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--- |
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base_model: Umong/wav2vec2-xls-r-300m-bengali |
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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: wav2vec2-xls-r-300m-bengali |
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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-xls-r-300m-bengali |
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This model is a fine-tuned version of [Umong/wav2vec2-xls-r-300m-bengali](https://huggingface.co/Umong/wav2vec2-xls-r-300m-bengali) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1636 |
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- Wer: 0.0883 |
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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: 0.0003 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 5 |
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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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| 5.3076 | 0.16 | 400 | 1.5883 | 0.9394 | |
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| 0.8841 | 0.33 | 800 | 0.5188 | 0.5337 | |
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| 0.5896 | 0.49 | 1200 | 0.4029 | 0.4340 | |
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| 0.4964 | 0.66 | 1600 | 0.3429 | 0.3766 | |
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| 0.4553 | 0.82 | 2000 | 0.3196 | 0.3642 | |
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| 0.4222 | 0.99 | 2400 | 0.3004 | 0.3436 | |
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| 0.3709 | 1.15 | 2800 | 0.2812 | 0.3225 | |
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| 0.352 | 1.32 | 3200 | 0.2753 | 0.3124 | |
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| 0.3283 | 1.48 | 3600 | 0.2616 | 0.2979 | |
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| 0.3235 | 1.65 | 4000 | 0.2573 | 0.2944 | |
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| 0.3129 | 1.81 | 4400 | 0.2458 | 0.2809 | |
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| 0.306 | 1.98 | 4800 | 0.2344 | 0.2771 | |
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| 0.2701 | 2.14 | 5200 | 0.2318 | 0.2661 | |
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| 0.2653 | 2.31 | 5600 | 0.2253 | 0.2629 | |
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| 0.2626 | 2.47 | 6000 | 0.2186 | 0.2542 | |
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| 0.2541 | 2.63 | 6400 | 0.2074 | 0.2474 | |
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| 0.2235 | 2.8 | 6800 | 0.2102 | 0.2442 | |
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| 0.2185 | 2.96 | 7200 | 0.2019 | 0.2327 | |
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| 0.2061 | 3.13 | 7600 | 0.1994 | 0.2308 | |
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| 0.2011 | 3.29 | 8000 | 0.1942 | 0.2260 | |
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| 0.1986 | 3.46 | 8400 | 0.1867 | 0.2187 | |
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| 0.197 | 3.62 | 8800 | 0.1825 | 0.2177 | |
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| 0.1931 | 3.79 | 9200 | 0.1856 | 0.2153 | |
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| 0.1879 | 3.95 | 9600 | 0.1777 | 0.2088 | |
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| 0.1599 | 4.12 | 10000 | 0.1781 | 0.0968 | |
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| 0.153 | 4.28 | 10400 | 0.1738 | 0.0944 | |
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| 0.1475 | 4.45 | 10800 | 0.1713 | 0.0905 | |
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| 0.1448 | 4.61 | 11200 | 0.1683 | 0.0907 | |
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| 0.1445 | 4.78 | 11600 | 0.1649 | 0.0897 | |
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| 0.1423 | 4.94 | 12000 | 0.1636 | 0.0883 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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