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wav2vec2-xls-r-300m-hsb-cv8

Automatic Speech Recognition
Transformers
PyTorch
Upper Sorbian
wav2vec2
mozilla-foundation/common_voice_8_0
generated_from_trainer
robust-speech-event
xlsr-fine-tuning-week
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Community
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metadata
language:
  - hsb
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
  - xlsr-fine-tuning-week
  - hf-asr-leaderboard
datasets:
  - common_voice
model-index:
  - name: Upper Sorbian comodoro Wav2Vec2 XLSR 300M CV8
    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: 56.3
          - name: Test CER
            type: cer
            value: 14.3

Upper Sorbian wav2vec2-xls-r-300m-hsb-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: 0.9643
  • Wer: 0.5037
  • Cer: 0.1278

Evaluation

The model can be evaluated using the attached eval.py script:

python eval.py --model_id comodoro/wav2vec2-xls-r-300m-hsb-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config hsb

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • num_epochs: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
4.3121 19.35 1200 3.2059 1.0 1.0
2.6525 38.71 2400 1.1324 0.9387 0.3204
1.3644 58.06 3600 0.8767 0.8099 0.2271
1.093 77.42 4800 0.8739 0.7603 0.2090
0.9546 96.77 6000 0.8454 0.6983 0.1882
0.8554 116.13 7200 0.8197 0.6484 0.1708
0.775 135.48 8400 0.8452 0.6345 0.1681
0.7167 154.84 9600 0.8551 0.6241 0.1631
0.6609 174.19 10800 0.8442 0.5821 0.1531
0.616 193.55 12000 0.8892 0.5864 0.1527
0.5815 212.9 13200 0.8839 0.5772 0.1503
0.55 232.26 14400 0.8905 0.5665 0.1436
0.5173 251.61 15600 0.8995 0.5471 0.1417
0.4969 270.97 16800 0.8633 0.5325 0.1334
0.4803 290.32 18000 0.9074 0.5253 0.1352
0.4596 309.68 19200 0.9159 0.5146 0.1294
0.4415 329.03 20400 0.9055 0.5189 0.1314
0.434 348.39 21600 0.9435 0.5208 0.1314
0.4199 367.74 22800 0.9199 0.5136 0.1290
0.4008 387.1 24000 0.9342 0.5174 0.1303
0.4051 406.45 25200 0.9436 0.5132 0.1292
0.3861 425.81 26400 0.9417 0.5084 0.1283
0.3738 445.16 27600 0.9573 0.5079 0.1299
0.3768 464.52 28800 0.9682 0.5062 0.1289
0.3647 483.87 30000 0.9643 0.5037 0.1278

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0