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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: 0.2549
  • Wer: 0.1573

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: 12.0
  • 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

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.18.1
  • Tokenizers 0.11.0