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cdactvm
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w2v_bert_malayalam_100125

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2-bert
Generated from Trainer
Inference Endpoints
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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- ## How to Get Started with the Model
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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  ---
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  library_name: transformers
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+ license: mit
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+ base_model: facebook/w2v-bert-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: w2v_bert_malayalam_100125
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+ results: []
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # w2v_bert_malayalam_100125
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+
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+ This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2922
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+ - Wer: 0.2847
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 8
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+ - optimizer: Use 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: 5
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:------:|:-----:|:---------------:|:------:|
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+ | 13.7406 | 0.0270 | 300 | 1.1859 | 0.8493 |
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+ | 6.3024 | 0.0539 | 600 | 0.9225 | 0.6676 |
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+ | 5.3208 | 0.0809 | 900 | 0.7547 | 0.6066 |
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+ | 5.0544 | 0.1078 | 1200 | 0.7201 | 0.6010 |
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+ | 4.552 | 0.1348 | 1500 | 0.6276 | 0.5619 |
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+ | 4.0527 | 0.1617 | 1800 | 0.6411 | 0.5327 |
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+ | 3.8443 | 0.1887 | 2100 | 0.6421 | 0.5384 |
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+ | 3.7674 | 0.2156 | 2400 | 0.6330 | 0.5094 |
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+ | 3.7184 | 0.2426 | 2700 | 0.5730 | 0.5052 |
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+ | 3.567 | 0.2696 | 3000 | 0.5459 | 0.4992 |
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+ | 3.633 | 0.2965 | 3300 | 0.5636 | 0.4906 |
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+ | 3.473 | 0.3235 | 3600 | 0.5271 | 0.4986 |
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+ | 3.3079 | 0.3504 | 3900 | 0.5346 | 0.4924 |
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+ | 3.6181 | 0.3774 | 4200 | 0.5180 | 0.4706 |
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+ | 3.3054 | 0.4043 | 4500 | 0.5266 | 0.4706 |
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+ | 3.2021 | 0.4313 | 4800 | 0.5171 | 0.4714 |
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+ | 3.237 | 0.4583 | 5100 | 0.4940 | 0.4442 |
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+ | 3.2646 | 0.4852 | 5400 | 0.5097 | 0.4691 |
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+ | 3.2935 | 0.5122 | 5700 | 0.5393 | 0.4513 |
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+ | 3.2023 | 0.5391 | 6000 | 0.4942 | 0.4491 |
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+ | 3.1896 | 0.5661 | 6300 | 0.4971 | 0.4706 |
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+ | 3.169 | 0.5930 | 6600 | 0.4829 | 0.4387 |
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+ | 3.1288 | 0.6200 | 6900 | 0.4699 | 0.4299 |
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+ | 3.0414 | 0.6469 | 7200 | 0.4697 | 0.4202 |
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+ | 3.1903 | 0.6739 | 7500 | 0.4547 | 0.4260 |
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+ | 3.1618 | 0.7009 | 7800 | 0.4512 | 0.4496 |
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+ | 2.8182 | 0.7278 | 8100 | 0.4548 | 0.4369 |
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+ | 2.9933 | 0.7548 | 8400 | 0.4716 | 0.4215 |
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+ | 2.8348 | 0.7817 | 8700 | 0.4551 | 0.4380 |
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+ | 2.9557 | 0.8087 | 9000 | 0.4702 | 0.4429 |
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+ | 2.9472 | 0.8356 | 9300 | 0.4360 | 0.4405 |
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+ | 2.9094 | 0.8626 | 9600 | 0.4693 | 0.4278 |
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+ | 3.0442 | 0.8895 | 9900 | 0.4419 | 0.4198 |
