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wav2vec2-xls-r-300m-ab-CV8

Automatic Speech Recognition Transformers PyTorch TensorBoard
Abkhaz wav2vec2 generated_from_trainer hf-asr-leaderboard robust-speech-event Eval Results Inference Endpoints
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metadata
license: apache-2.0
language: ab
tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-300m-ab-CV8
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: ab
        metrics:
          - name: Test WER
            type: wer
            value: 44.9

wav2vec2-xls-r-300m-ab-CV8

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: 0.2105
  • Wer: 0.5474

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: 0.0001
  • 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: 300
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.7729 0.63 500 3.0624 1.0021
2.7348 1.26 1000 1.0460 0.9815
1.2756 1.9 1500 0.4618 0.8309
1.0419 2.53 2000 0.3725 0.7449
0.9491 3.16 2500 0.3368 0.7345
0.9006 3.79 3000 0.3014 0.6936
0.8519 4.42 3500 0.2852 0.6767
0.8243 5.06 4000 0.2701 0.6504
0.7902 5.69 4500 0.2641 0.6221
0.7767 6.32 5000 0.2549 0.6192
0.7516 6.95 5500 0.2515 0.6179
0.737 7.59 6000 0.2408 0.5963
0.7217 8.22 6500 0.2429 0.6261
0.7101 8.85 7000 0.2366 0.5687
0.6922 9.48 7500 0.2277 0.5680
0.6866 10.11 8000 0.2242 0.5847
0.6703 10.75 8500 0.2222 0.5803
0.6649 11.38 9000 0.2247 0.5765
0.6513 12.01 9500 0.2182 0.5644
0.6369 12.64 10000 0.2128 0.5508
0.6425 13.27 10500 0.2132 0.5514
0.6399 13.91 11000 0.2116 0.5495
0.6208 14.54 11500 0.2105 0.5474

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.1
  • Tokenizers 0.10.3