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w2v-fine-tune-test-no-ws2

Automatic Speech Recognition
Transformers
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wav2vec2-bert
Generated from Trainer
Eval Results
Inference Endpoints
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README.md ADDED
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+ ---
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+ base_model: ylacombe/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-fine-tune-test-no-ws2
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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: tr
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+ split: test
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+ args: tr
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+ metrics:
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+ - name: Wer
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+ type: wer
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+ value: 0.11088339984899148
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+ ---
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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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+
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+ # w2v-fine-tune-test-no-ws2
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+
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+ This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/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: 0.1513
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+ - Wer: 0.1109
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 32
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+ - eval_batch_size: 8
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+ - seed: 42
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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: 10
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|
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+ | 2.192 | 0.22 | 300 | 0.2797 | 0.2985 |
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+ | 0.2226 | 0.44 | 600 | 0.2989 | 0.3491 |
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+ | 0.1941 | 0.66 | 900 | 0.2558 | 0.2451 |
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+ | 0.1659 | 0.88 | 1200 | 0.2320 | 0.2289 |
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+ | 0.1332 | 1.1 | 1500 | 0.2063 | 0.1971 |
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+ | 0.1129 | 1.31 | 1800 | 0.1873 | 0.2029 |
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+ | 0.1044 | 1.53 | 2100 | 0.1765 | 0.1856 |
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+ | 0.1026 | 1.75 | 2400 | 0.1719 | 0.1752 |
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+ | 0.0982 | 1.97 | 2700 | 0.1927 | 0.2023 |
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+ | 0.0769 | 2.19 | 3000 | 0.1776 | 0.1671 |
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+ | 0.0715 | 2.41 | 3300 | 0.1626 | 0.1634 |
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+ | 0.0695 | 2.63 | 3600 | 0.1666 | 0.1654 |
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+ | 0.0612 | 2.85 | 3900 | 0.1760 | 0.1609 |
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+ | 0.0614 | 3.07 | 4200 | 0.1645 | 0.1593 |
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+ | 0.0476 | 3.29 | 4500 | 0.1685 | 0.1593 |
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+ | 0.048 | 3.51 | 4800 | 0.1790 | 0.1583 |
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+ | 0.0489 | 3.73 | 5100 | 0.1578 | 0.1535 |
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+ | 0.0456 | 3.94 | 5400 | 0.1610 | 0.1617 |
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+ | 0.041 | 4.16 | 5700 | 0.1559 | 0.1439 |
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+ | 0.0367 | 4.38 | 6000 | 0.1536 | 0.1436 |
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+ | 0.0321 | 4.6 | 6300 | 0.1591 | 0.1449 |
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+ | 0.0349 | 4.82 | 6600 | 0.1616 | 0.1419 |
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+ | 0.0308 | 5.04 | 6900 | 0.1501 | 0.1401 |
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+ | 0.0233 | 5.26 | 7200 | 0.1588 | 0.1394 |
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+ | 0.0253 | 5.48 | 7500 | 0.1633 | 0.1356 |
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+ | 0.0254 | 5.7 | 7800 | 0.1522 | 0.1339 |
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+ | 0.0245 | 5.92 | 8100 | 0.1598 | 0.1371 |
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+ | 0.0189 | 6.14 | 8400 | 0.1497 | 0.1324 |
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+ | 0.0174 | 6.36 | 8700 | 0.1487 | 0.1270 |
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+ | 0.0178 | 6.57 | 9000 | 0.1397 | 0.1286 |
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+ | 0.0173 | 6.79 | 9300 | 0.1495 | 0.1281 |
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+ | 0.0178 | 7.01 | 9600 | 0.1462 | 0.1222 |
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+ | 0.0124 | 7.23 | 9900 | 0.1516 | 0.1225 |
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+ | 0.0121 | 7.45 | 10200 | 0.1554 | 0.1190 |
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+ | 0.0128 | 7.67 | 10500 | 0.1453 | 0.1228 |
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+ | 0.0113 | 7.89 | 10800 | 0.1468 | 0.1178 |
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+ | 0.0086 | 8.11 | 11100 | 0.1556 | 0.1186 |
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+ | 0.0085 | 8.33 | 11400 | 0.1507 | 0.1154 |
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+ | 0.0073 | 8.55 | 11700 | 0.1494 | 0.1169 |
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+ | 0.0079 | 8.77 | 12000 | 0.1507 | 0.1152 |
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+ | 0.0089 | 8.98 | 12300 | 0.1456 | 0.1137 |
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+ | 0.0062 | 9.2 | 12600 | 0.1518 | 0.1127 |
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+ | 0.005 | 9.42 | 12900 | 0.1534 | 0.1115 |
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+ | 0.005 | 9.64 | 13200 | 0.1514 | 0.1110 |
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+ | 0.0048 | 9.86 | 13500 | 0.1513 | 0.1109 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.0.dev0
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+ - Pytorch 2.1.2+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.1
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