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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 Inference Endpoints
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metadata
language:
  - ca
license: apache-2.0
tags:
  - automatic-speech-recognition
  - collectivat/tv3_parla
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - projecte-aina/parlament_parla
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
  - collectivat/tv3_parla
  - projecte-aina/parlament_parla
model-index:
  - name: wav2vec2-xls-r-300m-ca
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_8_0 ca
          type: mozilla-foundation/common_voice_8_0
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 13.170091241317552
          - name: Test CER
            type: cer
            value: 3.356726205534543
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: projecte-aina/parlament_parla ca
          type: projecte-aina/parlament_parla
          args: clean
        metrics:
          - name: Test WER
            type: wer
            value: 8.048005647723262
          - name: Test CER
            type: cer
            value: 2.240912911020065
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: collectivat/tv3_parla ca
          type: collectivat/tv3_parla
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 23.320629787889285
          - name: Test CER
            type: cer
            value: 10.43921620208999
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: speech-recognition-community-v2/dev_data ca
          type: speech-recognition-community-v2/dev_data
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 31.99671115046487
          - name: Test CER
            type: cer
            value: 15.820020687277324
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 22.04

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, the tv3_parla and parlament_parla datasets. It achieves the following results on the evaluation set (for the three datasets):

  • Loss: 0.2472
  • Wer: 0.1499

Model description

Please check the original facebook/wav2vec2-xls-r-1b Model card. This is just a finetuned version of that model.

Intended uses & limitations

As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language.

Training and evaluation data

More information needed

Training procedure

The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by @ccoreilly, which can be found on the text/ folder or here.

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

Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training.

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

Thanks

Want to thank both @ccoreilly and @gullabi who have contributed with their own resources and knowledge into making this model possible.