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wav2vec2-bloom-speech-jra

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Model description

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - JRA (Jarai) dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5600
  • Wer: 0.1212
  • Cer: 0.0473

Users should refer to the original model for tutorials on using a trained model for inference.

Intended uses & limitations

Users of this model must abide by the SIL RAIL-M License.

This model is created as a proof of concept and no guarantees are made regarding the performance of the model is specific situations.

Training and evaluation data

Training, Validation, and Test datasets were generated from the same corpus, ensuring that no duplicate files were used.

Training procedure

Standard finetuning of XLS-R was used based on the examples in the Hugging Face Transformers Github

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • 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: 250
  • num_epochs: 1000.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
No log 41.62 250 0.5465 0.3473 0.1059
2.1292 83.31 500 0.5108 0.2214 0.0731
2.1292 124.92 750 0.5218 0.1841 0.0629
0.0524 166.62 1000 0.4192 0.1550 0.0519
0.0524 208.31 1250 0.4669 0.1655 0.0529
0.0242 249.92 1500 0.5480 0.1667 0.0552
0.0242 291.62 1750 0.4986 0.1352 0.0496
0.0164 333.31 2000 0.5084 0.1364 0.0499
0.0164 374.92 2250 0.5017 0.1422 0.0527
0.0104 416.62 2500 0.4788 0.1282 0.0465
0.0104 458.31 2750 0.5268 0.1457 0.0519
0.0066 499.92 3000 0.5600 0.1212 0.0473
0.0066 541.62 3250 0.6352 0.1305 0.0476
0.0054 583.31 3500 0.6117 0.1224 0.0463
0.0054 624.92 3750 0.5915 0.1340 0.0476

Framework versions

  • Transformers 4.21.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 2.2.2
  • Tokenizers 0.12.1
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Evaluation results