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# xls-r-uzbek-cv10

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_10_0 - UZ dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2491
- Wer: 0.2588
- Cer: 0.0513

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:------:|:---------------:|:------:|
| 3.1215        | 0.68  | 500   | 1.0    | 3.1188          | 1.0    |
| 2.8562        | 1.36  | 1000  | 0.9689 | 2.5724          | 1.0002 |
| 1.2709        | 2.04  | 1500  | 0.1471 | 0.6278          | 0.6478 |
| 1.0817        | 2.72  | 2000  | 0.1304 | 0.4989          | 0.5931 |
| 0.9801        | 3.4   | 2500  | 0.1225 | 0.4582          | 0.5667 |
| 0.951         | 4.08  | 3000  | 0.1149 | 0.4239          | 0.5381 |
| 0.8834        | 4.76  | 3500  | 0.1092 | 0.4016          | 0.5158 |
| 0.857         | 5.44  | 4000  | 0.1047 | 0.3785          | 0.4992 |
| 0.8307        | 6.12  | 4500  | 0.1004 | 0.3720          | 0.4811 |
| 0.805         | 6.8   | 5000  | 0.0937 | 0.3450          | 0.4537 |
| 0.7828        | 7.48  | 5500  | 0.0912 | 0.3421          | 0.4460 |
| 0.7789        | 8.16  | 6000  | 0.0890 | 0.3295          | 0.4337 |
| 0.755         | 8.84  | 6500  | 0.0862 | 0.3257          | 0.4222 |
| 0.7464        | 9.52  | 7000  | 0.0847 | 0.3269          | 0.4155 |
| 0.7293        | 10.2  | 7500  | 0.0823 | 0.3121          | 0.4025 |
| 0.7283        | 10.88 | 8000  | 0.0789 | 0.2991          | 0.3941 |
| 0.7145        | 11.56 | 8500  | 0.0786 | 0.2961          | 0.3868 |
| 0.6963        | 12.24 | 9000  | 0.0767 | 0.2972          | 0.3784 |
| 0.6981        | 12.92 | 9500  | 0.0757 | 0.2880          | 0.3750 |
| 0.6888        | 13.6  | 10000 | 0.0745 | 0.2865          | 0.3703 |
| 0.6733        | 14.29 | 10500 | 0.0744 | 0.2887          | 0.3663 |
| 0.6701        | 14.97 | 11000 | 0.0735 | 0.2857          | 0.3624 |
| 0.6634        | 15.65 | 11500 | 0.0723 | 0.2822          | 0.3581 |
| 0.6484        | 16.33 | 12000 | 0.0706 | 0.2778          | 0.3503 |
| 0.6626        | 17.01 | 12500 | 0.0697 | 0.2697          | 0.3477 |
| 0.6341        | 17.69 | 13000 | 0.0708 | 0.2804          | 0.3511 |
| 0.6402        | 18.37 | 13500 | 0.0681 | 0.2665          | 0.3418 |
| 0.6343        | 19.05 | 14000 | 0.0687 | 0.2748          | 0.3425 |
| 0.6383        | 19.73 | 14500 | 0.0677 | 0.2696          | 0.3383 |
| 0.6178        | 20.41 | 15000 | 0.0690 | 0.2743          | 0.3417 |
| 0.6097        | 21.09 | 15500 | 0.0671 | 0.2663          | 0.3352 |
| 0.6245        | 21.77 | 16000 | 0.0665 | 0.2685          | 0.3318 |
| 0.6137        | 22.45 | 16500 | 0.0655 | 0.2700          | 0.3262 |
| 0.6018        | 23.13 | 17000 | 0.0652 | 0.2657          | 0.3225 |
| 0.6063        | 23.81 | 17500 | 0.0663 | 0.2664          | 0.3276 |
| 0.5917        | 24.49 | 18000 | 0.0658 | 0.2725          | 0.3264 |
| 0.5984        | 25.17 | 18500 | 0.0643 | 0.2593          | 0.3197 |
| 0.5949        | 25.85 | 19000 | 0.0635 | 0.2581          | 0.3161 |
| 0.5863        | 26.53 | 19500 | 0.0639 | 0.2543          | 0.3196 |
| 0.5858        | 27.21 | 20000 | 0.0628 | 0.2620          | 0.3136 |
| 0.5902        | 27.89 | 20500 | 0.0627 | 0.2549          | 0.3157 |
| 0.5794        | 28.57 | 21000 | 0.0624 | 0.2543          | 0.3136 |
| 0.5744        | 29.25 | 21500 | 0.0620 | 0.2542          | 0.3091 |
| 0.5899        | 29.93 | 22000 | 0.0624 | 0.2540          | 0.3122 |
| 0.5597        | 30.61 | 22500 | 0.0609 | 0.2500          | 0.3057 |
| 0.5595        | 31.29 | 23000 | 0.0616 | 0.2539          | 0.3087 |
