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--- |
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license: apache-2.0 |
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tags: |
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- 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: hubert-librispeech-clean-100h-demo-dist |
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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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# hubert-librispeech-clean-100h-demo-dist |
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This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the LIBRISPEECH_ASR - CLEAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0984 |
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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: 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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| 2.9031 | 0.11 | 100 | 2.9220 | 1.0 | |
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| 2.6437 | 0.22 | 200 | 2.6268 | 1.0 | |
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| 0.3934 | 0.34 | 300 | 0.4860 | 0.4182 | |
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| 0.3531 | 0.45 | 400 | 0.3088 | 0.2894 | |
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| 0.2255 | 0.56 | 500 | 0.2568 | 0.2426 | |
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| 0.3379 | 0.67 | 600 | 0.2073 | 0.2011 | |
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| 0.2419 | 0.78 | 700 | 0.1849 | 0.1838 | |
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| 0.2128 | 0.9 | 800 | 0.1662 | 0.1690 | |
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| 0.1341 | 1.01 | 900 | 0.1600 | 0.1541 | |
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| 0.0946 | 1.12 | 1000 | 0.1431 | 0.1404 | |
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| 0.1643 | 1.23 | 1100 | 0.1373 | 0.1304 | |
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| 0.0663 | 1.35 | 1200 | 0.1293 | 0.1307 | |
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| 0.162 | 1.46 | 1300 | 0.1247 | 0.1266 | |
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| 0.1433 | 1.57 | 1400 | 0.1246 | 0.1262 | |
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| 0.1581 | 1.68 | 1500 | 0.1219 | 0.1154 | |
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| 0.1036 | 1.79 | 1600 | 0.1127 | 0.1081 | |
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| 0.1352 | 1.91 | 1700 | 0.1087 | 0.1040 | |
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| 0.0471 | 2.02 | 1800 | 0.1085 | 0.1005 | |
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| 0.0945 | 2.13 | 1900 | 0.1066 | 0.0973 | |
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| 0.0843 | 2.24 | 2000 | 0.1102 | 0.0964 | |
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| 0.0774 | 2.35 | 2100 | 0.1079 | 0.0940 | |
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| 0.0952 | 2.47 | 2200 | 0.1056 | 0.0927 | |
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| 0.0635 | 2.58 | 2300 | 0.1026 | 0.0920 | |
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| 0.0665 | 2.69 | 2400 | 0.1012 | 0.0905 | |
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| 0.034 | 2.8 | 2500 | 0.1009 | 0.0900 | |
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| 0.0251 | 2.91 | 2600 | 0.0993 | 0.0883 | |
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### Framework versions |
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- Transformers 4.11.0.dev0 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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