datdo2717/whisper-small-ori
Automatic Speech Recognition
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Exception: SplitsNotFoundError Message: The split names could not be parsed from the dataset config. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 159, in compute compute_split_names_from_info_response( File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 131, in compute_split_names_from_info_response config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config) File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 567, in get_previous_step_or_raise raise CachedArtifactError( libcommon.simple_cache.CachedArtifactError: The previous step failed. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info for split_generator in builder._split_generators( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators raise ValueError( ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 75, in compute_split_names_from_streaming_response for split in get_dataset_split_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names info = get_dataset_config_info( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.
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The LRS2 dataset consists of thousands of BBC video clips, divided into Train, Validation, and Test folders. We used the same dataset consistent with previous works (Li et al., 2022; Gao & Grauman, 2021; Lee et al., 2021), created by randomly selecting two different speakers from LRS2 and mixing their speeches with signal-to-noise ratios between -5dB and 5dB. Since the LRS2 data contains reverberation and noise, and the overlap rate is not 100%, the dataset is closer to real-world scenarios. We use the same data split containing 11-hour training, 3-hour validation, and 1.5-hour test sets.