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bambara-vqvae

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metadata
language:
  - bm
tags:
  - generated_from_trainer
model-index:
  - name: bambara-vqvae
    results: []

bambara-vqvae

This model was trained from scratch on the oza75/bambara-tts dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5621
  • Mse: 0.0441
  • Snr: 21.0636

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: 5e-05
  • train_batch_size: 128
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Mse Snr
4.8671 0.4630 100 4.7616 0.0956 17.7057
4.5386 0.9259 200 4.5686 0.0972 17.6335
4.3309 1.3889 300 5.1175 0.0859 18.1749
3.9055 1.8519 400 3.6748 0.0930 17.8268
3.5462 2.3148 500 3.9152 0.0950 17.7360
3.363 2.7778 600 2.7866 0.0905 17.9436
3.2075 3.2407 700 3.7970 0.0883 18.0506
3.1522 3.7037 800 3.0179 0.0886 18.0392
2.9735 4.1667 900 4.4034 0.0810 18.4270
2.9057 4.6296 1000 2.4479 0.0832 18.3087
2.9167 5.0926 1100 2.3498 0.0879 18.0715
2.6331 5.5556 1200 2.4513 0.0878 18.0789
2.6169 6.0185 1300 2.0518 0.0843 18.2525
2.5604 6.4815 1400 2.1794 0.0771 18.6442
2.3571 6.9444 1500 2.1198 0.0755 18.7310
2.1271 7.4074 1600 2.5908 0.0789 18.5391
2.1477 7.8704 1700 2.5674 0.0746 18.7879
2.0593 8.3333 1800 2.3501 0.0746 18.7838
1.9987 8.7963 1900 2.0077 0.0645 19.4190
2.0554 9.2593 2000 1.8095 0.0760 18.7045
1.9182 9.7222 2100 1.8944 0.0640 19.4540
1.8437 10.1852 2200 1.6660 0.0592 19.7857
1.8262 10.6481 2300 2.0191 0.0614 19.6274
1.9519 11.1111 2400 1.6278 0.0652 19.3679
2.0337 11.5741 2500 2.1123 0.0613 19.6387
2.1823 12.0370 2600 1.9423 0.0627 19.5405
2.3532 12.5 2700 2.3935 0.0650 19.3812
2.4301 12.9630 2800 2.3374 0.0712 18.9884
2.5901 13.4259 2900 2.5515 0.0650 19.3858
2.5238 13.8889 3000 2.8894 0.0626 19.5469
2.4095 14.3519 3100 2.5859 0.0620 19.5864
2.3416 14.8148 3200 2.6629 0.0639 19.4582
2.2236 15.2778 3300 2.2810 0.0628 19.5334
2.2794 15.7407 3400 2.3351 0.0629 19.5267
2.283 16.2037 3500 1.9756 0.0684 19.1625
2.3028 16.6667 3600 2.0231 0.0646 19.4107
2.2763 17.1296 3700 2.3012 0.0654 19.3539
2.0332 17.5926 3800 2.1426 0.0594 19.7712
1.9263 18.0556 3900 1.9053 0.0630 19.5177
2.0636 18.5185 4000 1.8481 0.0640 19.4531
1.8676 18.9815 4100 1.8974 0.0590 19.8004
1.8779 19.4444 4200 2.2541 0.0549 20.1192
1.8702 19.9074 4300 2.1981 0.0582 19.8627
1.8691 20.3704 4400 1.9649 0.0604 19.7007
1.8502 20.8333 4500 1.7129 0.0608 19.6731
1.5922 21.2963 4600 1.5730 0.0581 19.8731