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+ | 2.7861 | 0.9165 | 10200 | 0.4344 | 0.4079 |
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+ | 2.9604 | 0.9435 | 10500 | 0.4338 | 0.4125 |
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+ | 2.8824 | 0.9704 | 10800 | 0.4474 | 0.4136 |
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+ | 2.9609 | 0.9974 | 11100 | 0.4267 | 0.4129 |
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+ | 2.8458 | 1.0243 | 11400 | 0.4365 | 0.3914 |
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+ | 2.9807 | 1.0512 | 11700 | 0.4217 | 0.3915 |
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+ | 2.7052 | 1.0782 | 12000 | 0.4294 | 0.3914 |
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+ | 2.7261 | 1.1051 | 12300 | 0.4059 | 0.3979 |
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+ | 2.757 | 1.1321 | 12600 | 0.4055 | 0.3924 |
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+ | 2.6792 | 1.1590 | 12900 | 0.4168 | 0.3943 |
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+ | 2.6238 | 1.1860 | 13200 | 0.4132 | 0.4120 |
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+ | 2.704 | 1.2130 | 13500 | 0.4135 | 0.3984 |
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+ | 2.7817 | 1.2399 | 13800 | 0.4213 | 0.3953 |
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+ | 2.7019 | 1.2669 | 14100 | 0.4136 | 0.3903 |
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+ | 2.683 | 1.2938 | 14400 | 0.4008 | 0.3857 |
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+ | 2.6341 | 1.3208 | 14700 | 0.3932 | 0.3921 |
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+ | 2.8109 | 1.3477 | 15000 | 0.3920 | 0.3886 |
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+ | 2.7188 | 1.3747 | 15300 | 0.4107 | 0.3775 |
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+ | 2.787 | 1.4016 | 15600 | 0.4017 | 0.3832 |
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+ | 2.5539 | 1.4286 | 15900 | 0.3919 | 0.3870 |
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+ | 2.7399 | 1.4556 | 16200 | 0.4052 | 0.3787 |
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+ | 2.6653 | 1.4825 | 16500 | 0.3994 | 0.3781 |
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+ | 2.6334 | 1.5095 | 16800 | 0.3962 | 0.3804 |
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+ | 2.6855 | 1.5364 | 17100 | 0.3904 | 0.3799 |
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+ | 2.5878 | 1.5634 | 17400 | 0.3874 | 0.3738 |
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+ | 2.6821 | 1.5903 | 17700 | 0.4038 | 0.3801 |
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+ | 2.7367 | 1.6173 | 18000 | 0.3895 | 0.3786 |
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+ | 2.5238 | 1.6442 | 18300 | 0.3802 | 0.3714 |
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+ | 2.6262 | 1.6712 | 18600 | 0.3871 | 0.3735 |
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+ | 2.6882 | 1.6982 | 18900 | 0.3718 | 0.3598 |
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+ | 2.6244 | 1.7251 | 19200 | 0.3690 | 0.3702 |
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+ | 2.5328 | 1.7521 | 19500 | 0.3749 | 0.3696 |
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+ | 2.7317 | 1.7790 | 19800 | 0.3849 | 0.3671 |
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+ | 2.7712 | 1.8060 | 20100 | 0.3799 | 0.3572 |
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+ | 2.5236 | 1.8329 | 20400 | 0.3669 | 0.3586 |
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+ | 2.5933 | 1.8599 | 20700 | 0.3695 | 0.3699 |
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+ | 2.6017 | 1.8869 | 21000 | 0.3794 | 0.3608 |
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+ | 2.6945 | 1.9138 | 21300 | 0.3683 | 0.3660 |
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+ | 2.4709 | 1.9408 | 21600 | 0.3681 | 0.3566 |
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+ | 2.3483 | 1.9677 | 21900 | 0.3668 | 0.3583 |
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+ | 2.441 | 1.9947 | 22200 | 0.3765 | 0.3623 |
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+ | 2.3229 | 2.0216 | 22500 | 0.3814 | 0.3570 |
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+ | 2.4638 | 2.0485 | 22800 | 0.3653 | 0.3535 |
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+ | 2.4375 | 2.0755 | 23100 | 0.3715 | 0.3556 |
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+ | 2.449 | 2.1024 | 23400 | 0.3664 | 0.3539 |
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+ | 2.3533 | 2.1294 | 23700 | 0.3648 | 0.3522 |
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+ | 2.5918 | 2.1563 | 24000 | 0.3697 | 0.3495 |
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+ | 2.2601 | 2.1833 | 24300 | 0.3645 | 0.3509 |
