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wav2vec2-xls-r-300m-mixed

Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
wav2vec2
generated_from_keras_callback
Inference Endpoints
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huseinzol05 commited on
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add model

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README.md CHANGED
@@ -11,61 +11,37 @@ probably proofread and complete it, then remove this comment. -->
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  # wav2vec2-xls-r-300m-mixed
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- Finetuned https://huggingface.co/facebook/wav2vec2-xls-r-300m on https://github.com/huseinzol05/malaya-speech/tree/master/data/mixed-stt
 
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- This model was finetuned on 3 languages,
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- 1. Malay
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- 2. Singlish
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- 3. Mandarin
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- **This model trained on a single RTX 3090 Ti 24GB VRAM, provided by https://mesolitica.com/**.
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- ## Evaluation set
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- Evaluation set from https://github.com/huseinzol05/malaya-speech/tree/master/pretrained-model/prepare-stt with sizes,
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- ```
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- len(malay), len(singlish), len(mandarin)
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- -> (765, 3579, 614)
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- ```
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- 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):
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- Mixed evaluation,
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- ```
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- CER: 0.0481054244857041
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- WER: 0.1322198446007387
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- CER with LM: 0.041196586938584696
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- WER with LM: 0.09880169127621556
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- ```
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- Malay evaluation,
 
 
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- ```
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- CER: 0.051636391937588406
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- WER: 0.19561999547293663
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- CER with LM: 0.03917689630621449
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- WER with LM: 0.12710746406824835
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- ```
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- Singlish evaluation,
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- ```
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- CER: 0.0494915200071987
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- WER: 0.12763802881676573
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- CER with LM: 0.04271234986432335
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- WER with LM: 0.09677160640413336
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- ```
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- Mandarin evaluation,
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- ```
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- CER: 0.035626554824269824
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- WER: 0.07993515937860181
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- CER with LM: 0.03487760945087219
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- WER with LM: 0.07536807168546154
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- ```
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-
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- Language model from https://huggingface.co/huseinzol05/language-model-bahasa-manglish-combined
 
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  # wav2vec2-xls-r-300m-mixed
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+ This model was trained from scratch on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ ## Model description
 
 
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+ More information needed
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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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+ - optimizer: None
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+ - training_precision: float32
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+ ### Training results
 
 
 
 
 
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+ ### Framework versions
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+ - Transformers 4.18.0
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+ - TensorFlow 2.6.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.12.1
 
 
 
 
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