Tahsin-Mayeesha
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README.md
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datasets:
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- openslr
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- SLR53
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metrics:
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- wer
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- cer
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- type: cer
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value: 0.07263099973420006
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name: Test CER
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---
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
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It achieves the following results on the evaluation set
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- Wer: 0.3110
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- Cer : 0.072
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Note : 10% of a total 218703 samples have been used for evaluation. Evaluation set has 21871 examples. Training was stopped after 30k steps. Output predictions are available under files section.
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### Training hyperparameters
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datasets:
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- openslr
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- SLR53
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- Harveenchadha/indic-text
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metrics:
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- wer
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- cer
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- type: cer
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value: 0.07263099973420006
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name: Test CER
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- type: wer # Required. Example: wer
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value: 0.17776164652632478 # Required. Example: 20.90
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name: Test WER with lm # Optional. Example: Test WER
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- type: cer
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value: 0.04394092712884769
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name: Test CER with lm
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---
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
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It achieves the following results on the evaluation set.
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Without language model :
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- Wer: 0.3110
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- Cer : 0.072
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With 5 gram language model trained on [indic-text](https://huggingface.co/datasets/Harveenchadha/indic-text/tree/main) dataset :
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- Wer: 0.17776
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- Cer : 0.04394
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Note : 10% of a total 218703 samples have been used for evaluation. Evaluation set has 21871 examples. Training was stopped after 30k steps. Output predictions are available under files section.
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### Training hyperparameters
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