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+ | 2.4091 | 2.2103 | 24600 | 0.3633 | 0.3481 |
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+ | 2.5612 | 2.2372 | 24900 | 0.3947 | 0.3475 |
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+ | 2.4217 | 2.2642 | 25200 | 0.3683 | 0.3538 |
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+ | 2.4534 | 2.2911 | 25500 | 0.3564 | 0.3521 |
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+ | 2.4084 | 2.3181 | 25800 | 0.3620 | 0.3489 |
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+ | 2.3584 | 2.3450 | 26100 | 0.3761 | 0.3561 |
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+ | 2.2511 | 2.3720 | 26400 | 0.3603 | 0.3495 |
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+ | 2.4207 | 2.3989 | 26700 | 0.3563 | 0.3455 |
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+ | 2.4695 | 2.4259 | 27000 | 0.3571 | 0.3428 |
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+ | 2.6855 | 2.4529 | 27300 | 0.3468 | 0.3471 |
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+ | 2.3552 | 2.4798 | 27600 | 0.3503 | 0.3436 |
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+ | 2.3278 | 2.5068 | 27900 | 0.3561 | 0.3503 |
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+ | 2.3505 | 2.5337 | 28200 | 0.3532 | 0.3504 |
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+ | 2.472 | 2.5607 | 28500 | 0.3460 | 0.3463 |
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+ | 2.3524 | 2.5876 | 28800 | 0.3551 | 0.3483 |
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+ | 2.4979 | 2.6146 | 29100 | 0.3512 | 0.3322 |
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+ | 2.3248 | 2.6416 | 29400 | 0.3572 | 0.3491 |
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+ | 2.5329 | 2.6685 | 29700 | 0.3395 | 0.3474 |
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+ | 2.4015 | 2.6955 | 30000 | 0.3545 | 0.3382 |
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+ | 2.3657 | 2.7224 | 30300 | 0.3484 | 0.3422 |
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+ | 2.3756 | 2.7494 | 30600 | 0.3436 | 0.3396 |
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+ | 2.4377 | 2.7763 | 30900 | 0.3462 | 0.3300 |
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+ | 2.4235 | 2.8033 | 31200 | 0.3405 | 0.3319 |
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+ | 2.4171 | 2.8302 | 31500 | 0.3743 | 0.3426 |
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+ | 2.2713 | 2.8572 | 31800 | 0.3443 | 0.3285 |
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+ | 2.3465 | 2.8842 | 32100 | 0.3480 | 0.3441 |
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+ | 2.2693 | 2.9111 | 32400 | 0.3538 | 0.3374 |
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+ | 2.2837 | 2.9381 | 32700 | 0.3352 | 0.3316 |
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+ | 2.2519 | 2.9650 | 33000 | 0.3453 | 0.3425 |
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+ | 2.3385 | 2.9920 | 33300 | 0.3369 | 0.3328 |
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+ | 2.4399 | 3.0189 | 33600 | 0.3369 | 0.3314 |
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+ | 2.1657 | 3.0458 | 33900 | 0.3354 | 0.3210 |
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+ | 2.1836 | 3.0728 | 34200 | 0.3418 | 0.3305 |
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+ | 2.1411 | 3.0997 | 34500 | 0.3403 | 0.3274 |
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+ | 2.1968 | 3.1267 | 34800 | 0.3431 | 0.3271 |
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+ | 2.1438 | 3.1536 | 35100 | 0.3344 | 0.3203 |
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+ | 2.2291 | 3.1806 | 35400 | 0.3370 | 0.3304 |
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+ | 2.2565 | 3.2076 | 35700 | 0.3379 | 0.3211 |
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+ | 2.2529 | 3.2345 | 36000 | 0.3323 | 0.3172 |
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+ | 2.1685 | 3.2615 | 36300 | 0.3289 | 0.3204 |
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+ | 2.0921 | 3.2884 | 36600 | 0.3380 | 0.3371 |
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+ | 2.2647 | 3.3154 | 36900 | 0.3278 | 0.3212 |
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+ | 2.1798 | 3.3423 | 37200 | 0.3404 | 0.3267 |
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+ | 2.0501 | 3.3693 | 37500 | 0.3318 | 0.3171 |
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+ | 2.1228 | 3.3963 | 37800 | 0.3377 | 0.3117 |
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+ | 2.2038 | 3.4232 | 38100 | 0.3312 | 0.3161 |
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+ | 2.113 | 3.4502 | 38400 | 0.3170 | 0.3131 |
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+ | 2.3311 | 3.4771 | 38700 | 0.3291 | 0.3179 |
