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--- |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- librispeech_asr |
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- generated_from_trainer |
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model-index: |
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- name: sew-mid-100k-librispeech-clean-100h-ft |
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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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# sew-mid-100k-librispeech-clean-100h-ft |
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This model is a fine-tuned version of [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) on the LIBRISPEECH_ASR - CLEAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1976 |
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- Wer: 0.1665 |
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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: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 64 |
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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: 3.0 |
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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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| 3.4274 | 0.11 | 100 | 4.1419 | 1.0 | |
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| 2.9657 | 0.22 | 200 | 3.1203 | 1.0 | |
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| 2.9069 | 0.34 | 300 | 3.0107 | 1.0 | |
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| 2.8666 | 0.45 | 400 | 2.8960 | 1.0 | |
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| 1.4535 | 0.56 | 500 | 1.4062 | 0.8664 | |
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| 0.6821 | 0.67 | 600 | 0.5530 | 0.4930 | |
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| 0.4827 | 0.78 | 700 | 0.4122 | 0.3630 | |
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| 0.4485 | 0.9 | 800 | 0.3597 | 0.3243 | |
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| 0.2666 | 1.01 | 900 | 0.3104 | 0.2790 | |
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| 0.2378 | 1.12 | 1000 | 0.2913 | 0.2613 | |
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| 0.2516 | 1.23 | 1100 | 0.2702 | 0.2452 | |
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| 0.2456 | 1.35 | 1200 | 0.2619 | 0.2338 | |
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| 0.2392 | 1.46 | 1300 | 0.2466 | 0.2195 | |
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| 0.2117 | 1.57 | 1400 | 0.2379 | 0.2092 | |
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| 0.1837 | 1.68 | 1500 | 0.2295 | 0.2029 | |
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| 0.1757 | 1.79 | 1600 | 0.2240 | 0.1949 | |
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| 0.1626 | 1.91 | 1700 | 0.2195 | 0.1927 | |
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| 0.168 | 2.02 | 1800 | 0.2137 | 0.1853 | |
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| 0.168 | 2.13 | 1900 | 0.2123 | 0.1839 | |
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| 0.1576 | 2.24 | 2000 | 0.2095 | 0.1803 | |
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| 0.1756 | 2.35 | 2100 | 0.2075 | 0.1776 | |
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| 0.1467 | 2.47 | 2200 | 0.2049 | 0.1754 | |
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| 0.1702 | 2.58 | 2300 | 0.2013 | 0.1722 | |
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| 0.177 | 2.69 | 2400 | 0.1993 | 0.1701 | |
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| 0.1417 | 2.8 | 2500 | 0.1983 | 0.1688 | |
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| 0.1302 | 2.91 | 2600 | 0.1977 | 0.1678 | |
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### Framework versions |
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- Transformers 4.12.0.dev0 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.13.4.dev0 |
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- Tokenizers 0.10.3 |
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