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wav2vec2-xls-r-300m-hebrew

Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Hebrew
wav2vec2
generated_from_trainer
hf-asr-leaderboard
robust-speech-event
Eval Results
Inference Endpoints
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metadata
tags:
  - generated_from_trainer
model-index:
  - name: wav2vec2-xls-r-300m-a-hebrew
    results: []

wav2vec2-xls-r-300m-a-hebrew

This model is a fine-tuned version of imvladikon/wav2vec2-xls-r-300m-a-hebrew on the None dataset. It achieves the following results on the evaluation set:

  • 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

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