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+ | 2.1042 | 3.5041 | 39000 | 0.3219 | 0.3159 |
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+ | 2.2017 | 3.5310 | 39300 | 0.3449 | 0.3168 |
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+ | 2.1555 | 3.5580 | 39600 | 0.3239 | 0.3091 |
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+ | 2.0275 | 3.5849 | 39900 | 0.3214 | 0.3108 |
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+ | 2.1272 | 3.6119 | 40200 | 0.3313 | 0.3141 |
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+ | 2.1742 | 3.6389 | 40500 | 0.3145 | 0.3104 |
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+ | 2.2524 | 3.6658 | 40800 | 0.3098 | 0.3073 |
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+ | 2.3791 | 3.6928 | 41100 | 0.3129 | 0.3151 |
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+ | 2.1903 | 3.7197 | 41400 | 0.3140 | 0.3086 |
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+ | 2.1773 | 3.7467 | 41700 | 0.3170 | 0.3122 |
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+ | 2.2465 | 3.7736 | 42000 | 0.3137 | 0.3113 |
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+ | 2.152 | 3.8006 | 42300 | 0.3090 | 0.3050 |
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+ | 2.0966 | 3.8275 | 42600 | 0.3133 | 0.3034 |
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+ | 2.0236 | 3.8545 | 42900 | 0.3065 | 0.3053 |
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+ | 2.2719 | 3.8815 | 43200 | 0.3177 | 0.3038 |
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+ | 2.0735 | 3.9084 | 43500 | 0.3057 | 0.3036 |
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+ | 2.0077 | 3.9354 | 43800 | 0.3083 | 0.2995 |
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+ | 2.2148 | 3.9623 | 44100 | 0.3100 | 0.3061 |
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+ | 1.9275 | 3.9893 | 44400 | 0.3193 | 0.3001 |
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+ | 2.0617 | 4.0162 | 44700 | 0.3018 | 0.3014 |
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+ | 1.97 | 4.0431 | 45000 | 0.2992 | 0.3017 |
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+ | 2.0957 | 4.0701 | 45300 | 0.3084 | 0.3047 |
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+ | 2.0003 | 4.0970 | 45600 | 0.3127 | 0.2997 |
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+ | 2.0239 | 4.1240 | 45900 | 0.3080 | 0.2988 |
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+ | 1.8299 | 4.1510 | 46200 | 0.3096 | 0.2993 |
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+ | 2.0207 | 4.1779 | 46500 | 0.3116 | 0.2990 |
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+ | 2.3016 | 4.2049 | 46800 | 0.2990 | 0.2965 |
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+ | 2.0119 | 4.2318 | 47100 | 0.2991 | 0.2978 |
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+ | 2.0965 | 4.2588 | 47400 | 0.3046 | 0.2969 |
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+ | 2.0322 | 4.2857 | 47700 | 0.2995 | 0.2982 |
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+ | 1.8958 | 4.3127 | 48000 | 0.3045 | 0.2984 |
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+ | 2.0243 | 4.3396 | 48300 | 0.3046 | 0.2936 |
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+ | 2.0465 | 4.3666 | 48600 | 0.3049 | 0.2937 |
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+ | 1.9224 | 4.3936 | 48900 | 0.2986 | 0.2910 |
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+ | 2.0303 | 4.4205 | 49200 | 0.3027 | 0.2925 |
221
+ | 1.9259 | 4.4475 | 49500 | 0.3035 | 0.2931 |
222
+ | 2.1682 | 4.4744 | 49800 | 0.3020 | 0.2921 |
223
+ | 1.9361 | 4.5014 | 50100 | 0.2984 | 0.2924 |
224
+ | 1.9593 | 4.5283 | 50400 | 0.2980 | 0.2887 |
225
+ | 2.0082 | 4.5553 | 50700 | 0.2959 | 0.2878 |
226
+ | 2.0995 | 4.5822 | 51000 | 0.2977 | 0.2886 |
227
+ | 1.9609 | 4.6092 | 51300 | 0.2950 | 0.2892 |
228
+ | 1.8096 | 4.6362 | 51600 | 0.2979 | 0.2890 |
229
+ | 1.8145 | 4.6631 | 51900 | 0.2978 | 0.2877 |
230
+ | 1.8261 | 4.6901 | 52200 | 0.2958 | 0.2871 |
231
+ | 1.8683 | 4.7170 | 52500 | 0.2939 | 0.2854 |
232
+ | 2.0299 | 4.7440 | 52800 | 0.2899 | 0.2851 |
233
+ | 2.0949 | 4.7709 | 53100 | 0.2916 | 0.2848 |
234
+ | 1.8456 | 4.7979 | 53400 | 0.2911 | 0.2854 |
235
+ | 1.9542 | 4.8249 | 53700 | 0.2932 | 0.2837 |
236
+ | 1.8429 | 4.8518 | 54000 | 0.2942 | 0.2866 |
237
+ | 1.9042 | 4.8788 | 54300 | 0.2939 | 0.2852 |
238
+ | 2.0831 | 4.9057 | 54600 | 0.2903 | 0.2848 |
239
+ | 1.8793 | 4.9327 | 54900 | 0.2912 | 0.2851 |
240
+ | 1.7786 | 4.9596 | 55200 | 0.2917 | 0.2850 |
241
+ | 1.9494 | 4.9866 | 55500 | 0.2922 | 0.2847 |
242
+
243
+
244
+ ### Framework versions
245
+
246
+ - Transformers 4.47.1
247
+ - Pytorch 2.5.1+cu124
248
+ - Datasets 3.2.0
249
+ - Tokenizers 0.21.0