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wav2vec2-large-mms-1b-DZ

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
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End of training

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  3. config.json +2 -2
  4. model.safetensors +2 -2
  5. training_args.bin +1 -1
README.md CHANGED
@@ -20,10 +20,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3318
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- - Wer: 0.5332
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- - Bleu: {'bleu': 0.20626502760570276, 'precisions': [0.4828561729093584, 0.26526984126984127, 0.15708092485549133, 0.09694133377904061], 'brevity_penalty': 0.9815017376632986, 'length_ratio': 0.981670739835592, 'translation_length': 8837, 'reference_length': 9002}
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- - Rouge: {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
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  ## Model description
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@@ -51,44 +51,53 @@ The following hyperparameters were used during training:
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 500
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- - num_epochs: 100
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Wer | Bleu | Rouge |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------:|
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- | 8.9409 | 1.0 | 121 | 7.3836 | 1.0009 | {'bleu': 0.0, 'precisions': [0.0, 0.0, 0.0, 0.0], 'brevity_penalty': 0.15361828967433966, 'length_ratio': 0.3480337702732726, 'translation_length': 3133, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 5.8951 | 2.0 | 242 | 3.9240 | 1.0 | {'bleu': 0.0, 'precisions': [0.0, 0.0, 0.0, 0.0], 'brevity_penalty': 0.00023460944616129434, 'length_ratio': 0.10686514107976006, 'translation_length': 962, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 3.448 | 3.0 | 363 | 3.3244 | 1.0072 | {'bleu': 0.0, 'precisions': [0.00021687269572760788, 0.0, 0.0, 0.0], 'brevity_penalty': 0.38585716882722343, 'length_ratio': 0.5122195067762719, 'translation_length': 4611, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 3.3048 | 4.0 | 484 | 3.2099 | 1.0540 | {'bleu': 0.0, 'precisions': [0.0012913223140495868, 0.0, 0.0, 0.0], 'brevity_penalty': 0.8500599971491325, 'length_ratio': 0.8602532770495446, 'translation_length': 7744, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 1.3604 | 5.0 | 605 | 0.6633 | 0.7965 | {'bleu': 0.034929556738440316, 'precisions': [0.21936736325225534, 0.05991019884541373, 0.017828437819669734, 0.007105396717983421], 'brevity_penalty': 0.9724101311329575, 'length_ratio': 0.9727838258164853, 'translation_length': 8757, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.6764 | 6.0 | 726 | 0.4717 | 0.6724 | {'bleu': 0.09414039105458619, 'precisions': [0.34541504687857305, 0.1395169578622816, 0.06105417276720351, 0.030010172939979655], 'brevity_penalty': 0.9711537088639254, 'length_ratio': 0.971561875138858, 'translation_length': 8746, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.6118 | 7.0 | 847 | 0.4297 | 0.6431 | {'bleu': 0.10327123610576155, 'precisions': [0.3748719699556162, 0.1584664536741214, 0.06853899883585565, 0.03080808080808081], 'brevity_penalty': 0.9758289500370382, 'length_ratio': 0.9761164185736503, 'translation_length': 8787, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.5499 | 8.0 | 968 | 0.4136 | 0.6383 | {'bleu': 0.09547633908306723, 'precisions': [0.37913718329148594, 0.15538461538461537, 0.06206191588785047, 0.0253592561284869], 'brevity_penalty': 0.9729807252327849, 'length_ratio': 0.9733392579426794, 'translation_length': 8762, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.5426 | 9.0 | 1089 | 0.3948 | 0.6236 | {'bleu': 0.1146211663681401, 'precisions': [0.3941814033086138, 0.1719851339228502, 0.07882061012990804, 0.03599188915174045], 'brevity_penalty': 0.973322929713784, 'length_ratio': 0.9736725172183959, 'translation_length': 