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
video: string
duration_sec: double
mode: string
total_utterances: int64
speakers: list<item: string>
child 0, item: string
person_id_database: struct<user: string, matthew: string, jason: string>
child 0, user: string
child 1, matthew: string
child 2, jason: string
num_clips_processed: int64
utterances_by_speaker: struct<User: list<item: struct<person_id: string, name: string, text: string, start_sec: double, end (... 435 chars omitted)
child 0, User: list<item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, (... 67 chars omitted)
child 0, item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, confidence (... 55 chars omitted)
child 0, person_id: string
child 1, name: string
child 2, text: string
child 3, start_sec: double
child 4, end_sec: double
child 5, confidence: double
child 6, source: string
child 7, gemini_speaker_label: string
child 1, Matthew: list<item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, (... 67 chars omitted)
child 0, item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, confidence (... 55 chars omitted)
child 0, person_id: string
child 1, name: string
child 2, text: string
child 3, start_sec: double
child 4, end_sec: double
...
ing, text: string, start_sec: double, end_sec: double, confidence (... 55 chars omitted)
child 0, person_id: string
child 1, name: string
child 2, text: string
child 3, start_sec: double
child 4, end_sec: double
child 5, confidence: double
child 6, source: string
child 7, gemini_speaker_label: string
latency_gemini_call_ms: double
diarized_utterances: list<item: struct<speaker_label: string, text: string, start_sec: double, end_sec: double, confidenc (... 11 chars omitted)
child 0, item: struct<speaker_label: string, text: string, start_sec: double, end_sec: double, confidence: double>
child 0, speaker_label: string
child 1, text: string
child 2, start_sec: double
child 3, end_sec: double
child 4, confidence: double
clip_start_sec: double
clip_end_sec: double
attributed_utterances: list<item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, (... 67 chars omitted)
child 0, item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, confidence (... 55 chars omitted)
child 0, person_id: string
child 1, name: string
child 2, text: string
child 3, start_sec: double
child 4, end_sec: double
child 5, confidence: double
child 6, source: string
child 7, gemini_speaker_label: string
latency_total_ms: double
latency_fuse_ms: double
clip_index: int64
latency_name_extraction_ms: double
to
{'clip_index': Value('int64'), 'clip_start_sec': Value('float64'), 'clip_end_sec': Value('float64'), 'diarized_utterances': List({'speaker_label': Value('string'), 'text': Value('string'), 'start_sec': Value('float64'), 'end_sec': Value('float64'), 'confidence': Value('float64')}), 'attributed_utterances': List({'person_id': Value('string'), 'name': Value('string'), 'text': Value('string'), 'start_sec': Value('float64'), 'end_sec': Value('float64'), 'confidence': Value('float64'), 'source': Value('string'), 'gemini_speaker_label': Value('string')}), 'latency_gemini_call_ms': Value('float64'), 'latency_fuse_ms': Value('float64'), 'latency_name_extraction_ms': Value('float64'), 'latency_total_ms': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
video: string
duration_sec: double
mode: string
total_utterances: int64
speakers: list<item: string>
child 0, item: string
person_id_database: struct<user: string, matthew: string, jason: string>
child 0, user: string
child 1, matthew: string
child 2, jason: string
num_clips_processed: int64
utterances_by_speaker: struct<User: list<item: struct<person_id: string, name: string, text: string, start_sec: double, end (... 435 chars omitted)
child 0, User: list<item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, (... 67 chars omitted)
child 0, item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, confidence (... 55 chars omitted)
child 0, person_id: string
child 1, name: string
child 2, text: string
child 3, start_sec: double
child 4, end_sec: double
child 5, confidence: double
child 6, source: string
child 7, gemini_speaker_label: string
child 1, Matthew: list<item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, (... 67 chars omitted)
child 0, item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, confidence (... 55 chars omitted)
child 0, person_id: string
child 1, name: string
child 2, text: string
child 3, start_sec: double
child 4, end_sec: double
...
ing, text: string, start_sec: double, end_sec: double, confidence (... 55 chars omitted)
child 0, person_id: string
child 1, name: string
child 2, text: string
child 3, start_sec: double
child 4, end_sec: double
child 5, confidence: double
child 6, source: string
child 7, gemini_speaker_label: string
latency_gemini_call_ms: double
diarized_utterances: list<item: struct<speaker_label: string, text: string, start_sec: double, end_sec: double, confidenc (... 11 chars omitted)
child 0, item: struct<speaker_label: string, text: string, start_sec: double, end_sec: double, confidence: double>
child 0, speaker_label: string
child 1, text: string
child 2, start_sec: double
child 3, end_sec: double
child 4, confidence: double
clip_start_sec: double
clip_end_sec: double
attributed_utterances: list<item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, (... 67 chars omitted)
child 0, item: struct<person_id: string, name: string, text: string, start_sec: double, end_sec: double, confidence (... 55 chars omitted)
child 0, person_id: string
child 1, name: string
child 2, text: string
child 3, start_sec: double
child 4, end_sec: double
child 5, confidence: double
child 6, source: string
child 7, gemini_speaker_label: string
latency_total_ms: double
latency_fuse_ms: double
clip_index: int64
latency_name_extraction_ms: double
to
{'clip_index': Value('int64'), 'clip_start_sec': Value('float64'), 'clip_end_sec': Value('float64'), 'diarized_utterances': List({'speaker_label': Value('string'), 'text': Value('string'), 'start_sec': Value('float64'), 'end_sec': Value('float64'), 'confidence': Value('float64')}), 'attributed_utterances': List({'person_id': Value('string'), 'name': Value('string'), 'text': Value('string'), 'start_sec': Value('float64'), 'end_sec': Value('float64'), 'confidence': Value('float64'), 'source': Value('string'), 'gemini_speaker_label': Value('string')}), 'latency_gemini_call_ms': Value('float64'), 'latency_fuse_ms': Value('float64'), 'latency_name_extraction_ms': Value('float64'), 'latency_total_ms': Value('float64')}
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.
