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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-base-960h |
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tags: |
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- automatic-speech-recognition |
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- libri10h |
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- mms |
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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-base-960h-librispeech-model |
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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-base-960h-librispeech-model |
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This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the LIBRI10H - ENG dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6832 |
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- Wer: 0.5184 |
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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: 0.003 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 100.0 |
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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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| 3.477 | 1.1565 | 200 | 2.8801 | 1.0 | |
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| 2.8001 | 2.3130 | 400 | 2.1488 | 0.9999 | |
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| 1.6185 | 3.4696 | 600 | 1.2806 | 0.9032 | |
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| 1.359 | 4.6261 | 800 | 1.1836 | 0.8888 | |
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| 1.2731 | 5.7826 | 1000 | 1.1609 | 0.8760 | |
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| 1.2092 | 6.9391 | 1200 | 1.1091 | 0.8629 | |
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| 1.1681 | 8.0928 | 1400 | 1.0761 | 0.8544 | |
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| 1.1297 | 9.2493 | 1600 | 1.0792 | 0.8494 | |
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| 1.0982 | 10.4058 | 1800 | 1.0455 | 0.8353 | |
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| 1.073 | 11.5623 | 2000 | 1.0372 | 0.8361 | |
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| 1.0436 | 12.7188 | 2200 | 1.0217 | 0.8368 | |
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| 1.021 | 13.8754 | 2400 | 0.9912 | 0.8129 | |
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| 0.9893 | 15.0290 | 2600 | 0.9936 | 0.8073 | |
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| 0.964 | 16.1855 | 2800 | 0.9619 | 0.7934 | |
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| 0.9391 | 17.3420 | 3000 | 0.9557 | 0.7898 | |
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| 0.9159 | 18.4986 | 3200 | 0.9378 | 0.7797 | |
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| 0.8927 | 19.6551 | 3400 | 0.9074 | 0.7680 | |
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| 0.8714 | 20.8116 | 3600 | 0.9022 | 0.7623 | |
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| 0.8412 | 21.9681 | 3800 | 0.8682 | 0.7360 | |
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| 0.8083 | 23.1217 | 4000 | 0.8436 | 0.7153 | |
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| 0.7838 | 24.2783 | 4200 | 0.8584 | 0.7048 | |
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| 0.7533 | 25.4348 | 4400 | 0.8101 | 0.6912 | |
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| 0.7286 | 26.5913 | 4600 | 0.7933 | 0.6707 | |
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| 0.6965 | 27.7478 | 4800 | 0.8056 | 0.6681 | |
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| 0.6859 | 28.9043 | 5000 | 0.7554 | 0.6417 | |
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| 0.6529 | 30.0580 | 5200 | 0.7624 | 0.6291 | |
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| 0.6315 | 31.2145 | 5400 | 0.7508 | 0.6123 | |
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| 0.6144 | 32.3710 | 5600 | 0.7255 | 0.6056 | |
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| 0.5933 | 33.5275 | 5800 | 0.7546 | 0.6045 | |
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| 0.5827 | 34.6841 | 6000 | 0.7054 | 0.5851 | |
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| 0.56 | 35.8406 | 6200 | 0.7157 | 0.5858 | |
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| 0.5431 | 36.9971 | 6400 | 0.7262 | 0.5788 | |
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| 0.5281 | 38.1507 | 6600 | 0.6931 | 0.5598 | |
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| 0.5091 | 39.3072 | 6800 | 0.7102 | 0.5660 | |
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| 0.5031 | 40.4638 | 7000 | 0.6894 | 0.5466 | |
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| 0.4867 | 41.6203 | 7200 | 0.6858 | 0.5441 | |
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| 0.4689 | 42.7768 | 7400 | 0.7051 | 0.5403 | |
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| 0.4653 | 43.9333 | 7600 | 0.6840 | 0.5335 | |
