Jzuluaga
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wav2vec2-xls-r-300m-en-atc-uwb-atcc

Automatic Speech Recognition

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Inference Endpoints

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Jzuluaga commited on Nov 30, 2022

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@@ -87,7 +87,7 @@ See Table 1 (page 3) in our paper: [How Does Pre-trained Wav2Vec 2.0 Perform on
 - We use the UWB-ATCC corpus to fine-tune this model. You can download the raw data here: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0
 - However, do not worry, we have prepared the database in `Datasets format`. Here, [UWB-ATCC corpus on HuggingFace](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). You can scroll and check the train/test partitions, and even listen to some audios.
 - If you want to prepare a database in HuggingFace format, you can follow the data loader script in: [data_loader_atc.py](https://huggingface.co/datasets/Jzuluaga/uwb_atcc/blob/main/atc_data_loader.py).
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 ## Writing your own inference script
 If you use language model, you need to install the KenLM bindings with:

 - We use the UWB-ATCC corpus to fine-tune this model. You can download the raw data here: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0
 - However, do not worry, we have prepared the database in `Datasets format`. Here, [UWB-ATCC corpus on HuggingFace](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). You can scroll and check the train/test partitions, and even listen to some audios.
 - If you want to prepare a database in HuggingFace format, you can follow the data loader script in: [data_loader_atc.py](https://huggingface.co/datasets/Jzuluaga/uwb_atcc/blob/main/atc_data_loader.py).
 ## Writing your own inference script
 If you use language model, you need to install the KenLM bindings with: