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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- common_voice
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model-index:
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- name: wav2vec2-large-xls-r-2b-armenian-colab
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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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# wav2vec2-large-xls-r-2b-armenian-colab
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the common_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.5166
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- Wer: 0.7397
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 120
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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 |
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|:-------------:|:------:|:-----:|:---------------:|:------:|
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| 3.7057 | 2.38 | 200 | 0.7731 | 0.8091 |
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| 0.5797 | 4.76 | 400 | 0.8279 | 0.7804 |
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| 0.4341 | 7.14 | 600 | 1.0343 | 0.8285 |
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| 0.3135 | 9.52 | 800 | 1.0551 | 0.8066 |
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| 0.2409 | 11.9 | 1000 | 1.0686 | 0.7897 |
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| 0.1998 | 14.29 | 1200 | 1.1329 | 0.7766 |
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| 0.1729 | 16.67 | 1400 | 1.3234 | 0.8567 |
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| 0.1533 | 19.05 | 1600 | 1.2432 | 0.8160 |
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| 0.1354 | 21.43 | 1800 | 1.2780 | 0.7954 |
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| 0.12 | 23.81 | 2000 | 1.2228 | 0.8054 |
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| 0.1175 | 26.19 | 2200 | 1.3484 | 0.8129 |
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| 0.1141 | 28.57 | 2400 | 1.2881 | 0.9130 |
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| 0.1053 | 30.95 | 2600 | 1.1972 | 0.7910 |
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| 0.0954 | 33.33 | 2800 | 1.3702 | 0.8048 |
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| 0.0842 | 35.71 | 3000 | 1.3963 | 0.7960 |
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| 0.0793 | 38.1 | 3200 | 1.4690 | 0.7991 |
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| 0.0707 | 40.48 | 3400 | 1.5045 | 0.8085 |
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| 0.0745 | 42.86 | 3600 | 1.4749 | 0.8004 |
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| 0.0693 | 45.24 | 3800 | 1.5047 | 0.7960 |
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| 0.0646 | 47.62 | 4000 | 1.4216 | 0.7997 |
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| 0.0555 | 50.0 | 4200 | 1.4676 | 0.8029 |
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| 0.056 | 52.38 | 4400 | 1.4273 | 0.8104 |
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| 0.0465 | 54.76 | 4600 | 1.3999 | 0.7841 |
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| 0.046 | 57.14 | 4800 | 1.6130 | 0.8473 |
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| 0.0404 | 59.52 | 5000 | 1.5586 | 0.7841 |
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| 0.0403 | 61.9 | 5200 | 1.3959 | 0.7653 |
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| 0.0404 | 64.29 | 5400 | 1.5318 | 0.8041 |
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| 0.0365 | 66.67 | 5600 | 1.5300 | 0.7854 |
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| 0.0338 | 69.05 | 5800 | 1.5051 | 0.7885 |
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| 0.0307 | 71.43 | 6000 | 1.5647 | 0.7935 |
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| 0.0235 | 73.81 | 6200 | 1.4919 | 0.8154 |
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| 0.0268 | 76.19 | 6400 | 1.5259 | 0.8060 |
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| 0.0275 | 78.57 | 6600 | 1.3985 | 0.7897 |
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| 0.022 | 80.95 | 6800 | 1.5515 | 0.8154 |
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| 0.017 | 83.33 | 7000 | 1.5737 | 0.7647 |
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| 0.0205 | 85.71 | 7200 | 1.4876 | 0.7572 |
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| 0.0174 | 88.1 | 7400 | 1.6331 | 0.7829 |
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| 0.0188 | 90.48 | 7600 | 1.5108 | 0.7685 |
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| 0.0134 | 92.86 | 7800 | 1.7125 | 0.7866 |
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| 0.0125 | 95.24 | 8000 | 1.6042 | 0.7635 |
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| 0.0133 | 97.62 | 8200 | 1.4608 | 0.7478 |
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| 0.0272 | 100.0 | 8400 | 1.4784 | 0.7309 |
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| 0.0133 | 102.38 | 8600 | 1.4471 | 0.7459 |
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| 0.0094 | 104.76 | 8800 | 1.4852 | 0.7272 |
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| 0.0103 | 107.14 | 9000 | 1.5679 | 0.7409 |
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| 0.0088 | 109.52 | 9200 | 1.5090 | 0.7309 |
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| 0.0077 | 111.9 | 9400 | 1.4994 | 0.7290 |
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| 0.0068 | 114.29 | 9600 | 1.5008 | 0.7340 |
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| 0.0054 | 116.67 | 9800 | 1.5166 | 0.7390 |
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| 0.0052 | 119.05 | 10000 | 1.5166 | 0.7397 |
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### Framework versions
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- Transformers 4.14.1
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- Pytorch 1.10.0
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- Datasets 1.16.1
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- Tokenizers 0.10.3
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