8765, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.5224 | 10.0 | 1210 | 0.3845 | 0.6133 | {'bleu': 0.14615786010373802, 'precisions': [0.4039206747207659, 0.19687660010240654, 0.10451895043731778, 0.060917988525143435], 'brevity_penalty': 0.9743488596571711, 'length_ratio': 0.9746722950455454, 'translation_length': 8774, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.5106 | 11.0 | 1331 | 0.3768 | 0.6081 | {'bleu': 0.14761534429663833, 'precisions': [0.4103973434100538, 0.2001029468536868, 0.10602727672679278, 0.0616822429906542], 'brevity_penalty': 0.969666867156736, 'length_ratio': 0.9701177516107532, 'translation_length': 8733, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4808 | 12.0 | 1452 | 0.3689 | 0.6038 | {'bleu': 0.14419001572294796, 'precisions': [0.4128158433872069, 0.20002556237218813, 0.10203784570596798, 0.05660377358490566], 'brevity_penalty': 0.9757151727531809, 'length_ratio': 0.9760053321484115, 'translation_length': 8786, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4887 | 13.0 | 1573 | 0.3645 | 0.5959 | {'bleu': 0.14552105797325302, 'precisions': [0.4212262541235354, 0.20283561118916849, 0.10340314136125654, 0.0558734432850892], 'brevity_penalty': 0.9762839328773337, 'length_ratio': 0.9765607642746057, 'translation_length': 8791, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4868 | 14.0 | 1694 | 0.3618 | 0.5964 | {'bleu': 0.14398457640279802, 'precisions': [0.4209029910155806, 0.20061294853786235, 0.10206455364931666, 0.05484522207267833], 'brevity_penalty': 0.9765113485390307, 'length_ratio': 0.9767829371250834, 'translation_length': 8793, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4694 | 15.0 | 1815 | 0.3552 | 0.5896 | {'bleu': 0.1407651620422715, 'precisions': [0.42652899126290705, 0.20315883326964718, 0.1007830626450116, 0.04898506961919141], 'brevity_penalty': 0.978782729886213, 'length_ratio': 0.97900466562986, 'translation_length': 8813, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4717 | 16.0 | 1936 | 0.3515 | 0.5820 | {'bleu': 0.14704090054592955, 'precisions': [0.4347430650295589, 0.21087567015573142, 0.10566860465116279, 0.05299461641991925], 'brevity_penalty': 0.9768523773634661, 'length_ratio': 0.9771161964007998, 'translation_length': 8796, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4697 | 17.0 | 2057 | 0.3472 | 0.5820 | {'bleu': 0.16519675176223306, 'precisions': [0.4347628256171084, 0.221356495082386, 0.12045388420133837, 0.0707189762586294], 'brevity_penalty': 0.9762839328773337, 'length_ratio': 0.9765607642746057, 'translation_length': 8791, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4495 | 18.0 | 2178 | 0.3440 | 0.5790 | {'bleu': 0.16684635490186076, 'precisions': [0.4380551127305853, 0.22327365728900256, 0.12163146394756008, 0.07200674536256324], 'brevity_penalty': 0.9752599372827168, 'length_ratio': 0.9755609864474561, 'translation_length': 8782, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4415 | 19.0 | 2299 | 0.3415 | 0.5724 | {'bleu': 0.162083792001847, 'precisions': [0.4451789377706861, 0.22375832053251407, 0.11798162461717952, 0.06515867656988521], 'brevity_penalty': 0.9743488596571711, 'length_ratio': 0.9746722950455454, 'translation_length': 8774, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4418 | 20.0 | 2420 | 0.3407 | 0.5635 | {'bleu': 0.1651392945199903, 'precisions': [0.45348043676069155, 0.22924648786717752, 0.12087272727272727, 0.06511862695608278], 'brevity_penalty': 0.9763976470202772, 'length_ratio': 0.9766718506998445, 'translation_length': 8792, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4411 | 21.0 | 2541 | 0.3380 | 0.5612 | {'bleu': 0.17032448446358872, 'precisions': [0.4554837246228876, 0.2328453214513049, 0.12492753623188406, 0.06908115358819585], 'brevity_penalty': 0.9792364011971344, 'length_ratio': 0.9794490113308154, 'translation_length': 8817, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4368 | 22.0 | 2662 | 0.3403 | 0.5563 | {'bleu': 0.17429463351579735, 'precisions': [0.46019505556815604, 0.2389256619144603, 0.12896681640341978, 0.07074601844090528], 