Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
version: int64
note: string
map: string
buckets: struct<short: string, medium: string, long: string, number: string, latin: string, polyphone: string (... 20 chars omitted)
child 0, short: string
child 1, medium: string
child 2, long: string
child 3, number: string
child 4, latin: string
child 5, polyphone: string
child 6, narration: string
items: list<item: struct<id: string, bucket: string, text: string>>
child 0, item: struct<id: string, bucket: string, text: string>
child 0, id: string
child 1, bucket: string
child 2, text: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 445
to
{'version': Value('int64'), 'note': Value('string'), 'buckets': {'short': Value('string'), 'medium': Value('string'), 'long': Value('string'), 'number': Value('string'), 'latin': Value('string'), 'polyphone': Value('string'), 'narration': Value('string')}, 'items': List({'id': Value('string'), 'bucket': Value('string'), 'text': Value('string')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/map/濕) was specified twice in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 357, in _generate_tables
yield Key(shard_idx, 0), self._cast_table(pa_table)
~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
version: int64
note: string
map: string
buckets: struct<short: string, medium: string, long: string, number: string, latin: string, polyphone: string (... 20 chars omitted)
child 0, short: string
child 1, medium: string
child 2, long: string
child 3, number: string
child 4, latin: string
child 5, polyphone: string
child 6, narration: string
items: list<item: struct<id: string, bucket: string, text: string>>
child 0, item: struct<id: string, bucket: string, text: string>
child 0, id: string
child 1, bucket: string
child 2, text: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 445
to
{'version': Value('int64'), 'note': Value('string'), 'buckets': {'short': Value('string'), 'medium': Value('string'), 'long': Value('string'), 'number': Value('string'), 'latin': Value('string'), 'polyphone': Value('string'), 'narration': Value('string')}, 'items': List({'id': Value('string'), 'bucket': Value('string'), 'text': Value('string')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
webfm-eval
Fixed evaluation set and harness for the WebFM TTS work. Small on purpose: it exists so two model tiers can be compared on the same sentences in the same run, not to be trained on.
Contents
eval/sentences.json 40 sentences, 7 buckets (short / medium / long /
number / latin / polyphone / narration)
eval/score.py raw + normalised CER, prefix CER, per-bucket and
per-voice aggregation, delta against a baseline
eval/t2s.json traditional -> simplified table, used only when
opencc is unavailable
config/ming_voices.json the eight WebFM voices and their prompt transcripts
refs/wav/v01..v08.wav prompt clips, reconstructed from the shipped F5
reference mels
kaggle/bake_voices.py prompt_latent + CAM++ speaker_emb for each voice
kaggle/ming_eval.py teacher x student matrix, resumable per cell
Why the scoring is fussy
Whisper usually transcribes Chinese in traditional characters. Comparing that
against simplified references and counting character differences adds tens of
points of fake error. Every score here is reported twice: cer_raw (as
produced) and cer_norm (after traditional->simplified, punctuation stripped).
The gap is measurement, not model quality.
cer_prefix scores only the first four characters. A garbled opening syllable
barely moves overall CER but ruins a radio line, and it is the failure mode that
weight-only int4 quantisation produced on the 3-step student.
Usage
python3 eval/score.py selftest
python3 eval/score.py aggregate --results results.jsonl --baseline teacher --by-bucket
results.jsonl carries one row per generated utterance:
{id, model, voice, text, hyp_text, spk_cos?, mel_l1?, dur_ratio?}.
Licence
MIT for the code. The prompt clips derive from a purchased SFW corpus; check the source terms before commercial use.
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