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wav2vec2-large-xls-r-300m-en-colab

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
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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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+ datasets:
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+ - librispeech_asr
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+ model-index:
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+ - name: wav2vec2-large-xls-r-300m-en-colab
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+ results: []
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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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+ # wav2vec2-large-xls-r-300m-en-colab
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1169
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+ - Wer: 0.0597
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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: 0.0003
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_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: 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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+ | 5.6951 | 0.22 | 100 | 3.1606 | 1.0 |
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+ | 2.924 | 0.45 | 200 | 2.9297 | 1.0 |
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+ | 2.5328 | 0.67 | 300 | 1.4339 | 0.8953 |
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+ | 0.8611 | 0.9 | 400 | 0.6104 | 0.5306 |
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+ | 0.3714 | 1.12 | 500 | 0.2497 | 0.2150 |
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+ | 0.2015 | 1.35 | 600 | 0.1853 | 0.1615 |
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+ | 0.1593 | 1.57 | 700 | 0.1613 | 0.1366 |
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+ | 0.1436 | 1.79 | 800 | 0.1503 | 0.1311 |
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+ | 0.1249 | 2.02 | 900 | 0.1374 | 0.1038 |
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+ | 0.0936 | 2.24 | 1000 | 0.1328 | 0.1016 |
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+ | 0.0896 | 2.47 | 1100 | 0.1234 | 0.0942 |
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+ | 0.0872 | 2.69 | 1200 | 0.1148 | 0.0922 |
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+ | 0.0859 | 2.91 | 1300 | 0.1140 | 0.0892 |
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+ | 0.0733 | 3.14 | 1400 | 0.1134 | 0.0839 |
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+ | 0.0633 | 3.36 | 1500 | 0.1085 | 0.0802 |
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+ | 0.0567 | 3.59 | 1600 | 0.1103 | 0.0807 |
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+ | 0.0604 | 3.81 | 1700 | 0.1088 | 0.0809 |
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+ | 0.0586 | 4.04 | 1800 | 0.1113 | 0.0804 |
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+ | 0.0516 | 4.26 | 1900 | 0.1123 | 0.0808 |
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+ | 0.055 | 4.48 | 2000 | 0.1130 | 0.0764 |
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+ | 0.0568 | 4.71 | 2100 | 0.1128 | 0.0807 |
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+ | 0.0529 | 4.93 | 2200 | 0.1009 | 0.0727 |
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+ | 0.0455 | 5.16 | 2300 | 0.1050 | 0.0726 |
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+ | 0.0443 | 5.38 | 2400 | 0.1078 | 0.0720 |
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+ | 0.0434 | 5.61 | 2500 | 0.1027 | 0.0702 |
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+ | 0.0418 | 5.83 | 2600 | 0.1009 | 0.0693 |
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+ | 0.0381 | 6.05 | 2700 | 0.1079 | 0.0689 |
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+ | 0.0344 | 6.28 | 2800 | 0.1062 | 0.0678 |
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+ | 0.0353 | 6.5 | 2900 | 0.1054 | 0.0682 |
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+ | 0.0342 | 6.73 | 3000 | 0.1030 | 0.0661 |
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+ | 0.0329 | 6.95 | 3100 | 0.1021 | 0.0659 |
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+ | 0.0316 | 7.17 | 3200 | 0.1085 | 0.0667 |
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+ | 0.0275 | 7.4 | 3300 | 0.1089 | 0.0645 |
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+ | 0.0275 | 7.62 | 3400 | 0.1064 | 0.0645 |
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+ | 0.0268 | 7.85 | 3500 | 0.1109 | 0.0639 |
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+ | 0.0259 | 8.07 | 3600 | 0.1123 | 0.0636 |
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+ | 0.024 | 8.3 | 3700 | 0.1169 | 0.0631 |
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+ | 0.0225 | 8.52 | 3800 | 0.1170 | 0.0617 |
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+ | 0.0229 | 8.74 | 3900 | 0.1153 | 0.0614 |
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+ | 0.0214 | 8.97 | 4000 | 0.1143 | 0.0610 |
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+ | 0.02 | 9.19 | 4100 | 0.1162 | 0.0606 |
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+ | 0.0194 | 9.42 | 4200 | 0.1173 | 0.0603 |
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+ | 0.0193 | 9.64 | 4300 | 0.1184 | 0.0601 |
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+ | 0.0177 | 9.87 | 4400 | 0.1169 | 0.0597 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.11.3
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+ - Pytorch 1.10.0+cu113
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+ - Datasets 1.18.3
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+ - Tokenizers 0.10.3