'brevity_penalty': 0.9793497875444289, 'length_ratio': 0.9795600977560542, 'translation_length': 8818, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4322 | 23.0 | 2783 | 0.3307 | 0.5598 | {'bleu': 0.18466486831726667, 'precisions': [0.4570876435148346, 0.24365028717294193, 0.1379360465116279, 0.08309503784693019], 'brevity_penalty': 0.9769660283987757, 'length_ratio': 0.9772272828260387, 'translation_length': 8797, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4263 | 24.0 | 2904 | 0.3398 | 0.5549 | {'bleu': 0.1797281293297898, 'precisions': [0.46188799272975123, 0.2423160311184798, 0.13404008132442638, 0.07613445378151261], 'brevity_penalty': 0.9776476696891355, 'length_ratio': 0.9778938013774717, 'translation_length': 8803, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4102 | 25.0 | 3025 | 0.3253 | 0.5472 | {'bleu': 0.19095303691786358, 'precisions': [0.469932931681255, 0.25488194001276326, 0.14375, 0.08476286579212916], 'brevity_penalty': 0.9769660283987757, 'length_ratio': 0.9772272828260387, 'translation_length': 8797, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4236 | 26.0 | 3146 | 0.3274 | 0.5474 | {'bleu': 0.18409612160683292, 'precisions': [0.4694085656016315, 0.2483468972533062, 0.13677811550151975, 0.07801774652603381], 'brevity_penalty': 0.9802564252131077, 'length_ratio': 0.9804487891579649, 'translation_length': 8826, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4177 | 27.0 | 3267 | 0.3255 | 0.5419 | {'bleu': 0.19152955171945296, 'precisions': [0.47450135992747056, 0.25489697278046297, 0.14405675401766324, 0.08372404554588078], 'brevity_penalty': 0.980029841295489, 'length_ratio': 0.9802266163074872, 'translation_length': 8824, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.4051 | 28.0 | 3388 | 0.3218 | 0.5380 | {'bleu': 0.20391771010111173, 'precisions': [0.47922814982973894, 0.2658002038735984, 0.1550848687073843, 0.09550184625713326], 'brevity_penalty': 0.9784423441477751, 'length_ratio': 0.9786714063541435, 'translation_length': 8810, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.3993 | 29.0 | 3509 | 0.3225 | 0.5364 | {'bleu': 0.1991760083321937, 'precisions': [0.4802765812740875, 0.26145038167938933, 0.15090514120202753, 0.09011725293132328], 'brevity_penalty': 0.9798032070519724, 'length_ratio': 0.9800044434570095, 'translation_length': 8822, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.3942 | 30.0 | 3630 | 0.3230 | 0.5335 | {'bleu': 0.20119532062721326, 'precisions': [0.48279375141498754, 0.2630843495934959, 0.15223362729507012, 0.09144098963557339], 'brevity_penalty': 0.981162257838828, 'length_ratio': 0.9813374805598756, 'translation_length': 8834, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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- | 0.3846 | 31.0 | 3751 | 0.3318 | 0.5332 | {'bleu': 0.20626502760570276, 'precisions': [0.4828561729093584, 0.26526984126984127, 0.15708092485549133, 0.09694133377904061], 'brevity_penalty': 0.9815017376632986, 'length_ratio': 0.981670739835592, 'translation_length': 8837, 'reference_length': 9002} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1983
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+ - Wer: 0.1456
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+ - Bleu: {'bleu': 0.7492044741492486, 'precisions': [0.8730063030967389, 0.7883760255527025, 0.7152661166678835, 0.6510430304617264], 'brevity_penalty': 0.9957339815437508, 'length_ratio': 0.995743055176554, 'translation_length': 18245, 'reference_length': 18323}
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+ - Rouge: {'rouge1': 0.8748364322650914, 'rouge2': 0.7928723605220098, 'rougeL': 0.8745015805780032, 'rougeLsum': 0.8743113489400196}
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  ## Model description
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51
  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 40
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  - mixed_precision_training: Native AMP
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57