1.549 21.7593 4700 1.4156 0.0633 19.5011
1.5809 22.2222 4800 1.4624 0.0622 19.5714
1.4754 22.6852 4900 1.3217 0.0596 19.7625
1.4417 23.1481 5000 1.6220 0.0587 19.8262
1.5053 23.6111 5100 1.6476 0.0550 20.1102
1.4686 24.0741 5200 1.4523 0.0595 19.7643
1.3764 24.5370 5300 1.7580 0.0544 20.1585
1.3844 25.0 5400 1.3904 0.0571 19.9453
1.3781 25.4630 5500 1.2143 0.0604 19.7001
1.2932 25.9259 5600 1.4524 0.0595 19.7648
1.3513 26.3889 5700 1.3591 0.0567 19.9765
1.2994 26.8519 5800 1.1925 0.0554 20.0771
1.2484 27.3148 5900 1.2242 0.0588 19.8152
1.197 27.7778 6000 1.2605 0.0528 20.2832
1.2305 28.2407 6100 1.1420 0.0559 20.0395
1.1684 28.7037 6200 1.1063 0.0597 19.7491
1.1854 29.1667 6300 1.2520 0.0560 20.0334
1.2 29.6296 6400 1.1011 0.0540 20.1849
1.176 30.0926 6500 1.0271 0.0567 19.9757
1.1101 30.5556 6600 1.1846 0.0563 20.0074
1.1426 31.0185 6700 1.0993 0.0570 19.9544
1.103 31.4815 6800 1.0927 0.0570 19.9572
1.0376 31.9444 6900 1.1137 0.0566 19.9874
1.0489 32.4074 7000 1.1397 0.0569 19.9585
1.0923 32.8704 7100 0.9335 0.0554 20.0784
1.0614 33.3333 7200 1.2029 0.0551 20.0982
1.0185 33.7963 7300 1.1350 0.0565 19.9945
1.0401 34.2593 7400 1.1784 0.0565 19.9957
1.0318 34.7222 7500 0.9407 0.0570 19.9527
1.0171 35.1852 7600 0.9488 0.0569 19.9638
1.0752 35.6481 7700 0.9837 0.0559 20.0420
1.0916 36.1111 7800 1.1981 0.0561 20.0249
1.1146 36.5741 7900 1.0566 0.0559 20.0370
1.1023 37.0370 8000 1.0555 0.0553 20.0849
1.1288 37.5 8100 1.1477 0.0559 20.0360
1.1209 37.9630 8200 1.1293 0.0576 19.9067
1.1332 38.4259 8300 1.2328 0.0542 20.1711
1.1516 38.8889 8400 1.1648 0.0547 20.1290
1.1606 39.3519 8500 1.1606 0.0564 19.9969
1.1642 39.8148 8600 1.2534 0.0553 20.0878
1.1356 40.2778 8700 1.1032 0.0564 19.9972
1.1859 40.7407 8800 1.2939 0.0554 20.0791
1.13 41.2037 8900 1.1200 0.0556 20.0620
1.1486 41.6667 9000 1.1004 0.0557 20.0569
1.0811 42.1296 9100 1.0300 0.0528 20.2869
1.0941 42.5926 9200 1.1182 0.0530 20.2731
1.0818 43.0556 9300 1.1210 0.0541 20.1829
1.0588 43.5185 9400 1.2151 0.0524 20.3231
1.0702 43.9815 9500 1.1523 0.0539 20.1985
1.0094 44.4444 9600 1.0473 0.0530 20.2713
1.0314 44.9074 9700 0.9793 0.0531 20.2655
1.0258 45.3704 9800 1.0890 0.0513 20.4112
1.0282 45.8333 9900 0.9711 0.0520 20.3517
0.9859 46.2963 10000 0.9357 0.0509 20.4481
0.9495 46.7593 10100 1.0113 0.0524 20.3229
0.9553 47.2222 10200 0.9366 0.0517 20.3766
0.9829 47.6852 10300 1.0695 0.0508 20.4506
0.9316 48.1481 10400 0.8101 0.0534 20.2348
