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wav2vec2-xls-r-300m-hebrew

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the private datasets in 2 stages - firstly was fine-tuned on a small dataset with good samples Then the obtained model was fine-tuned on a large dataset with the small good dataset, with various samples from different sources, and with an unlabeled dataset that was weakly labeled using a previously trained model.

Small dataset:

split size(gb) n_samples duration(hrs)
train 4.19 20306 28
dev 1.05 5076 7

Large dataset:

split size(gb) n_samples duration(hrs)
train 12.3 90777 69
dev 2.39 20246 14*
(*weakly labeled data wasn't used in validation set)

After firts training it achieves:

on small dataset

  • Loss: 0.5438
  • WER: 0.1773

on large dataset

  • WER: 0.3811

after second training: on small dataset

  • WER: 0.1697

on large dataset

  • Loss: 0.4502
  • WER: 0.2318

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

First training

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 3.15 1000 0.5203 0.4333
1.4284 6.31 2000 0.4816 0.3951
1.4284 9.46 3000 0.4315 0.3546
1.283 12.62 4000 0.4278 0.3404
1.283 15.77 5000 0.4090 0.3054
1.1777 18.93 6000 0.3893 0.3006
1.1777 22.08 7000 0.3968 0.2857
1.0994 25.24 8000 0.3892 0.2751
1.0994 28.39 9000 0.4061 0.2690
1.0323 31.54 10000 0.4114 0.2507
1.0323 34.7 11000 0.4021 0.2508
0.9623 37.85 12000 0.4032 0.2378
0.9623 41.01 13000 0.4148 0.2374
0.9077 44.16 14000 0.4350 0.2323
0.9077 47.32 15000 0.4515 0.2246
0.8573 50.47 16000 0.4474 0.2180
0.8573 53.63 17000 0.4649 0.2171
0.8083 56.78 18000 0.4455 0.2102
0.8083 59.94 19000 0.4587 0.2092
0.769 63.09 20000 0.4794 0.2012
0.769 66.25 21000 0.4845 0.2007
0.7308 69.4 22000 0.4937 0.2008
0.7308 72.55 23000 0.4920 0.1895
0.6927 75.71 24000 0.5179 0.1911
0.6927 78.86 25000 0.5202 0.1877
0.6622 82.02 26000 0.5266 0.1840
0.6622 85.17 27000 0.5351 0.1854
0.6315 88.33 28000 0.5373 0.1811
0.6315 91.48 29000 0.5331 0.1792
0.6075 94.64 30000 0.5390 0.1779
0.6075 97.79 31000 0.5459 0.1773

Second training

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 60.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.7 1000 0.5371 0.3811
1.3606 1.41 2000 0.5247 0.3902
1.3606 2.12 3000 0.5126 0.3859
1.3671 2.82 4000 0.5062 0.3828
1.3671 3.53 5000 0.4979 0.3672
1.3421 4.23 6000 0.4906 0.3816
1.3421 4.94 7000 0.4784 0.3651
1.328 5.64 8000 0.4810 0.3669
1.328 6.35 9000 0.4747 0.3597
1.3109 7.05 10000 0.4813 0.3808
1.3109 7.76 11000 0.4631 0.3561
1.2873 8.46 12000 0.4603 0.3431
1.2873 9.17 13000 0.4579 0.3533
1.2661 9.87 14000 0.4471 0.3365
1.2661 10.58 15000 0.4584 0.3437
1.249 11.28 16000 0.4461 0.3454
1.249 11.99 17000 0.4482 0.3367
1.2322 12.69 18000 0.4464 0.3335
1.2322 13.4 19000 0.4427 0.3454
1.22 14.1 20000 0.4440 0.3395
1.22 14.81 21000 0.4459 0.3378
1.2044 15.51 22000 0.4406 0.3199
1.2044 16.22 23000 0.4398 0.3155
1.1913 16.92 24000 0.4237 0.3150
1.1913 17.63 25000 0.4287 0.3279
1.1705 18.34 26000 0.4253 0.3103
1.1705 19.04 27000 0.4234 0.3098
1.1564 19.75 28000 0.4174 0.3076
1.1564 20.45 29000 0.4260 0.3160
1.1461 21.16 30000 0.4235 0.3036
1.1461 21.86 31000 0.4309 0.3055
1.1285 22.57 32000 0.4264 0.3006
1.1285 23.27 33000 0.4201 0.2880
1.1135 23.98 34000 0.4131 0.2975
1.1135 24.68 35000 0.4202 0.2849
1.0968 25.39 36000 0.4105 0.2888
1.0968 26.09 37000 0.4210 0.2834
1.087 26.8 38000 0.4123 0.2843
1.087 27.5 39000 0.4216 0.2803
1.0707 28.21 40000 0.4161 0.2787
1.0707 28.91 41000 0.4186 0.2740
1.0575 29.62 42000 0.4118 0.2845
1.0575 30.32 43000 0.4243 0.2773
1.0474 31.03 44000 0.4221 0.2707
1.0474 31.73 45000 0.4138 0.2700
1.0333 32.44 46000 0.4102 0.2638
1.0333 33.15 47000 0.4162 0.2650
1.0191 33.85 48000 0.4155 0.2636
1.0191 34.56 49000 0.4129 0.2656
1.0087 35.26 50000 0.4157 0.2632
1.0087 35.97 51000 0.4090 0.2654
0.9901 36.67 52000 0.4183 0.2587
0.9901 37.38 53000 0.4251 0.2648
0.9795 38.08 54000 0.4229 0.2555
0.9795 38.79 55000 0.4176 0.2546
0.9644 39.49 56000 0.4223 0.2513
0.9644 40.2 57000 0.4244 0.2530
0.9534 40.9 58000 0.4175 0.2538
0.9534 41.61 59000 0.4213 0.2505
0.9397 42.31 60000 0.4275 0.2565
0.9397 43.02 61000 0.4315 0.2528
0.9269 43.72 62000 0.4316 0.2501
0.9269 44.43 63000 0.4247 0.2471
0.9175 45.13 64000 0.4376 0.2469
0.9175 45.84 65000 0.4335 0.2450
0.9026 46.54 66000 0.4336 0.2452
0.9026 47.25 67000 0.4400 0.2427
0.8929 47.95 68000 0.4382 0.2429
0.8929 48.66 69000 0.4361 0.2415
0.8786 49.37 70000 0.4413 0.2398
0.8786 50.07 71000 0.4392 0.2415
0.8714 50.78 72000 0.4345 0.2406
0.8714 51.48 73000 0.4475 0.2402
0.8589 52.19 74000 0.4473 0.2374
0.8589 52.89 75000 0.4457 0.2357
0.8493 53.6 76000 0.4462 0.2366
0.8493 54.3 77000 0.4494 0.2356
0.8395 55.01 78000 0.4472 0.2352
0.8395 55.71 79000 0.4490 0.2339
0.8295 56.42 80000 0.4489 0.2318
0.8295 57.12 81000 0.4469 0.2320
0.8225 57.83 82000 0.4478 0.2321
0.8225 58.53 83000 0.4525 0.2326
0.816 59.24 84000 0.4532 0.2316
0.816 59.94 85000 0.4502 0.2318

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0
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Evaluation results