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--- |
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library_name: transformers |
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language: |
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- lg |
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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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datasets: |
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- yogera |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-bert |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Yogera |
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type: yogera |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.18076661374461328 |
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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-bert |
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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 Yogera dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3420 |
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- Wer: 0.1808 |
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- Cer: 0.0385 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- 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 | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 2.0247 | 1.0 | 57 | 0.3818 | 0.4570 | 0.0903 | |
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| 0.3116 | 2.0 | 114 | 0.2514 | 0.3059 | 0.0614 | |
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| 0.2069 | 3.0 | 171 | 0.2253 | 0.2704 | 0.0555 | |
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| 0.1586 | 4.0 | 228 | 0.2139 | 0.2601 | 0.0506 | |
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| 0.1293 | 5.0 | 285 | 0.2124 | 0.2200 | 0.0458 | |
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| 0.1052 | 6.0 | 342 | 0.2168 | 0.2087 | 0.0438 | |
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| 0.0864 | 7.0 | 399 | 0.2385 | 0.2110 | 0.0444 | |
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| 0.0791 | 8.0 | 456 | 0.2177 | 0.2030 | 0.0424 | |
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| 0.068 | 9.0 | 513 | 0.2356 | 0.2002 | 0.0422 | |
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| 0.0609 | 10.0 | 570 | 0.2482 | 0.2083 | 0.0429 | |
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| 0.0496 | 11.0 | 627 | 0.2482 | 0.1977 | 0.0438 | |
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| 0.0467 | 12.0 | 684 | 0.2556 | 0.1978 | 0.0419 | |
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| 0.0398 | 13.0 | 741 | 0.2688 | 0.1960 | 0.0409 | |
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| 0.0369 | 14.0 | 798 | 0.2580 | 0.1951 | 0.0411 | |
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| 0.0349 | 15.0 | 855 | 0.2673 | 0.1989 | 0.0426 | |
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| 0.0333 | 16.0 | 912 | 0.2926 | 0.1936 | 0.0413 | |
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| 0.0295 | 17.0 | 969 | 0.2854 | 0.1962 | 0.0408 | |
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| 0.0241 | 18.0 | 1026 | 0.2841 | 0.1888 | 0.0406 | |
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| 0.0221 | 19.0 | 1083 | 0.2928 | 0.1954 | 0.0419 | |
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| 0.0213 | 20.0 | 1140 | 0.3104 | 0.2041 | 0.0436 | |
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| 0.0208 | 21.0 | 1197 | 0.2975 | 0.1881 | 0.0416 | |
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| 0.0217 | 22.0 | 1254 | 0.2764 | 0.1913 | 0.0417 | |
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| 0.0193 | 23.0 | 1311 | 0.2933 | 0.1928 | 0.0419 | |
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| 0.0135 | 24.0 | 1368 | 0.3073 | 0.1859 | 0.0401 | |
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| 0.0146 | 25.0 | 1425 | 0.2925 | 0.1851 | 0.0401 | |
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| 0.0156 | 26.0 | 1482 | 0.3120 | 0.1898 | 0.0407 | |
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| 0.0137 | 27.0 | 1539 | 0.2996 | 0.1915 | 0.0428 | |
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| 0.0116 | 28.0 | 1596 | 0.3228 | 0.1859 | 0.0409 | |
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| 0.011 | 29.0 | 1653 | 0.3331 | 0.1915 | 0.0416 | |
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| 0.0116 | 30.0 | 1710 | 0.3209 | 0.1804 | 0.0391 | |
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| 0.0083 | 31.0 | 1767 | 0.3333 | 0.1862 | 0.0400 | |
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| 0.006 | 32.0 | 1824 | 0.3504 | 0.1854 | 0.0398 | |
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| 0.0075 | 33.0 | 1881 | 0.3189 | 0.1965 | 0.0413 | |
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| 0.0054 | 34.0 | 1938 | 0.3461 | 0.1837 | 0.0398 | |
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| 0.0074 | 35.0 | 1995 | 0.3263 | 0.1867 | 0.0402 | |
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| 0.0071 | 36.0 | 2052 | 0.3430 | 0.1876 | 0.0401 | |
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| 0.0068 | 37.0 | 2109 | 0.3381 | 0.1913 | 0.0411 | |
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| 0.0097 | 38.0 | 2166 | 0.3150 | 0.1937 | 0.0416 | |
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| 0.0079 | 39.0 | 2223 | 0.3313 | 0.1856 | 0.0405 | |
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| 0.0062 | 40.0 | 2280 | 0.3420 | 0.1808 | 0.0385 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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