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wav2vec2-timit-xls-r-53-wandb-colab

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
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wav2vec2-timit-xls-r-53-wandb-colab / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-timit-xls-r-53-wandb-colab
    results: []

wav2vec2-timit-xls-r-53-wandb-colab

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2451
  • Wer: 0.2503
  • Cer: 0.0799

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Wer Cer
No log 0.69 400 3.1514 1.0 0.9790
5.094 1.38 800 2.8674 1.0 0.9790
2.595 2.08 1200 0.5208 0.5344 0.1541
0.8139 2.77 1600 0.3568 0.4234 0.1275
0.4793 3.46 2000 0.2954 0.3645 0.1106
0.4793 4.15 2400 0.2649 0.3475 0.1037
0.3771 4.84 2800 0.2452 0.3186 0.0976
0.2912 5.54 3200 0.2385 0.3079 0.0960
0.2632 6.23 3600 0.2292 0.2954 0.0911
0.2176 6.92 4000 0.2248 0.2910 0.0908
0.2176 7.61 4400 0.2279 0.2816 0.0888
0.1958 8.3 4800 0.2227 0.2819 0.0878
0.1846 9.0 5200 0.2277 0.2779 0.0876
0.1573 9.69 5600 0.2280 0.2830 0.0877
0.1471 10.38 6000 0.2345 0.2770 0.0880
0.1471 11.07 6400 0.2389 0.2714 0.0852
0.133 11.76 6800 0.2253 0.2730 0.0869
0.1317 12.46 7200 0.2179 0.2662 0.0846
0.1268 13.15 7600 0.2315 0.2678 0.0851
0.1147 13.84 8000 0.2501 0.2679 0.0849
0.1147 14.53 8400 0.2463 0.2663 0.0839
0.1151 15.22 8800 0.2429 0.2662 0.0848
0.0968 15.92 9200 0.2502 0.2639 0.0839
0.0985 16.61 9600 0.2589 0.2616 0.0838
0.0938 17.3 10000 0.2414 0.2595 0.0835
0.0938 17.99 10400 0.2420 0.2617 0.0839
0.0878 18.69 10800 0.2257 0.2597 0.0829
0.0872 19.38 11200 0.2654 0.2586 0.0825
0.0774 20.07 11600 0.2558 0.2579 0.0829
0.0743 20.76 12000 0.2375 0.2564 0.0824
0.0743 21.45 12400 0.2522 0.2568 0.0813
0.0832 22.15 12800 0.2363 0.2569 0.0817
0.0698 22.84 13200 0.2510 0.2574 0.0816
0.0677 23.53 13600 0.2535 0.2570 0.0818
0.0648 24.22 14000 0.2595 0.2571 0.0819
0.0648 24.91 14400 0.2441 0.2542 0.0815
0.0685 25.61 14800 0.2503 0.2534 0.0803
0.066 26.3 15200 0.2489 0.2533 0.0804
0.0583 26.99 15600 0.2471 0.2512 0.0802
0.0624 27.68 16000 0.2487 0.2516 0.0804
0.0624 28.37 16400 0.2470 0.2518 0.0804
0.0663 29.07 16800 0.2478 0.2510 0.0800
0.0569 29.76 17200 0.2451 0.2503 0.0799

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 1.18.3
  • Tokenizers 0.13.3