readme: ---
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  license: mit
  language:
  - lb
  metrics:
  - wer
  pipeline_tag: automatic-speech-recognition
  
  model-index:
  - name: Lemswasabi/wav2vec2-base-luxembourgish-4h-with-lm
    results:
    - task:
        type: automatic-speech-recognition             # Required. Example: automatic-speech-recognition
        name: Speech Recognition             # Optional. Example: Speech Recognition
      metrics:
        - type: wer
          value: 20.10
          name: Dev WER
        - type: wer 
          value: 18.67
          name: Test WER
        - type: cer
          value: 7.41
          name: Dev CER
        - type: cer 
          value: 6.76
          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 pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech
  collected from [RTL.lu](https://www.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](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}
  }
  ```