sil-ai / wav2vec2-bloom-speech-quc

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Total runs: 3
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7-day runs: 0
30-day runs: 2
Model's Last Updated: October 21 2022
automatic-speech-recognition

Introduction of wav2vec2-bloom-speech-quc

Model Details of wav2vec2-bloom-speech-quc

wav2vec2-bloom-speech-quc

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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 - QUC (K’iche’) dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7370
  • Wer: 0.3508
  • Cer: 0.1010

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 5.55 250 2.9144 1.0 1.0
4.7167 11.11 500 0.9985 0.8459 0.2448
4.7167 16.66 750 0.6897 0.4787 0.1236
0.482 22.22 1000 0.6056 0.3869 0.0998
0.482 27.77 1250 0.6202 0.3770 0.1040
0.2359 33.33 1500 0.6799 0.4 0.1052
0.2359 38.88 1750 0.7070 0.3574 0.1004
0.1668 44.44 2000 0.7370 0.3508 0.1010
0.1668 49.99 2250 0.7869 0.3803 0.1070
0.1334 55.55 2500 0.7652 0.3803 0.1034
0.1334 61.11 2750 0.7867 0.3574 0.1022
Framework versions
  • Transformers 4.21.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 2.2.2
  • Tokenizers 0.12.1

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