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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 1520, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 130, 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 34, 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 1405, 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 982, 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 786, 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 1369, 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 948, 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 1382, 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 1560, 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.
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End of preview.
Emilia Expressive — Phase-1 Filtered Subset
Auto-generated by emilia_pipeline.scoring.phase1_hf. This is the Phase-1
filtered view: every clip that survived the S0+S1 acoustic funnel, physically
partitioned into quality tiers so you can download exactly the strictness
level you want -- before Phase-2 emotion labeling.
Derived from amphion/Emilia-Dataset (CC-BY-NC-4.0); the same license and usage restrictions apply.
- Pipeline version:
voxsift-emilia-v1.3-full(schema1.3) - Clips: 4,487,838 (4,487,838 with audio)
- Format: WebDataset tar shards under
data/{tier}/; each clip is{clip_id}.mp3+{clip_id}.json(loadable withdatasets.load_dataset("webdataset", ...)or thewebdatasetlibrary). Audio keeps its original sample rate (Emilia-ZH mixes 24/32/44.1 kHz -- see thesample_ratemetadata column); it is never resampled by the pipeline. - Metadata:
metadata/phase1_metrics.parquet-- one flat row per clip (full S0-S3 metrics +tier+sample_rate, keyed byclip_id). Phase-2 emotion/prosody labels are published incrementally asmetadata/s4_labels.parquetwith the same key; audio tars are never rewritten.
Tiers: pick your filtering level
| Tier | Selection rule | Clips |
|---|---|---|
prime |
S3 speaker-purity pass (single, intruded_trimmed, degraded_pass) AND prosody_dsp_score in the global top 40% of S3-pass clips — the curated expressive core |
1,300,693 |
extended |
S3 pass, below the top-40% prosody cut — clean but prosodically flatter | 1,951,038 |
s3rejected |
S1-pass but rejected by S3 sliding-window purity (possible speaker intrusion / degradation); shipped verbatim, no trim, use at your own risk | 1,236,107 |
Rules of thumb:
- Just want the best expressive TTS data -> download
data/prime/only. - Want more hours, still clean ->
data/prime/+data/extended/, then optionally re-cut byprosody_dsp_scoreyourself. - Custom funnel -> take all tiers and filter on
metadata/phase1_metrics.parquet: every S0-S3 metric is a column, so any stricter (or looser) gate is a parquet query, no repacking needed.
Filtering funnel (applied upstream of the tiers)
| Stage | What it does |
|---|---|
| S0 | Metadata prefilter: 3.0-30.0s, lang=zh, original DNSMOS ≥ 3.2, text ≥ 4 chars |
| S1 | Acoustic gate: aes_pq ≥ 7.0, aes_pc ≤ 2.5, aes_ce ≥ 5.0, SNR ≥ 20.0 dB, bandwidth ≥ 4000.0 Hz |
| S2 | Prosody richness score (prosody_dsp_score, z-scored over all S1 survivors); the top 40% cut among S3-pass clips defines prime |
| S3 | Sliding-window speaker purity; pass verdicts (single, intruded_trimmed, degraded_pass) split prime/extended, the rest -> s3rejected |
Clips are ordered by labeling priority (prosody_dsp_score * norm_aesthetics_pq); the
priority_rank field preserves that order. intruded_trimmed clips ship
head/tail-trimmed audio matching their advertised duration, re-encoded as
high-quality VBR MP3 (all other audio is the verbatim source bytes;
s3rejected audio is always verbatim).
Per-clip JSON schema
Identity (clip_id, source_shard, text, speaker, language,
duration_s), tier, purity (verdict + trim bounds + gender), and labeling
priority are always present. When packaged with include_metrics, a
metrics block carries the full S0-S3 acoustics + prosody numbers.
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