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| 0.4441 | 45.0870 | 7800 | 0.6987 | 0.5322 | |
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| 0.435 | 46.2435 | 8000 | 0.7294 | 0.5315 | |
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| 0.4302 | 47.4 | 8200 | 0.6831 | 0.5182 | |
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| 0.4173 | 48.5565 | 8400 | 0.7080 | 0.5217 | |
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| 0.4086 | 49.7130 | 8600 | 0.6974 | 0.5101 | |
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| 0.3995 | 50.8696 | 8800 | 0.6842 | 0.5059 | |
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| 0.3866 | 52.0232 | 9000 | 0.7347 | 0.5186 | |
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| 0.3779 | 53.1797 | 9200 | 0.7141 | 0.5008 | |
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| 0.3691 | 54.3362 | 9400 | 0.7005 | 0.4998 | |
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| 0.3609 | 55.4928 | 9600 | 0.7299 | 0.4964 | |
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| 0.3584 | 56.6493 | 9800 | 0.6965 | 0.4966 | |
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| 0.3461 | 57.8058 | 10000 | 0.7217 | 0.4898 | |
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| 0.3403 | 58.9623 | 10200 | 0.7178 | 0.4850 | |
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| 0.3342 | 60.1159 | 10400 | 0.7019 | 0.4832 | |
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| 0.3215 | 61.2725 | 10600 | 0.7528 | 0.4834 | |
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| 0.3182 | 62.4290 | 10800 | 0.7112 | 0.4794 | |
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| 0.3123 | 63.5855 | 11000 | 0.7456 | 0.4780 | |
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| 0.3065 | 64.7420 | 11200 | 0.7509 | 0.4729 | |
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| 0.302 | 65.8986 | 11400 | 0.7293 | 0.4743 | |
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| 0.2942 | 67.0522 | 11600 | 0.7418 | 0.4734 | |
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| 0.2872 | 68.2087 | 11800 | 0.7607 | 0.4643 | |
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| 0.2844 | 69.3652 | 12000 | 0.7360 | 0.4679 | |
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| 0.2775 | 70.5217 | 12200 | 0.7594 | 0.4639 | |
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| 0.2736 | 71.6783 | 12400 | 0.7489 | 0.4667 | |
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| 0.2633 | 72.8348 | 12600 | 0.7576 | 0.4670 | |
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| 0.2627 | 73.9913 | 12800 | 0.7881 | 0.4597 | |
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| 0.2592 | 75.1449 | 13000 | 0.7566 | 0.4573 | |
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| 0.2557 | 76.3014 | 13200 | 0.7827 | 0.4629 | |
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| 0.246 | 77.4580 | 13400 | 0.7816 | 0.4586 | |
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| 0.2455 | 78.6145 | 13600 | 0.7918 | 0.4574 | |
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| 0.238 | 79.7710 | 13800 | 0.7928 | 0.4519 | |
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| 0.2376 | 80.9275 | 14000 | 0.7769 | 0.4508 | |
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| 0.2319 | 82.0812 | 14200 | 0.7877 | 0.4519 | |
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| 0.2268 | 83.2377 | 14400 | 0.7943 | 0.4537 | |
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| 0.2297 | 84.3942 | 14600 | 0.7913 | 0.4500 | |
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| 0.2207 | 85.5507 | 14800 | 0.8011 | 0.4481 | |
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| 0.219 | 86.7072 | 15000 | 0.7940 | 0.4485 | |
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| 0.2159 | 87.8638 | 15200 | 0.8179 | 0.4470 | |
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| 0.2126 | 89.0174 | 15400 | 0.8171 | 0.4449 | |
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| 0.2097 | 90.1739 | 15600 | 0.8208 | 0.4456 | |
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| 0.2074 | 91.3304 | 15800 | 0.8218 | 0.4446 | |
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| 0.2062 | 92.4870 | 16000 | 0.8242 | 0.4439 | |
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| 0.206 | 93.6435 | 16200 | 0.8340 | 0.4432 | |
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| 0.2007 | 94.8 | 16400 | 0.8240 | 0.4420 | |
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| 0.1994 | 95.9565 | 16600 | 0.8306 | 0.4419 | |
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| 0.197 | 97.1101 | 16800 | 0.8371 | 0.4431 | |
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| 0.1955 | 98.2667 | 17000 | 0.8345 | 0.4421 | |
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| 0.1979 | 99.4232 | 17200 | 0.8382 | 0.4421 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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