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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
path: string
duration: double
text: string
alignments: list<item: list<item: string>>
child 0, item: list<item: string>
child 0, item: string
to
{'alignments': List(List(Json(decode=True)))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table 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/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
path: string
duration: double
text: string
alignments: list<item: list<item: string>>
child 0, item: list<item: string>
child 0, item: string
to
{'alignments': List(List(Json(decode=True)))}
because column names don't match
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 1694, 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 1880, 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.
alignments list |
|---|
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1.7000000000000002,... |
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[
... |
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... |
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[
... |
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... |
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... |
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... |
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0,
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[
"katik... |
[] |
[] |
End of preview.
Swahili Moshi Fine-tuning Dataset (mwanamke)
Overview
This dataset prepares Swahili conversational audio for fine-tuning
Moshika (the female-voice
Moshi variant) using
kyutai-labs/moshi-finetune.
It builds on the stereo, speaker-separated audio chunks from
rlabz/qsuperposition_mwanamke
and adds the .jsonl index and per-file .json transcripts that
moshi-finetune requires for training.
Source Data
- Origin:
rlabz/qsuperposition_mwanamke— stereo conversational Swahili audio chunks, left/right channel per speaker - Language: Swahili (Kiswahili)
- License: CC-BY-4.0 (inherited from the upstream source data)
Processing Pipeline
- Stereo audio export — Each row's stereo audio (decoded directly from
the underlying storage to avoid
datasets' automatic downmix to mono) was written out to disk as an individual.wavfile. .jsonlindex generation — A.jsonlfile was built listing every.wav's relative path and true duration (measured from the saved file itself viasphn.durations), in the exact formatmoshi-finetuneexpects:{"path": "audio/0.wav", "duration": 24.52} {"path": "audio/1.wav", "duration": 18.31}- Transcription — Each
.wav's left channel (Moshi's own speech stream) was transcribed usingannotate.pyfrommoshi-finetune, which runs Whisper (large) with word-level timestamps. This produces one.jsonsidecar per audio file (audio/0.json,audio/1.json, ...) containing timestamped word alignments, and the resulting transcript text was also merged back into the corresponding.jsonlrecord. - Incremental, checkpointed processing — Transcription was run and pushed to the Hub in periodic batches, so progress could be resumed after a Colab disconnect without re-transcribing already-completed files.
Dataset Structure
mwanamke.jsonl
audio/
├── 0.wav
├── 0.json
├── 1.wav
├── 1.json
└── ...
Each line of mwanamke.jsonl:
| Field | Description |
|---|---|
path |
Relative path to the stereo .wav file |
duration |
Duration of the audio clip, in seconds |
text |
Transcript text for the clip's left channel (Moshi's own speech stream) |
alignments |
Word-level [text, [start, end], speaker] timestamps from annotate.py |
Each audio/<id>.json sidecar contains the same alignment data as produced
directly by annotate.py, associated with its matching audio/<id>.wav.
Intended Use
Prepared as a ready-to-train dataset for fine-tuning Moshika on Swahili
dialogue via moshi-finetune's training pipeline (data.train_data should
point at mwanamke.jsonl).
Limitations
- Only the left channel (Moshi's own speech stream) is transcribed, per
moshi-finetune's expected format — the right channel (the other speaker/user stream) has no associated text. - Transcript quality depends on Whisper's accuracy for Swahili and on the upstream stereo channel separation, which is diarization-gated rather than true acoustic source separation.
- This is a small dataset derived from a limited set of source recordings, intended as an initial fine-tuning set rather than a large-scale corpus.
License & Attribution
This dataset is derived from rlabz/qsuperposition_mwanamke and is
distributed under CC-BY-4.0.
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