Datasets:
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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1539, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1419, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 976, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 794, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
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 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1401, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1579, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
mp3 audio | json dict | __key__ string | __url__ string |
|---|---|---|---|
{
"key": "af63b5af90cf7e12cec7e58ec5b5088519a8531ea9f43e53fb338c733a77ac99",
"url": "https://lcdn.letscast.fm/media/podcast/e491ed26/episode/902de2a3.mp3?t=1756021014",
"source": {
"id": "de_kicast_selected",
"name": "Der KI CAST",
"languages_hint": "de (RSS channel and item metadata; speech not indep... | af63b5af90cf7e12cec7e58ec5b5088519a8531ea9f43e53fb338c733a77ac99 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-extra-00000.tar | |
{
"key": "1507789d878a847b73b99a0f24ed5bbc954d701321c6c8741c4230e909de966f",
"url": "https://mcdn.podbean.com/mf/web/can9imrvn5izcxk5/sydamerika.mp3",
"source": {
"id": "es_ailotsen_selected",
"name": "AI-Lotsen Spanish editions",
"languages_hint": "es (item metadata; speech not independently verified... | 1507789d878a847b73b99a0f24ed5bbc954d701321c6c8741c4230e909de966f | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-extra-00000.tar | |
{
"key": "61c65b4b083f581de8fec523ed1061f7cfb21d62f7fbf991d536bfca430adc3a",
"url": "https://lcdn.letscast.fm/media/podcast/e491ed26/episode/caf8d6de.mp3?t=1755971313",
"source": {
"id": "de_kicast_selected",
"name": "Der KI CAST",
"languages_hint": "de (RSS channel and item metadata; speech not indep... | 61c65b4b083f581de8fec523ed1061f7cfb21d62f7fbf991d536bfca430adc3a | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-extra-00000.tar | |
{
"key": "29daa268ce4d64b886ec7de8e914c0a055b6d3142ae1dd509d5e674f76e5890f",
"url": "https://mcdn.podbean.com/mf/web/u93x2ttbwijjzkxw/Intensivt_larande_med_AI_och_Battro.mp3",
"source": {
"id": "es_ailotsen_selected",
"name": "AI-Lotsen Spanish editions",
"languages_hint": "es (item metadata; speech n... | 29daa268ce4d64b886ec7de8e914c0a055b6d3142ae1dd509d5e674f76e5890f | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-extra-00000.tar | |
{
"key": "f9a749a0970c19fcaf0e1193afa6db177f2d065b418e87b0d2b8d5386bbd080b",
"url": "https://lcdn.letscast.fm/media/podcast/e491ed26/episode/abb99f58.mp3?t=1746779946",
"source": {
"id": "de_kicast_selected",
"name": "Der KI CAST",
"languages_hint": "de (RSS channel and item metadata; speech not indep... | f9a749a0970c19fcaf0e1193afa6db177f2d065b418e87b0d2b8d5386bbd080b | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-extra-00000.tar | |
{
"key": "24e0a3ee1e5b0fbba7a277563a120e4ddfd9b98d957be525da55b1cbf5d4fec6",
"url": "https://www.ebk-koeln.de/aktuelles/podcast/04-podcast-lernvision-mixdown.mp3",
"source": {
"id": "de_ebk_lernvision",
"name": "Folge 04: „LernVision EBK“ – Lernen neu gedacht",
"languages_hint": "de (publisher descrip... | 24e0a3ee1e5b0fbba7a277563a120e4ddfd9b98d957be525da55b1cbf5d4fec6 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-extra-direct-00000.tar | |
{
"key": "13dbc5c12a61d05c7a43363b5f78018709f1529a2724421b6ec9275614ca2015",
"url": "https://anchor.fm/s/8e48153c/podcast/play/104821397/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-5-30%2Ff450340e-a45b-e06a-e784-e20afeacd9fc.mp3",
"source": {
"id": "de_wissen_bewegung_selected",
"name": "... | 13dbc5c12a61d05c7a43363b5f78018709f1529a2724421b6ec9275614ca2015 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
"key": "09c5a4ad5955d89f08f3c2407871f89af488b170e40214371b39debea87337a9",
"url": "https://anchor.fm/s/108b64348/podcast/play/112590487/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-11-13%2F34a10207-3788-8159-95dc-49081e655498.mp3",
"source": {
"id": "es_rgartner_selected",
"name": "RGart... | 09c5a4ad5955d89f08f3c2407871f89af488b170e40214371b39debea87337a9 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
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"url": "https://anchor.fm/s/8e48153c/podcast/play/104617449/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-5-25%2Fa19bb9f8-7c05-a8f8-4227-cc32eb889e82.mp3",
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"id": "de_wissen_bewegung_selected",
"name": "... | 22502bedc21b07988af6fde8d85069046da546b706b46eb92ea17d9c43e346b1 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
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"url": "https://anchor.fm/s/108b64348/podcast/play/112340232/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-11-8%2F7366dcaa-717a-a55b-90ea-e6db6178e294.mp3",
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"name": "RGartn... | f06211ef25c0b8bd814f6026ff4058e31cf8b48b66c63142292b52aeebd93339 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
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"url": "https://anchor.fm/s/8e48153c/podcast/play/104536090/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-5-23%2Fd0becc66-cdd8-2a14-7e71-bfb4624799a9.mp3",
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"name": "... | 2fea1ca772f56c01ff4dce4d9a21b40eb4b5aab7c71c4ae3190ef4f1007cc3b8 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
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"source": {
