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wav2vec2-xls-r-sl-a1

Automatic Speech Recognition Transformers PyTorch
Slovenian wav2vec2 generated_from_trainer hf-asr-leaderboard model_for_talk mozilla-foundation/common_voice_8_0 robust-speech-event Eval Results Inference Endpoints
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metadata
language:
  - sl
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  - sl
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-xls-r-sl-a1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: sl
        metrics:
          - name: Test WER
            type: wer
            value: 0.20626555409164105
          - name: Test CER
            type: cer
            value: 0.051648321634392154
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: sl
        metrics:
          - name: Test WER
            type: wer
            value: 0.5406156320830592
          - name: Test CER
            type: cer
            value: 0.22249723590310583
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: sl
        metrics:
          - name: Test WER
            type: wer
            value: 55.24

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

  • Loss: 0.2756
  • Wer: 0.2279

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-sl-a1 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs

  1. To evaluate on speech-recognition-community-v2/dev_data

python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-sl-a1 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 7.1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • 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: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.3881 6.1 500 2.9710 1.0
2.6401 12.2 1000 1.7677 0.9734
1.5152 18.29 1500 0.5564 0.6011
1.2191 24.39 2000 0.4319 0.4390
1.0237 30.49 2500 0.3141 0.3175
0.8892 36.59 3000 0.2748 0.2689
0.8296 42.68 3500 0.2680 0.2534
0.7602 48.78 4000 0.2820 0.2506
0.7186 54.88 4500 0.2672 0.2398
0.6887 60.98 5000 0.2729 0.2402
0.6507 67.07 5500 0.2767 0.2361
0.6226 73.17 6000 0.2817 0.2332
0.6024 79.27 6500 0.2679 0.2279
0.5787 85.37 7000 0.2837 0.2316
0.5744 91.46 7500 0.2838 0.2284
0.5556 97.56 8000 0.2763 0.2281

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0