| 0.5664        | 31.97 | 23500 | 0.0610 | 0.2504          | 0.3070 |
| 0.5608        | 32.65 | 24000 | 0.0611 | 0.2535          | 0.3066 |
| 0.5557        | 33.33 | 24500 | 0.0608 | 0.2538          | 0.3047 |
| 0.5741        | 34.01 | 25000 | 0.0596 | 0.2480          | 0.3009 |
| 0.5614        | 34.69 | 25500 | 0.0601 | 0.2516          | 0.3033 |
| 0.5436        | 35.37 | 26000 | 0.0601 | 0.2540          | 0.3004 |
| 0.555         | 36.05 | 26500 | 0.0595 | 0.2486          | 0.2993 |
| 0.5474        | 36.73 | 27000 | 0.0598 | 0.2536          | 0.3003 |
| 0.5352        | 37.41 | 27500 | 0.0597 | 0.2589          | 0.2986 |
| 0.5489        | 38.1  | 28000 | 0.0586 | 0.2485          | 0.2925 |
| 0.5438        | 38.77 | 28500 | 0.0581 | 0.2500          | 0.2908 |
| 0.541         | 39.46 | 29000 | 0.0577 | 0.2451          | 0.2879 |
| 0.5462        | 40.14 | 29500 | 0.0581 | 0.2510          | 0.2935 |
| 0.529         | 40.82 | 30000 | 0.0575 | 0.2435          | 0.2879 |
| 0.5169        | 41.5  | 30500 | 0.0572 | 0.2474          | 0.2860 |
| 0.5281        | 42.18 | 31000 | 0.0575 | 0.2478          | 0.2884 |
| 0.527         | 42.86 | 31500 | 0.0568 | 0.2492          | 0.2845 |
| 0.5172        | 43.54 | 32000 | 0.0575 | 0.2451          | 0.2885 |
| 0.5154        | 44.22 | 32500 | 0.0574 | 0.2490          | 0.2873 |
| 0.5129        | 44.9  | 33000 | 0.0569 | 0.2446          | 0.2853 |
| 0.5075        | 45.58 | 33500 | 0.0565 | 0.2485          | 0.2828 |
| 0.5077        | 46.26 | 34000 | 0.0559 | 0.2452          | 0.2807 |
| 0.5004        | 46.94 | 34500 | 0.0572 | 0.2501          | 0.2882 |
| 0.5319        | 47.62 | 35000 | 0.0575 | 0.2516          | 0.2856 |
| 0.4956        | 48.3  | 35500 | 0.0567 | 0.2495          | 0.2821 |
| 0.5053        | 48.98 | 36000 | 0.0565 | 0.2482          | 0.2825 |
| 0.5014        | 49.66 | 36500 | 0.0559 | 0.2441          | 0.2808 |
| 0.4945        | 50.34 | 37000 | 0.0562 | 0.2460          | 0.2807 |
| 0.51          | 51.02 | 37500 | 0.0547 | 0.2434          | 0.2741 |
| 0.5095        | 51.7  | 38000 | 0.0558 | 0.2434          | 0.2790 |
| 0.5026        | 52.38 | 38500 | 0.0560 | 0.2478          | 0.2787 |
| 0.5081        | 53.06 | 39000 | 0.0566 | 0.2485          | 0.2821 |
| 0.5021        | 53.74 | 39500 | 0.0551 | 0.2410          | 0.2752 |
| 0.4945        | 54.42 | 40000 | 0.0552 | 0.2436          | 0.2766 |
| 0.4882        | 55.1  | 40500 | 0.0555 | 0.2438          | 0.2769 |
| 0.497         | 55.78 | 41000 | 0.0550 | 0.2423          | 0.2758 |
| 0.4925        | 56.46 | 41500 | 0.0560 | 0.2474          | 0.2790 |
| 0.4894        | 57.14 | 42000 | 0.0559 | 0.2497          | 0.2797 |
| 0.4767        | 57.82 | 42500 | 0.0556 | 0.2528          | 0.2800 |
| 0.4796        | 58.5  | 43000 | 0.0549 | 0.2463          | 0.2755 |
| 0.4767        | 59.18 | 43500 | 0.0548 | 0.2452          | 0.2753 |
| 0.4786        | 59.86 | 44000 | 0.0551 | 0.2480          | 0.2769 |
| 0.4804        | 60.54 | 44500 | 0.0556 | 0.2514          | 0.2789 |
| 0.4794        | 61.22 | 45000 | 0.0539 | 0.2391          | 0.2715 |
| 0.4789        | 61.9  | 45500 | 0.0546 | 0.2461          | 0.2725 |
| 0.4683        | 62.58 | 46000 | 0.0541 | 0.2444          | 0.2707 |
| 0.4721        | 63.27 | 46500 | 0.0539 | 0.2468          | 0.2693 |
| 0.4792        | 63.94 | 47000 | 0.0546 | 0.2479          | 0.2738 |
| 0.4712        | 64.63 | 47500 | 0.0547 | 0.2466          | 0.2742 |
| 0.4607        | 65.31 | 48000 | 0.0539 | 0.2503          | 0.2707 |
| 0.4712        | 65.99 | 48500 | 0.0543 | 0.2458          | 0.2718 |