0.9645 48.6111 10500 0.9829 0.0503 20.5004
0.9223 49.0741 10600 0.9013 0.0517 20.3745
0.9247 49.5370 10700 0.8640 0.0520 20.3559
0.9265 50.0 10800 0.9571 0.0508 20.4535
0.9149 50.4630 10900 0.8377 0.0493 20.5842
0.9087 50.9259 11000 0.8752 0.0486 20.6452
0.8582 51.3889 11100 0.8580 0.0520 20.3545
0.8732 51.8519 11200 0.9128 0.0487 20.6348
0.8973 52.3148 11300 0.8384 0.0505 20.4767
0.853 52.7778 11400 0.8849 0.0490 20.6070
0.8422 53.2407 11500 0.8829 0.0478 20.7160
0.8353 53.7037 11600 0.9033 0.0500 20.5217
0.8281 54.1667 11700 0.8503 0.0491 20.6028
0.8437 54.6296 11800 0.8617 0.0514 20.4034
0.8529 55.0926 11900 0.8649 0.0500 20.5240
0.832 55.5556 12000 0.8541 0.0492 20.5944
0.8331 56.0185 12100 0.7907 0.0485 20.6559
0.8279 56.4815 12200 0.8512 0.0478 20.7184
0.7912 56.9444 12300 0.8266 0.0481 20.6883
0.8058 57.4074 12400 0.8213 0.0490 20.6134
0.7863 57.8704 12500 0.8091 0.0474 20.7526
0.7759 58.3333 12600 0.7051 0.0460 20.8827
0.766 58.7963 12700 0.8549 0.0476 20.7343
0.773 59.2593 12800 0.7508 0.0468 20.8111
0.7683 59.7222 12900 0.7186 0.0477 20.7255
0.7684 60.1852 13000 0.7521 0.0455 20.9347
0.7289 60.6481 13100 0.7606 0.0467 20.8210
0.727 61.1111 13200 0.7770 0.0485 20.6545
0.74 61.5741 13300 0.6966 0.0482 20.6852
0.741 62.0370 13400 0.7099 0.0473 20.7609
0.7321 62.5 13500 0.7439 0.0470 20.7887
0.7313 62.9630 13600 0.7171 0.0468 20.8111
0.7257 63.4259 13700 0.7233 0.0468 20.8060
0.7016 63.8889 13800 0.7619 0.0463 20.8533
0.6963 64.3519 13900 0.7370 0.0466 20.8295
0.7218 64.8148 14000 0.7081 0.0468 20.8096
0.691 65.2778 14100 0.6877 0.0463 20.8571
0.6883 65.7407 14200 0.6769 0.0457 20.9102
0.6903 66.2037 14300 0.7429 0.0481 20.6951
0.6916 66.6667 14400 0.7208 0.0456 20.9191
0.6867 67.1296 14500 0.7134 0.0462 20.8635
0.6508 67.5926 14600 0.6630 0.0464 20.8489
0.6528 68.0556 14700 0.6637 0.0461 20.8780
0.6573 68.5185 14800 0.6419 0.0456 20.9207
0.659 68.9815 14900 0.6901 0.0460 20.8864
0.6279 69.4444 15000 0.6815 0.0452 20.9613
0.6409 69.9074 15100 0.6239 0.0462 20.8657
0.6391 70.3704 15200 0.6703 0.0451 20.9689
0.6559 70.8333 15300 0.6870 0.0450 20.9783
0.6417 71.2963 15400 0.6202 0.0459 20.8984
0.6364 71.7593 15500 0.6792 0.0460 20.8857
0.6197 72.2222 15600 0.6580 0.0463 20.8582
0.6415 72.6852 15700 0.6028 0.0456 20.9194
0.6312 73.1481 15800 0.6408 0.0451 20.9713
0.628 73.6111 15900 0.6099 0.0443 21.0436
0.6471 74.0741 16000 0.6503 0.0458 20.9028
0.6005 74.5370 16100 0.6151 0.0455 20.9343
