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wav2vec2-xls-r-300m-spanish-large-LM

Automatic Speech Recognition
Transformers
PyTorch
Safetensors
wav2vec2
generated_from_trainer
es
robust-speech-event
Inference Endpoints
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wav2vec2-xls-r-300m-spanish-large-LM / README.md
Jhon Parra
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - es
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-spanish-large
    results: []

wav2vec2-large-xls-r-300m-spanish-large

This model is a fine-tuned version of tomascufaro/xls-r-es-test on the common_voice dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1431
  • Wer: 0.1197

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: 0.0002
  • train_batch_size: 10
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 20
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 300
  • num_epochs: 5
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.1769 0.15 400 0.1795 0.1698
0.217 0.3 800 0.2000 0.1945
0.2372 0.45 1200 0.1985 0.1859
0.2351 0.6 1600 0.1901 0.1772
0.2269 0.75 2000 0.1968 0.1783
0.2284 0.9 2400 0.1873 0.1771
0.2014 1.06 2800 0.1840 0.1696
0.1988 1.21 3200 0.1904 0.1730
0.1919 1.36 3600 0.1827 0.1630
0.1919 1.51 4000 0.1788 0.1629
0.1817 1.66 4400 0.1755 0.1558
0.1812 1.81 4800 0.1795 0.1638
0.1808 1.96 5200 0.1762 0.1603
0.1625 2.11 5600 0.1721 0.1557
0.1477 2.26 6000 0.1735 0.1504
0.1508 2.41 6400 0.1708 0.1478
0.157 2.56 6800 0.1644 0.1466
0.1491 2.71 7200 0.1638 0.1445
0.1458 2.86 7600 0.1582 0.1426
0.1387 3.02 8000 0.1607 0.1376
0.1269 3.17 8400 0.1559 0.1364
0.1172 3.32 8800 0.1521 0.1335
0.1203 3.47 9200 0.1534 0.1330
0.1177 3.62 9600 0.1485 0.1304
0.1167 3.77 10000 0.1498 0.1302
0.1194 3.92 10400 0.1463 0.1287
0.1053 4.07 10800 0.1483 0.1282
0.098 4.22 11200 0.1498 0.1267
0.0958 4.37 11600 0.1461 0.1233
0.0946 4.52 12000 0.1444 0.1218
0.094 4.67 12400 0.1434 0.1206
0.0932 4.82 12800 0.1424 0.1206
0.0912 4.98 13200 0.1431 0.1197

Framework versions

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