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metadata
language:
  - hsb
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - hsb
  - robust-speech-event
  - model_for_talk
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-hsb-v2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: hsb
        metrics:
          - name: Test WER
            type: wer
            value: []
          - name: Test CER
            type: cer
            value: []

wav2vec2-large-xls-r-300m-hsb-v2

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

  • Loss: 0.5328
  • Wer: 0.4596

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: 0.00045
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
8.5979 3.23 100 3.5602 1.0
3.303 6.45 200 3.2238 1.0
3.2034 9.68 300 3.2002 0.9888
2.7986 12.9 400 1.2408 0.9210
1.3869 16.13 500 0.7973 0.7462
1.0228 19.35 600 0.6722 0.6788
0.8311 22.58 700 0.6100 0.6150
0.717 25.81 800 0.6236 0.6013
0.6264 29.03 900 0.6031 0.5575
0.5494 32.26 1000 0.5656 0.5309
0.4781 35.48 1100 0.5289 0.4996
0.4311 38.71 1200 0.5375 0.4768
0.3902 41.94 1300 0.5246 0.4703
0.3508 45.16 1400 0.5382 0.4696
0.3199 48.39 1500 0.5328 0.4596

Framework versions

  • Transformers 4.16.1
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.2
  • Tokenizers 0.11.0