training/run_parler_tts_training.py [584:600]:
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        if description_column_name is not None and data_args.max_description_token_length is not None:
            with accelerator.local_main_process_first():
                # filter description that is shorter than max_text_length
                vectorized_datasets = vectorized_datasets.filter(
                    lambda x: len(x) < data_args.max_description_token_length,
                    num_proc=num_workers,
                    input_columns=["input_ids"],
                )

        if data_args.max_prompt_token_length is not None:
            with accelerator.local_main_process_first():
                # filter description that is shorter than max_text_length
                vectorized_datasets = vectorized_datasets.filter(
                    lambda x: len(x) < data_args.max_prompt_token_length,
                    num_proc=num_workers,
                    input_columns=["prompt_input_ids"],
                )
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training/run_parler_tts_training.py [622:638]:
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    if description_column_name is not None and data_args.max_description_token_length is not None:
        with accelerator.local_main_process_first():
            # filter description that is shorter than max_text_length
            vectorized_datasets = vectorized_datasets.filter(
                lambda x: len(x) < data_args.max_description_token_length,
                num_proc=num_workers,
                input_columns=["input_ids"],
            )

    if data_args.max_prompt_token_length is not None:
        with accelerator.local_main_process_first():
            # filter description that is shorter than max_text_length
            vectorized_datasets = vectorized_datasets.filter(
                lambda x: len(x) < data_args.max_prompt_token_length,
                num_proc=num_workers,
                input_columns=["prompt_input_ids"],
            )
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