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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
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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 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

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
  • 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