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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- hf-asr-leaderboard |
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datasets: |
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- librispeech_asr |
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model-index: |
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- name: hubert-base-libri-clean-ft100h-v3 |
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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: LibriSpeech |
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type: librispeech_asr |
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config: clean |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: '8.1938' |
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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: LibriSpeech |
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type: librispeech_asr |
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config: other |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: '16.9783' |
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language: |
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- en |
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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-base-libri-clean-ft100h-v3 |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the librispeech_asr dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1120 |
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- Wer: 0.1332 |
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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: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 600 |
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- num_epochs: 8 |
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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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| 5.201 | 0.14 | 250 | 3.9799 | 1.0 | |
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| 2.8893 | 0.28 | 500 | 3.4838 | 1.0 | |
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| 2.8603 | 0.42 | 750 | 3.3505 | 1.0 | |
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| 2.7216 | 0.56 | 1000 | 2.1194 | 0.9989 | |
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| 1.3372 | 0.7 | 1250 | 0.8124 | 0.6574 | |
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| 0.8238 | 0.84 | 1500 | 0.5712 | 0.5257 | |
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| 0.6449 | 0.98 | 1750 | 0.4442 | 0.4428 | |
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| 0.5241 | 1.12 | 2000 | 0.3442 | 0.3672 | |
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| 0.4458 | 1.26 | 2250 | 0.2850 | 0.3186 | |
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| 0.3959 | 1.4 | 2500 | 0.2507 | 0.2882 | |
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| 0.3641 | 1.54 | 2750 | 0.2257 | 0.2637 | |
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| 0.3307 | 1.68 | 3000 | 0.2044 | 0.2434 | |
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| 0.2996 | 1.82 | 3250 | 0.1969 | 0.2313 | |
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| 0.2794 | 1.96 | 3500 | 0.1823 | 0.2193 | |
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| 0.2596 | 2.1 | 3750 | 0.1717 | 0.2096 | |
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| 0.2563 | 2.24 | 4000 | 0.1653 | 0.2000 | |
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| 0.2532 | 2.38 | 4250 | 0.1615 | 0.1971 | |
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| 0.2376 | 2.52 | 4500 | 0.1559 | 0.1916 | |
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| 0.2341 | 2.66 | 4750 | 0.1494 | 0.1855 | |
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| 0.2102 | 2.8 | 5000 | 0.1464 | 0.1781 | |
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| 0.2222 | 2.94 | 5250 | 0.1399 | 0.1732 | |
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| 0.2081 | 3.08 | 5500 | 0.1450 | 0.1707 | |
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| 0.1963 | 3.22 | 5750 | 0.1337 | 0.1655 | |
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| 0.2107 | 3.36 | 6000 | 0.1344 | 0.1633 | |
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| 0.1866 | 3.5 | 6250 | 0.1339 | 0.1611 | |
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| 0.186 | 3.64 | 6500 | 0.1311 | 0.1563 | |
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| 0.1703 | 3.78 | 6750 | 0.1307 | 0.1537 | |
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| 0.1819 | 3.92 | 7000 | 0.1277 | 0.1555 | |
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| 0.176 | 4.06 | 7250 | 0.1280 | 0.1515 | |
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| 0.1837 | 4.2 | 7500 | 0.1249 | 0.1504 | |
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| 0.1678 | 4.34 | 7750 | 0.1236 | 0.1480 | |
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| 0.1624 | 4.48 | 8000 | 0.1194 | 0.1456 | |
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| 0.1631 | 4.62 | 8250 | 0.1215 | 0.1462 | |
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| 0.1736 | 4.76 | 8500 | 0.1192 | 0.1451 | |
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| 0.1752 | 4.9 | 8750 | 0.1206 | 0.1432 | |
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| 0.1578 | 5.04 | 9000 | 0.1151 | 0.1415 | |
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| 0.1537 | 5.18 | 9250 | 0.1185 | 0.1402 | |
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| 0.1771 | 5.33 | 9500 | 0.1165 | 0.1414 | |
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| 0.1481 | 5.47 | 9750 | 0.1152 | 0.1413 | |
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| 0.1509 | 5.61 | 10000 | 0.1152 | 0.1382 | |
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| 0.146 | 5.75 | 10250 | 0.1133 | 0.1385 | |
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| 0.1464 | 5.89 | 10500 | 0.1139 | 0.1371 | |
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| 0.1442 | 6.03 | 10750 | 0.1162 | 0.1365 | |
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| 0.128 | 6.17 | 11000 | 0.1147 | 0.1371 | |
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| 0.1381 | 6.31 | 11250 | 0.1148 | 0.1378 | |
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| 0.1343 | 6.45 | 11500 | 0.1113 | 0.1363 | |
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| 0.1325 | 6.59 | 11750 | 0.1134 | 0.1355 | |
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| 0.1442 | 6.73 | 12000 | 0.1142 | 0.1358 | |
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| 0.1286 | 6.87 | 12250 | 0.1133 | 0.1352 | |
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| 0.1349 | 7.01 | 12500 | 0.1129 | 0.1344 | |
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| 0.1338 | 7.15 | 12750 | 0.1131 | 0.1328 | |
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| 0.1403 | 7.29 | 13000 | 0.1124 | 0.1338 | |
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| 0.1314 | 7.43 | 13250 | 0.1141 | 0.1335 | |
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| 0.1283 | 7.57 | 13500 | 0.1124 | 0.1332 | |
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| 0.1347 | 7.71 | 13750 | 0.1107 | 0.1332 | |
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| 0.1195 | 7.85 | 14000 | 0.1119 | 0.1332 | |
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| 0.1326 | 7.99 | 14250 | 0.1120 | 0.1332 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.12.1 |
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