| 0.4647        | 66.67 | 49000 | 0.0538 | 0.2474          | 0.2693 |
| 0.4736        | 67.35 | 49500 | 0.0541 | 0.2514          | 0.2696 |
| 0.4718        | 68.03 | 50000 | 0.0540 | 0.2506          | 0.2692 |
| 0.4695        | 68.71 | 50500 | 0.0538 | 0.2499          | 0.2675 |
| 0.4549        | 69.39 | 51000 | 0.0534 | 0.2491          | 0.2669 |
| 0.4605        | 70.07 | 51500 | 0.0532 | 0.2497          | 0.2660 |
| 0.4538        | 70.75 | 52000 | 0.0536 | 0.2472          | 0.2684 |
| 0.4571        | 71.43 | 52500 | 0.0523 | 0.2441          | 0.2629 |
| 0.4608        | 72.11 | 53000 | 0.0529 | 0.2469          | 0.2652 |
| 0.4541        | 72.79 | 53500 | 0.0533 | 0.2498          | 0.2673 |
| 0.4424        | 73.47 | 54000 | 0.0530 | 0.2504          | 0.2658 |
| 0.4482        | 74.15 | 54500 | 0.0534 | 0.2517          | 0.2684 |
| 0.4554        | 74.83 | 55000 | 0.0529 | 0.2471          | 0.2656 |
| 0.444         | 75.51 | 55500 | 0.0535 | 0.2493          | 0.2675 |
| 0.4464        | 76.19 | 56000 | 0.0524 | 0.2461          | 0.2635 |
| 0.4436        | 76.87 | 56500 | 0.0526 | 0.2479          | 0.2641 |
| 0.4432        | 77.55 | 57000 | 0.0526 | 0.2513          | 0.2641 |
| 0.4459        | 78.23 | 57500 | 0.0521 | 0.2460          | 0.2625 |
| 0.4433        | 78.91 | 58000 | 0.0521 | 0.2457          | 0.2622 |
| 0.4407        | 79.59 | 58500 | 0.0528 | 0.2531          | 0.2659 |
| 0.4389        | 80.27 | 59000 | 0.0521 | 0.2485          | 0.2631 |
| 0.4384        | 80.95 | 59500 | 0.0522 | 0.2502          | 0.2653 |
| 0.4306        | 81.63 | 60000 | 0.0528 | 0.2480          | 0.2665 |
| 0.4505        | 82.31 | 60500 | 0.0523 | 0.2461          | 0.2637 |
| 0.4442        | 82.99 | 61000 | 0.0523 | 0.2519          | 0.2641 |
| 0.4349        | 83.67 | 61500 | 0.0522 | 0.2509          | 0.2625 |
| 0.4398        | 84.35 | 62000 | 0.0523 | 0.2510          | 0.2659 |
| 0.4398        | 85.03 | 62500 | 0.0526 | 0.2507          | 0.2648 |
| 0.4355        | 85.71 | 63000 | 0.0523 | 0.2500          | 0.2653 |
| 0.4373        | 86.39 | 63500 | 0.0524 | 0.2523          | 0.2650 |
| 0.4391        | 87.07 | 64000 | 0.0523 | 0.2509          | 0.2635 |
| 0.4381        | 87.75 | 64500 | 0.0521 | 0.2502          | 0.2635 |
| 0.4297        | 88.43 | 65000 | 0.0521 | 0.2521          | 0.2632 |
| 0.44          | 89.12 | 65500 | 0.0520 | 0.2507          | 0.2624 |
| 0.4313        | 89.8  | 66000 | 0.0519 | 0.2497          | 0.2623 |
| 0.4402        | 90.48 | 66500 | 0.0517 | 0.2488          | 0.2608 |
| 0.4324        | 91.16 | 67000 | 0.0512 | 0.2485          | 0.2585 |
| 0.4317        | 91.84 | 67500 | 0.0513 | 0.2488          | 0.2587 |
| 0.437         | 92.52 | 68000 | 0.0513 | 0.2473          | 0.2590 |
| 0.4389        | 93.2  | 68500 | 0.0512 | 0.2472          | 0.2581 |
| 0.4428        | 93.88 | 69000 | 0.0512 | 0.2475          | 0.2587 |
| 0.4294        | 94.56 | 69500 | 0.0513 | 0.2489          | 0.2596 |
| 0.4247        | 95.24 | 70000 | 0.0515 | 0.2499          | 0.2597 |
| 0.4309        | 95.92 | 70500 | 0.0514 | 0.2493          | 0.2590 |
| 0.4366        | 96.6  | 71000 | 0.0512 | 0.2492          | 0.2592 |
| 0.4245        | 97.28 | 71500 | 0.0513 | 0.2493          | 0.2587 |
| 0.4346        | 97.96 | 72000 | 0.0512 | 0.2478          | 0.2583 |
| 0.4289        | 98.64 | 72500 | 0.0512 | 0.2489          | 0.2585 |
| 0.4246        | 99.32 | 73000 | 0.0513 | 0.2487          | 0.2589 |
| 0.4241        | 100.0 | 73500 | 0.0513 | 0.2491          | 0.2588 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.10.3