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"name": "RGartn... | 183744ab7f51d8a42debb5f5985c13a2364241a41d886194f916e4e9fc50a65c | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
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"source": {
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"name": "Wisse... | dba5d77033868e498f6ed53f4f36ce31f502ebdb6552e30800d081f9982ca2eb | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar |
{
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"source": {
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"name": "RGartn... | 0c2f39ca1a7118c1659102280114841297855963d39e33c1922c6e89dd2668e1 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
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"name": "... | 53b85b5ba577219f70ee2b9e2116a1ec144057776375b40fb9474e9ecd534841 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
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"url": "https://anchor.fm/s/108b64348/podcast/play/112267545/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-11-7%2Ff7737b45-bf0b-4ffd-7973-5c68c5b03020.mp3",
"source": {
"id": "es_rgartner_selected",
"name": "RGartn... | 06bb33faaf08396be44c3fae892d6d6e1476e3a90c7132da234725a070be1c9b | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
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"url": "https://anchor.fm/s/8e48153c/podcast/play/104193931/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-5-16%2F5b8c74ba-c9fa-6e81-7b1e-4ccfd54ca438.mp3",
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"source": {
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{
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"source": {
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"name": "... | 307700282903062dcc56c163cf6ada4e71a89872c112ca4955fe188bd75cfec3 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar | |
{
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"url": "https://anchor.fm/s/108b64348/podcast/play/111373517/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-10-18%2Fe2a9d42a-09db-8ef4-a41e-e03bb9bc6f94.mp3",
"source": {
"id": "es_rgartner_selected",
"name": "RGart... | 79a287512ce3f9e1f1354227fd4229d49104263f964813c2a7af8f5dbe7d8a07 | hf://datasets/TTS-AGI/NotebookLM-Podcasts@c4cdd484541ad17fce4ad34007a483a1f69ccc60/des-rss-00000.tar |
End of preview.
NotebookLM Podcasts
A multilingual collection of 8,223 podcast episodes totaling 2,411.5 hours, organized as WebDataset TAR shards. Google NotebookLM is a research and note-taking tool that can generate podcast-style audio discussions from source material. The collection contains AI-generated audio; some episodes also contain human introductions or editing.
License: Public Domain.
Collection
An Agentify-style search used parallel agents to find publicly available podcasts, RSS feeds, and direct audio links. Original feed and episode metadata were retained with the audio. NotebookLM attribution and spoken language follow the available publisher and feed information; not every recording has been independently listened to.
Listen to examples
Two complete episodes per language. Sign in to access the audio in this private dataset.
English
Ep.59/1803 - Ontological Dependence — ChatSEP (7:34)
Ep.45/1803 - Medieval Theories: Properties of Terms — ChatSEP (7:41)
German
OX.ai 18/18 - Robert Sapolsky: Individuelle Unterschiede und die wichtigste Aussicht — Wissen in Bewegung (6:14)
OX.ai 14/18 - Robert Sapolsky: Menschliches Sexualverhalten II: Pheromone und Abwechslung — Wissen in Bewegung (6:33)
Spanish
El brócoli entre los donuts: Por qué el contenido serio se siente incómodo en YouTube — RGartner Audios Español (5:58)
El influencer equivocado: Por qué fracasan las campañas publicitarias — RGartner Audios Español (7:38)
Dutch
BoerBurgerBeweging – BBBeter Nederland — Politieke Bla Bla (10:47)
CDA – Bouwen op vertrouwen — Politieke Bla Bla (18:54)
French
⏪ Le récap' de Deep Media 🎙️ Mélissa Hébert, directrice plateformes à Télé-Québec — Deep Media (6:07)
⏪ Le récap' de Deep Media 🎙️ Sandrine Roustan, directrice générale des contenus de la RTBF — Deep Media (6:11)
Statistics
| Language | Episodes | Hours |
|---|---|---|
| English | 6,624 | 1,975.3 |
| Spanish | 662 | 209.4 |
| German | 319 | 96.6 |
| Japanese | 142 | 33.6 |
| Chinese | 126 | 16.7 |
| English / Dutch – unresolved | 104 | 29.5 |
| Norwegian | 66 | 8.0 |
| French | 57 | 13.6 |
| Dutch | 38 | 12.8 |
| Ukrainian | 34 | 4.1 |
| Swedish / English – unresolved | 17 | 4.3 |
| Italian | 15 | 3.8 |
| Swedish | 8 | 0.8 |
| Danish | 5 | 0.7 |
| Finnish | 4 | 1.1 |
| Portuguese | 1 | 0.5 |
| English / German – unresolved | 1 | 0.7 |
| Total | 8,223 | 2,411.5 |
Every episode has a known duration. Mixed-language labels indicate that the spoken language has not been resolved.
Dataset structure
Each TAR shard contains paired files sharing the same key: KEY.mp3, KEY.m4a, KEY.wav, or KEY.mp4, and KEY.json. Most audio is MP3 or M4A; the collection also includes WAV and one MP4 containing audio and video. File extensions follow the container signature.
The JSON metadata can include source and episode information, RSS XML or publisher-page HTML, source and media URLs, timestamps, HTTP response details, provenance notes, file size, and a SHA-256 checksum. Available fields vary by source; session-cookie header values are redacted.
shard-00000.tar
KEY.mp3
KEY.json
...
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