | | 0.6991 | 0.67 | 810 | 0.6134 | 0.7905 | | 0.5793 | 0.67 | 820 | 0.5633 | 0.8118 | | 0.4755 | 0.68 | 830 | 0.6031 | 0.7929 | | 0.7645 | 0.69 | 840 | 0.5896 | 0.7962 | | 0.742 | 0.7 | 850 | 0.5811 | 0.8036 | | 0.5281 | 0.71 | 860 | 0.6449 | 0.7855 | | 0.722 | 0.72 | 870 | 0.6593 | 0.7765 | | 0.8174 | 0.72 | 880 | 0.5410 | 0.8003 | | 0.5373 | 0.73 | 890 | 0.5802 | 0.7954 | | 0.3868 | 0.74 | 900 | 0.6015 | 0.7954 | | 0.5459 | 0.75 | 910 | 0.5485 | 0.7970 | | 0.4629 | 0.76 | 920 | 0.6961 | 0.7584 | | 0.6952 | 0.76 | 930 | 0.5608 | 0.8053 | | 0.8452 | 0.77 | 940 | 0.5649 | 0.8044 | | 0.6026 | 0.78 | 950 | 0.5330 | 0.8127 | | 0.5131 | 0.79 | 960 | 0.5971 | 0.7888 | | 0.6814 | 0.8 | 970 | 0.5594 | 0.8061 | | 0.6001 | 0.81 | 980 | 0.5851 | 0.7954 | | 0.5367 | 0.81 | 990 | 0.5716 | 0.8003 | | 0.8356 | 0.82 | 1000 | 0.6519 | 0.7683 | | 0.502 | 0.83 | 1010 | 0.6180 | 0.7749 | | 0.5343 | 0.84 | 1020 | 0.5377 | 0.8053 | | 0.5288 | 0.85 | 1030 | 0.5902 | 0.7962 | | 0.5786 | 0.86 | 1040 | 0.6221 | 0.7905 | | 0.6272 | 0.86 | 1050 | 0.6688 | 0.7831 | | 0.5105 | 0.87 | 1060 | 0.6209 | 0.7880 | | 0.5806 | 0.88 | 1070 | 0.6145 | 0.7929 | | 0.5805 | 0.89 | 1080 | 0.6150 | 0.7847 | | 0.4812 | 0.9 | 1090 | 0.5812 | 0.8061 | | 0.5558 | 0.9 | 1100 | 0.6388 | 0.8044 | | 0.7507 | 0.91 | 1110 | 0.5873 | 0.8044 | | 0.7217 | 0.92 | 1120 | 0.5404 | 0.8085 | | 0.8146 | 0.93 | 1130 | 0.5449 | 0.8003 | | 0.6112 | 0.94 | 1140 | 0.5038 | 0.8151 | | 0.7305 | 0.95 | 1150 | 0.4767 | 0.8316 | | 0.3422 | 0.95 | 1160 | 0.5178 | 0.8127 | | 0.4644 | 0.96 | 1170 | 0.5073 | 0.8200 | | 0.4664 | 0.97 | 1180 | 0.4988 | 0.8184 | | 0.6223 | 0.98 | 1190 | 0.5120 | 0.8283 | | 0.6961 | 0.99 | 1200 | 0.5217 | 0.8118 | | 0.6706 | 1.0 | 1210 | 0.5235 | 0.8094 | | 0.3899 | 1.0 | 1220 | 0.5085 | 0.8184 | | 0.418 | 1.01 | 1230 | 0.5171 | 0.8135 | | 0.5011 | 1.02 | 1240 | 0.5056 | 0.8217 | | 0.2969 | 1.03 | 1250 | 0.5209 | 0.8217 | | 0.5093 | 1.04 | 1260 | 0.4921 | 0.8348 | | 0.5167 | 1.04 | 1270 | 0.5081 | 0.8274 | | 0.6382 | 1.05 | 1280 | 0.4851 | 0.8291 | | 0.3493 | 1.06 | 1290 | 0.4946 | 0.8324 | | 0.3471 | 1.07 | 1300 | 0.5122 | 0.8299 | | 0.452 | 1.08 | 1310 | 0.5592 | 0.8291 | | 0.4362 | 1.09 | 1320 | 0.5528 | 0.8266 | | 