### Credits
Author: Shukrullo Turgunov (aka Vodiylik)

{"language": ["uz"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_10_0", "generated_from_trainer"], "datasets": ["common_voice_10_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "xls-r-uzbek-cv10", "results": []}]}
transformers pytorch tensorboard safetensors wav2vec2 pretraining automatic-speech-recognition mozilla-foundation/common_voice_10_0 generated_from_trainer uz dataset:common_voice_10_0 base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #pretraining #automatic-speech-recognition #mozilla-foundation/common_voice_10_0 #generated_from_trainer #uz #dataset-common_voice_10_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us 

xls-r-uzbek-cv10
================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_10\_0 - UZ dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2491
* Wer: 0.2588
* Cer: 0.0513


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: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.12.0
* Datasets 2.4.0
* Tokenizers 0.10.3


### Credits


Author: Shukrullo Turgunov (aka Vodiylik)



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.12.0
* Datasets 2.4.0
* Tokenizers 0.10.3 ### Credits


Author: Shukrullo Turgunov (aka Vodiylik)
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #pretraining #automatic-speech-recognition #mozilla-foundation/common_voice_10_0 #generated_from_trainer #uz #dataset-common_voice_10_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.12.0
* Datasets 2.4.0
* Tokenizers 0.10.3 ### Credits


Author: Shukrullo Turgunov (aka Vodiylik)
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #pretraining #automatic-speech-recognition #mozilla-foundation/common_voice_10_0 #generated_from_trainer #uz #dataset-common_voice_10_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.12.0
* Datasets 2.4.0
* Tokenizers 0.10.3### Credits


Author: Shukrullo Turgunov (aka Vodiylik)