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--- |
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tags: |
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- automatic-speech-recognition |
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- generated_from_trainer |
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license: mit |
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language: |
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- lb |
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metrics: |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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model-index: |
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- name: Lemswasabi/wav2vec2-base-luxembourgish-4h |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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metrics: |
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- type: wer |
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value: 23.95 |
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name: Dev WER |
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- type: wer |
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value: 23.09 |
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name: Test WER |
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- type: cer |
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value: 7.97 |
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name: Dev CER |
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- type: cer |
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value: 7.63 |
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name: Test CER |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# |
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## Model description |
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We pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech |
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collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 4h of labelled |
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Luxembourgish Speech from the same domain. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7.5e-05 |
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- train_batch_size: 3 |
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- eval_batch_size: 3 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 12 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2000 |
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- num_epochs: 50.0 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.20.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.1 |
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- Tokenizers 0.12.1 |
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## Citation |
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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/) |
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``` |
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@misc{lb-wav2vec2, |
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author = {Nguyen, Le Minh and Nayak, Shekhar and Coler, Matt.}, |
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keywords = {Luxembourgish, multilingual speech recognition, language modelling, wav2vec 2.0 XLSR-53, under-resourced language}, |
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title = {IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS}, |
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year = {2022}, |
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copyright = {2023 IEEE} |
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} |
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``` |