0.4224 | 1.09 | 1330 | 0.5571 | 0.8192 | | 0.333 | 1.1 | 1340 | 0.5714 | 0.8110 | | 0.2944 | 1.11 | 1350 | 0.5156 | 0.8299 | | 0.4004 | 1.12 | 1360 | 0.5208 | 0.8340 | | 0.6824 | 1.13 | 1370 | 0.5426 | 0.8258 | | 0.3746 | 1.13 | 1380 | 0.4902 | 0.8365 | | 0.3679 | 1.14 | 1390 | 0.4868 | 0.8373 | | 0.5009 | 1.15 | 1400 | 0.5192 | 0.8283 | | 0.5577 | 1.16 | 1410 | 0.4937 | 0.8316 | | 0.2566 | 1.17 | 1420 | 0.5043 | 0.8250 | | 0.6625 | 1.18 | 1430 | 0.5416 | 0.8209 | | 0.3251 | 1.18 | 1440 | 0.5146 | 0.8291 | | 0.4306 | 1.19 | 1450 | 0.5313 | 0.8266 | | 0.3159 | 1.2 | 1460 | 0.5308 | 0.8291 | | 0.3598 | 1.21 | 1470 | 0.4869 | 0.8439 | | 0.5498 | 1.22 | 1480 | 0.4670 | 0.8537 | | 0.4947 | 1.23 | 1490 | 0.4928 | 0.8463 | | 0.3948 | 1.23 | 1500 | 0.4816 | 0.8455 | | 0.3137 | 1.24 | 1510 | 0.4755 | 0.8439 | | 0.3525 | 1.25 | 1520 | 0.4972 | 0.8389 | | 0.4821 | 1.26 | 1530 | 0.4954 | 0.8381 | | 0.6099 | 1.27 | 1540 | 0.5096 | 0.8324 | | 0.3172 | 1.27 | 1550 | 0.5029 | 0.8389 | | 0.29 | 1.28 | 1560 | 0.4852 | 0.8455 | | 0.288 | 1.29 | 1570 | 0.4916 | 0.8496 | | 0.3771 | 1.3 | 1580 | 0.4734 | 0.8505 | | 0.3106 | 1.31 | 1590 | 0.4746 | 0.8431 | | 0.3494 | 1.32 | 1600 | 0.5069 | 0.8431 | | 0.3183 | 1.32 | 1610 | 0.5155 | 0.8398 | | 0.4353 | 1.33 | 1620 | 0.5242 | 0.8332 | | 0.6207 | 1.34 | 1630 | 0.5161 | 0.8340 | | 0.3241 | 1.35 | 1640 | 0.5037 | 0.8406 | | 0.3646 | 1.36 | 1650 | 0.4890 | 0.8439 | | 0.2341 | 1.37 | 1660 | 0.4884 | 0.8496 | | 0.4874 | 1.37 | 1670 | 0.4688 | 0.8562 | | 0.6701 | 1.38 | 1680 | 0.4589 | 0.8554 | | 0.391 | 1.39 | 1690 | 0.4684 | 0.8537 | | 0.3333 | 1.4 | 1700 | 0.4738 | 0.8513 | | 0.2449 | 1.41 | 1710 | 0.4753 | 0.8488 | | 0.361 | 1.41 | 1720 | 0.4946 | 0.8496 | | 0.2229 | 1.42 | 1730 | 0.4971 | 0.8463 | | 0.5915 | 1.43 | 1740 | 0.4904 | 0.8513 | | 0.1812 | 1.44 | 1750 | 0.4782 | 0.8537 | | 0.5887 | 1.45 | 1760 | 0.4702 | 0.8570 | | 0.2823 | 1.46 | 1770 | 0.4665 | 0.8570 | | 0.3397 | 1.46 | 1780 | 0.4673 | 0.8546 | | 0.4727 | 1.47 | 1790 | 0.4638 | 0.8578 | | 0.3303 | 1.48 | 1800 | 0.4636 | 0.8578 | | 0.4544 | 1.49 | 1810 | 0.4646 | 0.8587 | | 0.366 | 1.5 | 1820 | 0.4643 | 0.8587 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.1.dev0 - Tokenizers 0.15.2