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PereLluis13
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wav2vec2-xls-r-300m-ca

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Catalan
wav2vec2
collectivat/tv3_parla
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_8_0
projecte-aina/parlament_parla
robust-speech-event
Eval Results
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metadata
language:
  - ca
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - collectivat/tv3_parla
  - projecte-aina/parlament_parla
  - generated_from_trainer
model-index:
  - name: wav2vec2-xls-r-300m-ca
    results: []

wav2vec2-xls-r-300m-ca

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2472
  • Wer: 0.1499

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: 7.5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 18.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
6.2099 0.09 500 3.4125 1.0
2.9961 0.18 1000 2.9224 1.0
2.2147 0.26 1500 0.6521 0.5568
1.3017 0.35 2000 0.3153 0.2761
1.1196 0.44 2500 0.2444 0.2367
1.0712 0.53 3000 0.2324 0.2132
1.052 0.62 3500 0.2173 0.2032
1.2813 2.13 4000 0.3326 0.2099
1.2365 2.4 4500 0.3224 0.2003
1.2193 2.66 5000 0.3198 0.1957
1.2072 2.93 5500 0.3063 0.1933
1.213 3.2 6000 0.3051 0.1980
1.2074 3.46 6500 0.3012 0.1879
1.1918 3.73 7000 0.2947 0.1829
1.1893 4.0 7500 0.2895 0.1807
1.1751 4.26 8000 0.2878 0.1776
1.1628 4.53 8500 0.2835 0.1731
1.1577 4.79 9000 0.2816 0.1761
1.1448 5.06 9500 0.2757 0.1740
1.1407 5.33 10000 0.2768 0.1798
1.1401 5.59 10500 0.2780 0.1816
1.1333 5.86 11000 0.2748 0.1750
1.1571 6.13 11500 0.2808 0.1708
1.1505 6.39 12000 0.2726 0.1692
1.1519 6.66 12500 0.2749 0.1654
1.136 6.93 13000 0.2765 0.1643
1.1326 7.19 13500 0.2706 0.1668
1.1342 7.46 14000 0.2665 0.1638
1.1286 7.72 14500 0.2669 0.1636
1.1243 7.99 15000 0.2619 0.1623
1.1173 8.26 15500 0.2652 0.1604
1.1129 8.52 16000 0.2610 0.1598
1.1091 8.79 16500 0.2608 0.1584
1.1053 9.06 17000 0.2633 0.1664
1.1004 9.32 17500 0.2594 0.1662
1.0995 9.59 18000 0.2623 0.1569
1.0964 9.86 18500 0.2624 0.1597
1.09 10.12 19000 0.2577 0.1578
1.089 10.39 19500 0.2574 0.1531
1.0864 10.66 20000 0.2556 0.1546
1.0806 10.92 20500 0.2548 0.1583
1.0842 11.19 21000 0.2550 0.1542
1.0805 11.45 21500 0.2561 0.1524
1.0722 11.72 22000 0.2540 0.1566
1.0763 11.99 22500 0.2549 0.1572
1.0835 12.25 23000 0.2586 0.1521
1.0883 12.52 23500 0.2583 0.1519
1.0888 12.79 24000 0.2551 0.1582
1.0933 13.05 24500 0.2628 0.1537
1.0799 13.32 25000 0.2600 0.1508
1.0804 13.59 25500 0.2620 0.1475
1.0814 13.85 26000 0.2537 0.1517
1.0693 14.12 26500 0.2560 0.1542
1.0724 14.38 27000 0.2540 0.1574
1.0704 14.65 27500 0.2548 0.1626
1.0729 14.92 28000 0.2548 0.1601
1.0724 15.18 28500 0.2511 0.1512
1.0655 15.45 29000 0.2498 0.1490
1.0608 15.98 30000 0.2487 0.1481
1.0541 16.52 31000 0.2468 0.1504
1.0584 17.05 32000 0.2467 0.1493
1.0507 17.58 33000 0.2481 0.1517

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0