readme: ---
  tags:
  - generated_from_keras_callback
  model-index:
  - name: wav2vec2-xls-r-300m-mixed
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information Keras had access to. You should
  probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-xls-r-300m-mixed
  
  Finetuned https://huggingface.co/facebook/wav2vec2-xls-r-300m on https://github.com/huseinzol05/malaya-speech/tree/master/data/mixed-stt
  
  **Update 2022-07-02, https://huggingface.co/mesolitica/wav2vec2-xls-r-300m-mixed slightly better accuracy**.
  
  This model was finetuned on 3 languages,
  
  1. Malay
  2. Singlish
  3. Mandarin
  
  **This model trained on a single Tesla V100 32GB VRAM, provided by https://keyreply.com/**. 
  
  ## Evaluation set
  
  Evaluation set from https://github.com/huseinzol05/malaya-speech/tree/master/pretrained-model/prepare-stt with sizes,
  
  ```
  len(malay), len(singlish), len(mandarin)
  -> (765, 3579, 614)
  ```
  
  It achieves the following results on the evaluation set based on [evaluate-wav2vec2-xls-r-300m-mixed.ipynb](evaluate-wav2vec2-xls-r-300m-mixed.ipynb):
  
  Mixed evaluation,
  
  ```
  CER: 0.048555454439612775
  WER: 0.14151468058308714
  CER with LM: 0.03977501945111893
  WER with LM: 0.09809135311921899
  ```
  
  Malay evaluation,
  
  ```
  CER: 0.05372605571018908
  WER: 0.23714922876687583
  CER with LM: 0.03508559320616622
  WER with LM: 0.1294898148329521
  ```
  
  Singlish evaluation,
  
  ```
  CER: 0.0488366183589853
  WER: 0.1294114484378467
  CER with LM: 0.04119293317615
  WER with LM: 0.09411106530063
  ```
  
  Mandarin evaluation,
  
  ```
  CER: 0.04047435404966954
  WER: 0.09291050873816364
  CER with LM: 0.037352703254831865
  WER with LM: 0.08217217867571727
  ```
  
  Language model from https://huggingface.co/huseinzol05/language-model-bahasa-manglish-combined