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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.
pt unknown | __key__ string | __url__ string |
|---|---|---|
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDAzYmJjZTBlYjRiNjBiNjAzM2ZiNjEyY2M5ZTFiMGY0YzVjYjFiNWM4MTh(...TRUNCATED) | 003bbce0eb4b60b6033fb612cc9e1b0f4c5cb1b5c818e817e5487ac337bb1441 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDA4ZWQ1MjFhNjVkNjRjOWZjY2NkNTIzNzJiNzhlNTZjZmI4MzhkOWEwOTF(...TRUNCATED) | 008ed521a65d64c9fcccd52372b78e56cfb838d9a091be78491a611dbe6a9773 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDA5MzA2Y2NmZmQ5NzM0OGIyYjMzOTEzNTFkYTRjN2QzZmVlYjFmODNmYWZ(...TRUNCATED) | 009306ccffd97348b2b3391351da4c7d3feeb1f83faff7de1b0377f6319686d4 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDEyM2MyNTUxOTIyMDA0Y2IwYjg2NWViOTNlNWJhNWE0ODQ4MGFkZjlkMGU(...TRUNCATED) | 0123c2551922004cb0b865eb93e5ba5a48480adf9d0e8fc625f1a396db76b1f2 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDEzMDIwZmE1MDBkNGMyNTYxNGNkM2RjNjlmMWRiMzQxZjk5YjY2ZDk2Mzg(...TRUNCATED) | 013020fa500d4c25614cd3dc69f1db341f99b66d9638823f0a30aaea07459832 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDE2OTE2YWI2MGRlNmYyNTgzZGU5YWE1YTI4YjllYzVjOTg0OTRmYTY1YjF(...TRUNCATED) | 016916ab60de6f2583de9aa5a28b9ec5c98494fa65b1c11d752814686189e800 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDE4N2NiNDY1Y2MyYzEyYzkxZWVmOTZjMjZjZDM5MDMwZGNkYWM5NDkxMzV(...TRUNCATED) | 0187cb465cc2c12c91eef96c26cd39030dcdac949135d0893e92e934038e06bc | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDJhOTJiZDBlYzY4N2Y3Mjg4Njk4OTc4MDY4OTE5M2U2NDYwMmI5N2Q4MDM(...TRUNCATED) | 02a92bd0ec687f72886989780689193e64602b97d8034d3c0b369e7bb4e39123 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDJmMDYxMGZhOWU3ZGI2ZGYwZGU1YmE0OWYxNTRlMTQzZjAwN2MzMDAxOWR(...TRUNCATED) | 02f0610fa9e7db6df0de5ba49f154e143f007c30019dcadacaed6b75fe3ca425 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAABUAA4AMDJmOTRiNTk4N2M3Njc3ZGJjZDFkZTQyNDMzN2ViZmE5MmQzNTY3ZGNjYWV(...TRUNCATED) | 02f94b5987c7677dbcd1de424337ebfa92d3567dccaebfcaf8a6eff40e71b8e0 | "hf://datasets/jay-junjiewu/side-transformer-ref42-3h-mel-cache@b5acaf9c8cd06c632168a7bd3cf94eecf37c(...TRUNCATED) |
End of preview.
REF42-3H encoder training Mel cache
The 80-bin log-Mel tensors that the REF42 speech encoder was trained on (EXP12 of the
side_transformer project). REF42 is the encoder of the REF42-3H MCI-from-speech model;
its weights and resumable training states are in the model repository
jay-junjiewu/side-transformer-ref42-3h (encoder_training/).
Contents
| Path | Contents |
|---|---|
data/ls/ls-NNNNN.tar |
107,235 LibriSpeech utterances (train-clean-100 + train-clean-360; training and validation rows) |
data/ami/ami-NNNNN.tar |
1,432 AMI headset segments |
manifest.jsonl.gz |
One row per tensor: id, corpus, split, speaker, duration, frame window (mel_start, frames), CTC targets and text, source file name and SHA-256, and mel.shard / mel.member / mel.sha256 locating the tensor |
Each tar member is <sha>.pt, a torch.float32 tensor [80, T] saved with
torch.save. It covers the whole native recording; the training window is
tensor[:, mel_start:mel_start + frames]. Features: 16 kHz mono, 400-sample FFT and window,
160-sample hop (10 ms), 80 Mel bins, natural log of power clamped at 1e-10. Normalise with
encoder_training/data/normalization.json from the model repository.
Resuming encoder training
Unpack the tars into one folder and point each manifest row's mel path at its member,
then place the manifest as experiments/exp12/data/manifest.jsonl in the code
repository; retrieval_mci.exp12.training resumes from the last.pt states in the model
repository. The Mel tensors were checked against the training manifest's SHA-256 when
packed.
Licence and attribution
Derived from LibriSpeech (Panayotov et al., 2015) and the AMI Meeting Corpus (Carletta et al., 2005), both CC BY 4.0. No clinical data is included.
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