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---
tags:
- automatic-speech-recognition
- generated_from_trainer
language:
- lb
metrics:
- wer
pipeline_tag: automatic-speech-recognition
license: mit
model-index:
- name: Lemswasabi/wav2vec2-large-xlsr-53-842h-luxembourgish-14h-with-lm
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Speech Recognition # Optional. Example: Speech Recognition
metrics:
- type: wer
value: 11.68
name: Dev WER
- type: wer
value: 10.71
name: Test WER
- type: cer
value: 2.64
name: Dev CER
- type: cer
value: 2.31
name: Test CER
---
<!-- 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 fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 14h 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
- 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](https://slt2022.org/)
```
@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}
}
``` |