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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
format: string
label_type: string
source: struct<dataset: string, revision: string, public_sample_license: string, attribution: string, audio_ (... 18 chars omitted)
child 0, dataset: string
child 1, revision: string
child 2, public_sample_license: string
child 3, attribution: string
child 4, audio_in_artifact: bool
scope: struct<selection: string, role_projection: string, frames_per_second: double, total_labeled_seconds: (... 497 chars omitted)
child 0, selection: string
child 1, role_projection: string
child 2, frames_per_second: double
child 3, total_labeled_seconds: int64
child 4, records: int64
child 5, teacher_proposals: struct<BARGE: int64, BACKCHANNEL: int64, ENDPOINT: int64>
child 0, BARGE: int64
child 1, BACKCHANNEL: int64
child 2, ENDPOINT: int64
child 6, splits: struct<pilot: list<item: string>, rule: string>
child 0, pilot: list<item: string>
child 0, item: string
child 1, rule: string
child 7, conversations: struct<conv_04692967d40e: struct<local_file: string, sha256: string, records: int64>, conv_bcff0c2a0 (... 222 chars omitted)
child 0, conv_04692967d40e: struct<local_file: string, sha256: string, records: int64>
child 0, local_file: string
child 1, sha256: string
child 2, records: int64
child 1, conv_bcff0c2a0be7: struct<local_file: string, sha256: string, records: int64>
child 0, local_file: string
child 1, sha256: string
...
, agent: string>
child 0, user: string
child 1, agent: string
schema_version: string
policy: struct<rules_revision: string, frame_ms: int64, thresholds: struct<ep_thr: double, ep_deb: int64, ba (... 103 chars omitted)
child 0, rules_revision: string
child 1, frame_ms: int64
child 2, thresholds: struct<ep_thr: double, ep_deb: int64, barge_thr: double, barge_deb: int64, bc_thr: double, bc_deb: i (... 42 chars omitted)
child 0, ep_thr: double
child 1, ep_deb: int64
child 2, barge_thr: double
child 3, barge_deb: int64
child 4, bc_thr: double
child 5, bc_deb: int64
child 6, theta: double
child 7, bc_refractory: int64
state: struct<mode: string, prev_state: string, energy_db: double, frames_left: int64>
child 0, mode: string
child 1, prev_state: string
child 2, energy_db: double
child 3, frames_left: int64
outcome: null
action: struct<id: string, executed: bool>
child 0, id: string
child 1, executed: bool
teacher_proposal: struct<source: string, events: list<item: struct<kind: string, value: double, threshold: double, mar (... 14 chars omitted)
child 0, source: string
child 1, events: list<item: struct<kind: string, value: double, threshold: double, margin: double>>
child 0, item: struct<kind: string, value: double, threshold: double, margin: double>
child 0, kind: string
child 1, value: double
child 2, threshold: double
child 3, margin: double
label: null
to
{'schema_version': Value('string'), 'source_ref': {'dataset': Value('string'), 'revision': Value('string'), 'conversation_id': Value('string'), 'start_ms': Value('int64'), 'end_ms': Value('int64'), 'channels': {'user': Value('string'), 'agent': Value('string')}}, 'trace': Value('string'), 'turn_id': Value('string'), 'ts': Value('float64'), 'tier': Value('string'), 'model': Value('string'), 'policy': {'rules_revision': Value('string'), 'frame_ms': Value('int64'), 'thresholds': {'ep_thr': Value('float64'), 'ep_deb': Value('int64'), 