Datasets:

SharedPL25
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parentlab

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Audio

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Mask must be a pyarrow.Array of type boolean
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1626, in _prepare_split_single
                  writer.write(example, key)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 538, in write
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 496, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 610, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 625, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2141, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 273, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3697, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              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/builder.py", line 1635, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 637, in finalize
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 496, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 610, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 625, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2141, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 273, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3697, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              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/parquet_and_info.py", line 1433, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 989, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1487, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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audio audio	label class label
	5Calming Moments with Kids
	5Calming Moments with Kids
	5Calming Moments with Kids
	5Calming Moments with Kids
	5Calming Moments with Kids
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	12Mindfulness Practices
	0Anxiety 101
	0Anxiety 101
	0Anxiety 101
	0Anxiety 101
	0Anxiety 101
	0Anxiety 101
	0Anxiety 101
	0Anxiety 101
	1Apparently So
	1Apparently So
	1Apparently So
	1Apparently So
	1Apparently So
	1Apparently So
	1Apparently So
	1Apparently So
	1Apparently So
	2Baby Sleep_ Myths & Methods
	2Baby Sleep_ Myths & Methods
	2Baby Sleep_ Myths & Methods
	2Baby Sleep_ Myths & Methods
	2Baby Sleep_ Myths & Methods
	2Baby Sleep_ Myths & Methods
	3Bedtime Stories
	3Bedtime Stories
	3Bedtime Stories
	3Bedtime Stories
	3Bedtime Stories
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	4Bullying_ Recognizing & Resolving
	6Cooking with Kids
	6Cooking with Kids
	6Cooking with Kids
	6Cooking with Kids
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats
	7Expert Chats

End of preview.

Parent Lab Open Source Content Repository

Overview

This is an open-source repository created by Parent Lab, a company dedicated to parenting education and founded by Jill Li, Bo Shao, and Zhen Shao, and led by CEO Joshua Iwata.

The goal of this project is to provide free access and distribution (geographically restricted in China) to high-quality parenting content crafted by industry-leading experts. The materials include podcasts, corresponding transcripts, meditations, courses, quizzes, and structured pathways to assist parents and caregivers in nurturing secure attachments with their children.

Content Types

The repository contains various types of expert-generated content:

Podcasts and corresponding transcripts Meditations Courses Quizzes Pathways (structured programs guiding users through specific parenting topics)

File Structure

Content is organized systematically to facilitate easy access and scalability. The structure is as follows:

assets/

├── podcasts/

│ ├── :podcastName/

│ │ ├── audio.mp3

│ │ └

── transcript.pdf

├── meditations/

│ ├── :meditationName/

│ │ ├── audio.mp3

│ │ └── transcript.pdf

├── courses/

│ ├── :courseName/

│ │ ├── lesson1.pdf

│ │ ├── lesson2.pdf

│ │ └── ...

├── quizzes/

│ ├── :quizName.pdf

├── pathways/

│ ├── :pathwayName.pdf

Replace :podcastName, :meditationName, etc., with the actual titles/names of your content.

Hosting & Access via Hugging Face

All content within this repository is hosted on Hugging Face Datasets, providing reliable storage and distribution. Interaction with the data should be performed following Hugging Face's official methods and guidelines.

Getting Started

Install the datasets library from Hugging Face:

python pip install datasets

Load and interact with data:

from datasets import load_dataset

Example loading podcast dataset

podcast_dataset = load_dataset("ParentLab/parenting-content", data_dir="assets/podcasts")

Example accessing transcripts for a specific podcast

transcript = podcast_dataset["train"][0]["transcript"]

audio_path = podcast_dataset["train"][0]["audio"]

For detailed documentation, refer to the Hugging Face Datasets documentation.

Usage & License

This content is made available freely and openly, subject to geographic restrictions in China. Users outside the restricted regions are encouraged to utilize and share this material freely, ensuring proper attribution to Parent Lab.

Please review the included LICENSE file for details regarding distribution and reuse permissions.

Downloads last month: 27

Total file size:

5.74 GB