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---
language:
- he
tags:
- automatic-speech-recognition
- generated_from_trainer
- he
- hf-asr-leaderboard
- robust-speech-event
base_model: facebook/wav2vec2-xls-r-300m
model-index:
- name: wav2vec2-xls-r-300m-hebrew
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Custom Dataset
type: custom
args: he
metrics:
- type: wer
value: 23.18
name: Test WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-hebrew
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/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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