'barge_thr': Value('float64'), 'barge_deb': Value('int64'), 'bc_thr': Value('float64'), 'bc_deb': Value('int64'), 'theta': Value('float64'), 'bc_refractory': Value('int64')}}, 'state': {'mode': Value('string'), 'prev_state': Value('string'), 'energy_db': Value('float64'), 'frames_left': Value('int64')}, 'questions': {'turn_state': {'type': Value('string'), 'instructions': Value('string'), 'criteria': {'listen': Value('string'), 'hold': Value('string'), 'speak': Value('string'), 'backchannel': Value('string')}}, 'end_of_turn': {'type': Value('string'), 'instructions': Value('string'), 'criteria': {'true': Value('string'), 'false': Value('string')}}, 'barge_in': {'type': Value('string'), 'instructions': Value('string'), 'criteria': {'true': Value('string'), 'false': Value('string')}}, 'assistant_backchannel': {'type': Value('string'), 'instructions': Value('string'), 'criteria': {'true': Value('string'), 'false': Value('string')}}}, 'answers': {'turn_state': {'type': Value('string'), 'choice': Value('string'), 'probabilities': {'listen': Value('float64'), 'hold': Value('float64'), 'speak': Value('float64'), 'backchannel': Value('float64')}, 'confidence': Value('float64')}, 'end_of_turn': {'type': Value('string'), 'noul': Value('float64')}, 'barge_in': {'type': Value('string'), 'noul': Value('float64')}, 'assistant_backchannel': {'type': Value('string'), 'noul': Value('float64')}}, 'action': {'id': Value('string'), 'executed': Value('bool')}, 'teacher_proposal': {'source': Value('string'), 'events': List({'kind': Value('string'), 'value': Value('float64'), 'threshold': Value('float64'), 'margin': Value('float64')})}, 'outcome': Value('null'), 'label': Value('null'), 'teacher_scores': {'heads': List(Value('string')), 'values': List(Value('float64'))}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
for key, table 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/json/json.py", line 361, 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 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
format: string
label_type: string
source: struct<dataset: string, revision: string, public_sample_license: string, attribution: string, audio_ (... 18 chars omitted)
child 0, dataset: string
child 1, revision: string
child 2, public_sample_license: string
child 3, attribution: string
child 4, audio_in_artifact: bool
scope: struct<selection: string, role_projection: string, frames_per_second: double, total_labeled_seconds: (... 497 chars omitted)
child 0, selection: string
child 1, role_projection: string
child 2, frames_per_second: double
child 3, total_labeled_seconds: int64
child 4, records: int64
child 5, teacher_proposals: struct<BARGE: int64, BACKCHANNEL: int64, ENDPOINT: int64>
child 0, BARGE: int64
child 1, BACKCHANNEL: int64
child 2, ENDPOINT: int64
child 6, splits: struct<pilot: list<item: string>, rule: string>
child 0, pilot: list<item: string>
child 0, item: string
child 1, rule: string
child 7, conversations: struct<conv_04692967d40e: struct<local_file: string, sha256: string, records: int64>, conv_bcff0c2a0 (... 222 chars omitted)
child 0, conv_04692967d40e: struct<local_file: string, sha256: string, records: int64>
child 0, local_file: string
child 1, sha256: string
child 2, records: int64
child 1, conv_bcff0c2a0be7: struct<local_file: string, sha256: string, records: int64>
child 0, local_file: string
child 1, sha256: string
...
