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It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This model is uncased. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models This model was pre-trained with 522MB of indonesian Wikipedia and 1GB of indonesian newspapers. The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form:

Fill-mask
Input Text→ Output Text
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Fine-tuned facebook/wav2vec2-large-xlsr-53 on the Indonesian Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be evaluated as follows on the Indonesian test data of Common Voice. The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO

Speech recognition
Input Audio→ Output Text
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