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Check out the documentation for more information.
4_6_2026_wsw_run2
Who-Said-What diarization (ONLINE mode) on jason_matt_h264.mp4.
Online Mode Features
- Egocentric speaker detection — Gemini prompt explicitly handles AR glasses perspective. The wearer is labeled as "User" and is never visible in keyframes.
- Name extraction from context — When someone says "my name is X", the system learns their name and retroactively updates all previous utterances from the same speaker.
- Person ID database — Learned names persist across clips. Database cleared between evaluations.
Summary
- Video:
jason_matt_h264.mp4(115.4 sec) - Clips processed: 9
- Total utterances: 37
- Speakers detected: User, Matthew, Jason
- Person ID database: {"user": "User", "matthew": "Matthew", "jason": "Jason"}
- Avg Gemini latency: 3358 ms/clip
Full Transcript
[0.0s - 0.8s] User: "And then begin the conversation."
[1.2s - 1.6s] Matthew: "Okay."
[1.9s - 4.4s] Matthew: "My name is Matthew."
[4.9s - 6.6s] Matthew: "I am a jig student."
[7.2s - 9.6s] Matthew: "And I'm going to graduate this March."
[10.5s - 10.9s] Jason: "Uh my name is Jason."
[11.6s - 12.0s] Jason: "And then."
[12.3s - 13.8s] Jason: "I'm a student of"
[26.0s - 32.5s] Matthew: "big, um, they figured out a lot to be in the military."
[33.1s - 34.9s] Matthew: "I had an open eye, I signed it the next day."
[35.4s - 36.3s] Matthew: "So what is your opinion on this?"
[37.0s - 38.1s] Jason: "We probably want to get closer."
[38.1s - 39.6s] Jason: "It's too wide for now."
[52.3s - 53.6s] Matthew: "I think that it's normal, like like."
[53.6s - 55.3s] Matthew: "Like I feel like that's what the government would ask you to do."
[55.4s - 55.5s] Jason: "Yeah."
[56.0s - 62.9s] Jason: "Oh, so they would probably prevent or any any private company from getting too much."
[65.2s - 72.7s] Matthew: "any private company from getting too much power in the military because they would want to have more control over national security and defense."
[72.8s - 72.9s] Jason: "I."
[72.9s - 73.5s] Jason: "Yes, that's right."
[73.8s - 74.4s] Jason: "The US government."
[74.4s - 74.8s] Matthew: "Yeah, yeah, yeah."
[76.0s - 79.8s] Matthew: "I mean, I feel like if you are a"
[79.4s - 84.7s] Matthew: "I feel like if you are allowed to use AI at work, they can just use AI, they don't want to get it."
[85.5s - 86.1s] Jason: "So do you think any government officials use AI currently?"
[86.8s - 87.7s] Matthew: "Yeah, yeah."
[88.2s - 92.6s] Matthew: "It's it's used by I think they use quantum."
[91.2s - 92.4s] Matthew: "I think they use graph at all right, that's what I said."
[92.5s - 92.9s] Jason: "Oh, they use graph."
[93.1s - 95.3s] Matthew: "So on using graph in the workplace, yeah."
[95.4s - 98.5s] Jason: "So graph's able to see all the confidential public information."
[98.5s - 100.5s] Matthew: "Yeah, yeah, I'll probably provision."
[100.7s - 104.9s] Matthew: "Because they're the partner and then they and then now."
[104.2s - 111.6s] Matthew: "the partner and then they and then now with the that they don't want to continue."
[111.6s - 112.9s] Matthew: "Like cuz the government workers are probably using."
[112.9s - 115.5s] Jason: "Oh, so they they probably they probably used anthropic."
[115.5s - 116.7s] Jason: "there's probably there's probably."
Files
| File | Description |
|---|---|
diarization_results.json |
Full results with named speakers + person ID database |
clips_detail.jsonl |
Per-clip diarized + attributed utterances |
latency_stats.json |
Per-clip latency (Gemini, fusion, name extraction) |
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