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Model: mesolitica/wav2vec2-xls-r-300m-mixed
Source: Original Platform

README.md

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+---
+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
+
+This model was finetuned on 3 languages,
+
+1. Malay
+2. Singlish
+3. Mandarin
+
+**This model trained on a single RTX 3090 Ti 24GB VRAM, provided by https://mesolitica.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-gpu.ipynb](evaluate-gpu.ipynb):
+
+Mixed evaluation,
+
+```
+CER: 0.0481054244857041
+WER: 0.1322198446007387
+CER with LM: 0.041196586938584696
+WER with LM: 0.09880169127621556
+```
+
+Malay evaluation,
+
+```
+CER: 0.051636391937588406
+WER: 0.19561999547293663
+CER with LM: 0.03917689630621449
+WER with LM: 0.12710746406824835
+```
+
+Singlish evaluation,
+
+```
+CER: 0.0494915200071987
+WER: 0.12763802881676573
+CER with LM: 0.04271234986432335
+WER with LM: 0.09677160640413336
+```
+
+Mandarin evaluation,
+
+```
+CER: 0.035626554824269824
+WER: 0.07993515937860181
+CER with LM: 0.03487760945087219
+WER with LM: 0.07536807168546154
+```
+
+Language model from https://huggingface.co/huseinzol05/language-model-bahasa-manglish-combined