0.5998 75.0 16200 0.6550 0.0453 20.9517
0.6076 75.4630 16300 0.6203 0.0456 20.9209
0.612 75.9259 16400 0.6461 0.0448 21.0018
0.6088 76.3889 16500 0.6396 0.0454 20.9440
0.607 76.8519 16600 0.6423 0.0451 20.9665
0.613 77.3148 16700 0.5506 0.0452 20.9617
0.6216 77.7778 16800 0.6033 0.0451 20.9713
0.5965 78.2407 16900 0.5685 0.0452 20.9654
0.6057 78.7037 17000 0.5780 0.0442 21.0586
0.6066 79.1667 17100 0.5873 0.0448 21.0039
0.592 79.6296 17200 0.6609 0.0450 20.9780
0.5918 80.0926 17300 0.6346 0.0459 20.8910
0.5894 80.5556 17400 0.5987 0.0455 20.9292
0.5788 81.0185 17500 0.6360 0.0447 21.0109
0.5813 81.4815 17600 0.6108 0.0446 21.0205
0.5965 81.9444 17700 0.5828 0.0447 21.0046
0.5848 82.4074 17800 0.5959 0.0451 20.9723
0.5851 82.8704 17900 0.5986 0.0443 21.0487
0.5964 83.3333 18000 0.5758 0.0454 20.9396
0.5723 83.7963 18100 0.6167 0.0446 21.0190
0.5766 84.2593 18200 0.6056 0.0446 21.0172
0.5828 84.7222 18300 0.5831 0.0442 21.0598
0.5747 85.1852 18400 0.5841 0.0444 21.0353
0.59 85.6481 18500 0.5907 0.0445 21.0262
0.5689 86.1111 18600 0.5549 0.0446 21.0194
0.6038 86.5741 18700 0.5776 0.0448 21.0044
0.5745 87.0370 18800 0.5802 0.0451 20.9694
0.573 87.5 18900 0.5801 0.0442 21.0588
0.5665 87.9630 19000 0.6071 0.0454 20.9416
0.5489 88.4259 19100 0.5778 0.0444 21.0404
0.56 88.8889 19200 0.6000 0.0443 21.0468
0.5578 89.3519 19300 0.5696 0.0444 21.0400
0.5536 89.8148 19400 0.5665 0.0440 21.0786
0.5721 90.2778 19500 0.5666 0.0443 21.0495
0.5719 90.7407 19600 0.5694 0.0443 21.0505
0.5767 91.2037 19700 0.5783 0.0446 21.0216
0.5699 91.6667 19800 0.5687 0.0442 21.0589
0.5622 92.1296 19900 0.5692 0.0444 21.0370
0.5718 92.5926 20000 0.5753 0.0439 21.0847
0.5638 93.0556 20100 0.5667 0.0439 21.0862
0.5802 93.5185 20200 0.5550 0.0444 21.0415
0.5687 93.9815 20300 0.5851 0.0443 21.0440
0.5718 94.4444 20400 0.5577 0.0442 21.0627
0.5548 94.9074 20500 0.5573 0.0443 21.0511
0.5569 95.3704 20600 0.5648 0.0438 21.0929
0.5607 95.8333 20700 0.5621 0.0442 21.0554
0.5666 96.2963 20800 0.5657 0.0444 21.0404
0.5727 96.7593 20900 0.5649 0.0441 21.0633
0.5796 97.2222 21000 0.5673 0.0442 21.0560
0.554 97.6852 21100 0.5575 0.0440 21.0743
0.5653 98.1481 21200 0.5647 0.0443 21.0512
0.5707 98.6111 21300 0.5649 0.0443 21.0504
0.5518 99.0741 21400 0.5614 0.0442 21.0580
0.5662 99.5370 21500 0.5577 0.0441 21.0645
0.5648 100.0 21600 0.5621 0.0441 21.0636

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.2.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1