sil-ai / wav2vec2-bloom-speech-mya

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Total runs: 10
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Model's Last Updated: October 21 2022
automatic-speech-recognition

Introduction of wav2vec2-bloom-speech-mya

Model Details of wav2vec2-bloom-speech-mya

wav2vec2-bloom-speech-mya

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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 - MYA (Burmese) dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7582
  • Wer: 0.8736
  • Cer: 0.1082

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 27.74 250 2.1563 1.1336 0.5756
2.5628 55.53 500 0.6279 0.9386 0.1224
2.5628 83.32 750 0.6446 0.9783 0.1163
0.0714 111.11 1000 0.7582 0.8736 0.1082
0.0714 138.84 1250 0.7555 0.9314 0.1215
0.0341 166.63 1500 0.8160 0.9170 0.1208
0.0341 194.42 1750 0.7230 0.9603 0.1238
Framework versions
  • Transformers 4.21.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 2.2.2
  • Tokenizers 0.12.1

Runs of sil-ai wav2vec2-bloom-speech-mya on huggingface.co

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