, agent: string>
child 0, user: string
child 1, agent: string
schema_version: string
policy: struct<rules_revision: string, frame_ms: int64, thresholds: struct<ep_thr: double, ep_deb: int64, ba (... 103 chars omitted)
child 0, rules_revision: string
child 1, frame_ms: int64
child 2, thresholds: struct<ep_thr: double, ep_deb: int64, barge_thr: double, barge_deb: int64, bc_thr: double, bc_deb: i (... 42 chars omitted)
child 0, ep_thr: double
child 1, ep_deb: int64
child 2, barge_thr: double
child 3, barge_deb: int64
child 4, bc_thr: double
child 5, bc_deb: int64
child 6, theta: double
child 7, bc_refractory: int64
state: struct<mode: string, prev_state: string, energy_db: double, frames_left: int64>
child 0, mode: string
child 1, prev_state: string
child 2, energy_db: double
child 3, frames_left: int64
outcome: null
action: struct<id: string, executed: bool>
child 0, id: string
child 1, executed: bool
teacher_proposal: struct<source: string, events: list<item: struct<kind: string, value: double, threshold: double, mar (... 14 chars omitted)
child 0, source: string
child 1, events: list<item: struct<kind: string, value: double, threshold: double, margin: double>>
child 0, item: struct<kind: string, value: double, threshold: double, margin: double>
child 0, kind: string
child 1, value: double
child 2, threshold: double
child 3, margin: double
label: null
to
{'schema_version': Value('string'), 'source_ref': {'dataset': Value('string'), 'revision': Value('string'), 'conversation_id': Value('string'), 'start_ms': Value('int64'), 'end_ms': Value('int64'), 'channels': {'user': Value('string'), 'agent': Value('string')}}, 'trace': Value('string'), 'turn_id': Value('string'), 'ts': Value('float64'), 'tier': Value('string'), 'model': Value('string'), 'policy': {'rules_revision': Value('string'), 'frame_ms': Value('int64'), 'thresholds': {'ep_thr': Value('float64'), 'ep_deb': Value('int64'), 'barge_thr': Value('float64'), 'barge_deb': Value('int64'), 'bc_thr': Value('float64'), 'bc_deb': Value('int64'), 'theta': Value('float64'), 'bc_refractory': Value('int64')}}, 'state': {'mode': Value('string'), 'prev_state': Value('string'), 'energy_db': Value('float64'), 'frames_left': Value('int64')}, 'questions': {'turn_state': {'type': Value('string'), 'instructions': Value('string'), 'criteria': {'listen': Value('string'), 'hold': Value('string'), 'speak': Value('string'), 'backchannel': Value('string')}}, 'end_of_turn': {'type': Value('string'), 'instructions': Value('string'), 'criteria': {'true': Value('string'), 'false': Value('string')}}, 'barge_in': {'type': Value('string'), 'instructions': Value('string'), 'criteria': {'true': Value('string'), 'false': Value('string')}}, 'assistant_backchannel': {'type': Value('string'), 'instructions': Value('string'), 'criteria': {'true': Value('string'), 'false': Value('string')}}}, 'answers': {'turn_state': {'type': Value('string'), 'choice': Value('string'), 'probabilities': {'listen': Value('float64'), 'hold': Value('float64'), 'speak': Value('float64'), 'backchannel': Value('float64')}, 'confidence': Value('float64')}, 'end_of_turn': {'type': Value('string'), 'noul': Value('float64')}, 'barge_in': {'type': Value('string'), 'noul': Value('float64')}, 'assistant_backchannel': {'type': Value('string'), 'noul': Value('float64')}}, 'action': {'id': Value('string'), 'executed': Value('bool')}, 'teacher_proposal': {'source': Value('string'), 'events': List({'kind': Value('string'), 'value': Value('float64'), 'threshold': Value('float64'), 'margin': Value('float64')})}, 'outcome': Value('null'), 'label': Value('null'), 'teacher_scores': {'heads': List(Value('string')), 'values': List(Value('float64'))}}
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 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 1702, 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 1887, 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.
