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--- |
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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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- common_voice_16_0 |
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metrics: |
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- wer |
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model-index: |
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- name: w2v-bertkmr-test |
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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: common_voice_16_0 |
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type: common_voice_16_0 |
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config: kmr |
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split: test |
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args: kmr |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.1570856537948175 |
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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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# w2v-bertkmr-test |
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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 common_voice_16_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2399 |
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- Wer: 0.1571 |
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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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- 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: 150 |
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- num_epochs: 10 |
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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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| No log | 0.8 | 200 | 0.3476 | 0.3257 | |
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| 1.2561 | 1.6 | 400 | 0.2756 | 0.2669 | |
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| 0.1906 | 2.4 | 600 | 0.2484 | 0.2363 | |
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| 0.1906 | 3.2 | 800 | 0.2336 | 0.2177 | |
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| 0.1242 | 4.0 | 1000 | 0.2192 | 0.1919 | |
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| 0.0853 | 4.8 | 1200 | 0.2217 | 0.1879 | |
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| 0.0853 | 5.6 | 1400 | 0.2272 | 0.1786 | |
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| 0.0586 | 6.4 | 1600 | 0.2292 | 0.1695 | |
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| 0.0365 | 7.2 | 1800 | 0.2276 | 0.1613 | |
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| 0.0365 | 8.0 | 2000 | 0.2127 | 0.1626 | |
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| 0.0222 | 8.8 | 2200 | 0.2271 | 0.1568 | |
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| 0.0118 | 9.6 | 2400 | 0.2399 | 0.1571 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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