Open-ASQA-Speech for R1-A

Now support for:

• LibriTTS
• MOSEI

Dataset Usage

MOSEI

You can assess the data with datasets/affect/get_data.py from https://github.com/pliang279/MultiBench, which will return [vision, audio, text, ind, label].

# Example code
traindata, validdata, test_robust = get_dataloader('./mosei_raw.pkl', data_type='mosei')

LibriTTS

LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate.

There are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):

• dev.clean dev.other
• test.clean test.other
• train.clean.100 train.clean.360 train.other.500

** Configurations ** The default configuration is "all".

• "dev": only the "dev.clean" split (good for testing the dataset quickly)
• "clean": contains only "clean" splits
• "other": contains only "other" splits
• "all": contains only "all" splits

# Example code
load_dataset("blabble-io/libritts", "clean", split="train.clean.100")