  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Wer | Bleu | Rouge |
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+ |:-------------:|:-------:|:-----:|:---------------:|:------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|
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+ | 5.9588 | 1.0 | 285 | 2.7873 | 0.9827 | {'bleu': 0.0, 'precisions': [0.059154929577464786, 0.0, 0.0, 0.0], 'brevity_penalty': 0.11433624320676729, 'length_ratio': 0.3155956311912624, 'translation_length': 2485, 'reference_length': 7874} | {'rouge1': 0.03875997597517414, 'rouge2': 0.0, 'rougeL': 0.03892048827301332, 'rougeLsum': 0.038793322006122906} |
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+ | 0.4602 | 2.0 | 570 | 0.3555 | 0.2311 | {'bleu': 0.6283054199052752, 'precisions': [0.7993283417749394, 0.6805363212891854, 0.5854142185663925, 0.5068155835018908], 'brevity_penalty': 0.9912846225149247, 'length_ratio': 0.9913223817060525, 'translation_length': 18164, 'reference_length': 18323} | {'rouge1': 0.7917720369498722, 'rouge2': 0.6755927340957304, 'rougeL': 0.7912377266650397, 'rougeLsum': 0.7913035186420103} |
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+ | 0.3842 | 3.0 | 855 | 0.3058 | 0.2090 | {'bleu': 0.6605337548855493, 'precisions': [0.8175150519978106, 0.7074787393696849, 0.616208731142045, 0.5403591352859135], 'brevity_penalty': 0.99710327314591, 'length_ratio': 0.9971074605686842, 'translation_length': 18270, 'reference_length': 18323} | {'rouge1': 0.8146357732958056, 'rouge2': 0.7071194587560558, 'rougeL': 0.814052758845498, 'rougeLsum': 0.8138241815295024} |
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+ | 0.3719 | 4.0 | 1140 | 0.2868 | 0.2010 | {'bleu': 0.6712219662583173, 'precisions': [0.8250068549492734, 0.7188067932568779, 0.6293752283522105, 0.5544580419580419], 'brevity_penalty': 0.9951857415819062, 'length_ratio': 0.9951972930197021, 'translation_length': 18235, 'reference_length': 18323} | {'rouge1': 0.8222029713967768, 'rouge2': 0.7197657000623603, 'rougeL': 0.8217923739918656, 'rougeLsum': 0.821719465931791} |
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+ | 0.3336 | 5.0 | 1425 | 0.2742 | 0.1959 | {'bleu': 0.6785372107003715, 'precisions': [0.8300621254604431, 0.7259128904531457, 0.6393695014662757, 0.5666900666900667], 'brevity_penalty': 0.9926599804903764, 'length_ratio': 0.9926867870981826, 'translation_length': 18189, 'reference_length': 18323} | {'rouge1': 0.8265645999560629, 'rouge2': 0.7247965384723267, 'rougeL': 0.8259582425920233, 'rougeLsum': 0.8259017198295564} |
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+ | 0.3442 | 6.0 | 1710 | 0.2642 | 0.1903 | {'bleu': 0.6873788245362181, 'precisions': [0.832968476357268, 0.7314617981743153, 0.6461886022445708, 0.5733948950256991], 'brevity_penalty': 0.9972127357870568, 'length_ratio': 0.9972166130000546, 'translation_length': 18272, 'reference_length': 18323} | {'rouge1': 0.8325100938158259, 'rouge2': 0.7340864170622121, 'rougeL': 0.83191579042451, 'rougeLsum': 0.8318541463934617} |
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+ | 0.3341 | 7.0 | 1995 | 0.2582 | 0.1843 | {'bleu': 0.6963666431657629, 'precisions': [0.839673317254988, 0.7409495177251659, 0.6574662285505659, 0.5849270806043141], 'brevity_penalty': 0.9956791710107048, 'length_ratio': 0.9956884789608689, 'translation_length': 18244, 'reference_length': 18323} | {'rouge1': 0.838537901118194, 'rouge2': 0.7427526814996277, 'rougeL': 0.8379557459558569, 'rougeLsum': 0.8379007055417265} |
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+ | 0.3172 | 8.0 | 2280 | 0.2494 | 0.1804 | {'bleu': 0.7001144932835086, 'precisions': [0.843205001645278, 0.7451742291301078, 0.6613327487943884, 0.5895822408669813], 'brevity_penalty': 0.9951309011278089, 'length_ratio': 0.9951427168040168, 'translation_length': 18234, 'reference_length': 18323} | {'rouge1': 0.8428458068475617, 'rouge2': 0.7473487117700717, 'rougeL': 0.8422200154587571, 'rougeLsum': 0.8420994714247132} |
69
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