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Lemswasabi/
wav2vec2-base-luxembourgish-4h-with-lm

Name: wav2vec2-base-luxembourgish-4h-with-lm
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

generated_from_trainer

mit

model-index

endpoints_compatible

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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. Additionally, we rescore the output transcription with a 5-gram language model trained on text corpora from RTL.lu and the Luxembourgish parliament.

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
train_batch_size: 3
eval_batch_size: 3
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 12
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2000
num_epochs: 50.0
mixed_precision_training: 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}
}

Lemswasabi/wav2vec2-base-luxembourgish-4h-with-lm