Lemswasabi/wav2vec2-base-luxembourgish-4h

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

Model description

We pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech collected from RTL.lu. Then the model was fine-tuned on 4h of labelled Luxembourgish Speech from the same domain.

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: 7.5e-05
• —trainbatchsize: 3
• —evalbatchsize: 3
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 12
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 2000
• —num_epochs: 50.0
• —mixedprecisiontraining: Native AMP

Framework versions

• —Transformers 4.20.0.dev0
• —Pytorch 1.11.0+cu113
• —Datasets 2.2.1
• —Tokenizers 0.12.1

Citation

This model is a result of our paper IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS submitted to the IEEE SLT 2022 workshop

@misc{lb-wav2vec2,
  author = {Nguyen, Le Minh and Nayak, Shekhar and Coler, Matt.},
  keywords = {Luxembourgish, multilingual speech recognition, language modelling, wav2vec 2.0 XLSR-53, under-resourced language},
  title = {IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS},
  year = {2022},
  copyright = {2023 IEEE}
}