schema_version string | source_ref dict | trace string | turn_id string | ts float64 | tier string | model string | policy dict | state dict | questions dict | answers dict | action dict | teacher_proposal dict | outcome null | label null | teacher_scores dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 0,
"end_ms": 80,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f1 | 0.08 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "listen",
"prev_state": "LISTEN",
"energy_db": -94.449,
"frames_left": 3
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "speak",
"probabilities": {
"listen": 0.21055,
"hold": 0.031968,
"speak": 0.73998,
"backchannel": 0.017502
},
"confidence": 0.73998
},
"end_of_turn": {
"type": "noul",
"noul": 0.9953
},
"barge_in": {
"type": ... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
0.2262,
-1.6588,
1.4831,
-2.2612,
0.2062,
0.0008,
0.6962,
0.9953
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 80,
"end_ms": 160,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f2 | 0.16 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "listen",
"prev_state": "LISTEN",
"energy_db": -89.496,
"frames_left": 2
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "listen",
"probabilities": {
"listen": 0.992934,
"hold": 0.000402,
"speak": 0.005745,
"backchannel": 0.00092
},
"confidence": 0.992934
},
"end_of_turn": {
"type": "noul",
"noul": 0.8563
},
"barge_in": {
"type... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
4.5706,
-3.2411,
-0.5818,
-2.4137,
0.0556,
0.005,
0.0733,
0.8563
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 160,
"end_ms": 240,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f3 | 0.24 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "listen",
"prev_state": "LISTEN",
"energy_db": -84.92,
"frames_left": 1
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "listen",
"probabilities": {
"listen": 0.99864,
"hold": 0.000063,
"speak": 0.000899,
"backchannel": 0.000398
},
"confidence": 0.99864
},
"end_of_turn": {
"type": "noul",
"noul": 0.8356
},
"barge_in": {
"type"... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
5.7312,
-3.9439,
-1.282,
-2.0963,
0.047,
0.0062,
0.0179,
0.8356
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 240,
"end_ms": 320,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f4 | 0.32 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "listen",
"prev_state": "LISTEN",
"energy_db": -85.013,
"frames_left": 0
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "listen",
"probabilities": {
"listen": 0.999111,
"hold": 0.00003,
"speak": 0.000743,
"backchannel": 0.000116
},
"confidence": 0.999111
},
"end_of_turn": {
"type": "noul",
"noul": 0.7957
},
"barge_in": {
"type... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
6.3453,
-4.0723,
-0.8583,
-2.7157,
0.0475,
0.0059,
0.023,
0.7957
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 320,
"end_ms": 400,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f5 | 0.4 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
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} | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f6 | 0.48 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f7 | 0.56 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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"speak": "The agent has the floor and is spe... | {
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IDFON-VOICE-DECISION/1 | {
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"channels": {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f8 | 0.64 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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"speak": "The agent has the floor and is spe... | {
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"barge_in": {
"typ... | {
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IDFON-VOICE-DECISION/1 | {
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"channels": {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f9 | 0.72 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
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"barge_in": {
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} |
IDFON-VOICE-DECISION/1 | {
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"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
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"channels": {
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"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f10 | 0.8 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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} |
IDFON-VOICE-DECISION/1 | {
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"channels": {
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"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f11 | 0.88 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
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} |
IDFON-VOICE-DECISION/1 | {
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"channels": {
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"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f12 | 0.96 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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"speak": "The agent has the floor and is spe... | {
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IDFON-VOICE-DECISION/1 | {
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"channels": {
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"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f13 | 1.04 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} |
IDFON-VOICE-DECISION/1 | {
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"conversation_id": "conv_04692967d40e",
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"channels": {
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"agent": "b.flac"
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f14 | 1.12 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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"speak": "The agent has the floor and is spe... | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f15 | 1.2 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f16 | 1.28 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f17 | 1.36 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f18 | 1.44 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f19 | 1.52 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f20 | 1.6 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f21 | 1.68 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f22 | 1.76 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f23 | 1.84 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} |
IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f24 | 1.92 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f25 | 2 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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"barge_in": {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f26 | 2.08 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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"barge_in": {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f27 | 2.16 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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"barge_in": {
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IDFON-VOICE-DECISION/1 | {
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"channels": {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f28 | 2.24 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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"barge_in": {
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} |
IDFON-VOICE-DECISION/1 | {
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"agent": "b.flac"
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f29 | 2.32 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f30 | 2.4 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f31 | 2.48 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f32 | 2.56 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f33 | 2.64 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f34 | 2.72 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f35 | 2.8 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f36 | 2.88 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f37 | 2.96 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f38 | 3.04 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f39 | 3.12 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f40 | 3.2 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f41 | 3.28 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f42 | 3.36 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f43 | 3.44 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f44 | 3.52 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f45 | 3.6 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f46 | 3.68 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f47 | 3.76 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f48 | 3.84 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f49 | 3.92 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f50 | 4 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f51 | 4.08 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f52 | 4.16 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f53 | 4.24 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f54 | 4.32 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f55 | 4.4 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f56 | 4.48 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f57 | 4.56 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f58 | 4.64 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f59 | 4.72 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f60 | 4.8 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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IDFON-VOICE-DECISION/1 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f63 | 5.04 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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IDFON-VOICE-DECISION/1 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f66 | 5.28 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f67 | 5.36 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f68 | 5.44 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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IDFON-VOICE-DECISION/1 | {
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} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f69 | 5.52 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
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} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 5520,
"end_ms": 5600,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f70 | 5.6 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "speaking",
"prev_state": "SPEAK",
"energy_db": -31.058,
"frames_left": 2
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "speak",
"probabilities": {
"listen": 0.453133,
"hold": 0.002611,
"speak": 0.544074,
"backchannel": 0.000183
},
"confidence": 0.544074
},
"end_of_turn": {
"type": "noul",
"noul": 0
},
"barge_in": {
"type": "n... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
2.8888,
-2.2678,
3.0717,
-4.9267,
0.0492,
0.4471,
0.4429,
0
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 5600,
"end_ms": 5680,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f71 | 5.68 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "speaking",
"prev_state": "SPEAK",
"energy_db": -50.585,
"frames_left": 1
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "listen",
"probabilities": {
"listen": 0.650037,
"hold": 0.006594,
"speak": 0.343274,
"backchannel": 0.000095
},
"confidence": 0.650037
},
"end_of_turn": {
"type": "noul",
"noul": 0
},
"barge_in": {
"type": "... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
2.978,
-1.6129,
2.3395,
-5.8484,
0.058,
0.3956,
0.4847,
0
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 5680,
"end_ms": 5760,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f72 | 5.76 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "speaking",
"prev_state": "SPEAK",
"energy_db": -69.51,
"frames_left": 0
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "listen",
"probabilities": {
"listen": 0.565711,
"hold": 0.33003,
"speak": 0.104239,
"backchannel": 0.00002
},
"confidence": 0.565711
},
"end_of_turn": {
"type": "noul",
"noul": 0.002
},
"barge_in": {
"type":... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
2.5994,
2.0605,
0.908,
-7.6587,
0.1436,
0.1997,
0.4004,
0.002
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 5760,
"end_ms": 5840,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f73 | 5.84 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "speaking",
"prev_state": "SPEAK",
"energy_db": -36.116,
"frames_left": 3
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "speak",
"probabilities": {
"listen": 0.458365,
"hold": 0.003052,
"speak": 0.538219,
"backchannel": 0.000364
},
"confidence": 0.538219
},
"end_of_turn": {
"type": "noul",
"noul": 0
},
"barge_in": {
"type": "n... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
2.7946,
-2.2173,
2.9552,
-4.3434,
0.03,
0.3971,
0.4809,
0
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 5840,
"end_ms": 5920,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f74 | 5.92 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "speaking",
"prev_state": "SPEAK",
"energy_db": -26.33,
"frames_left": 2
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "speak",
"probabilities": {
"listen": 0.012049,
"hold": 0.000322,
"speak": 0.983963,
"backchannel": 0.003666
},
"confidence": 0.983963
},
"end_of_turn": {
"type": "noul",
"noul": 0.0148
},
"barge_in": {
"type... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
-0.2337,
-3.8559,
4.1689,
-1.4236,
0.0201,
0.1852,
0.986,
0.0148
]
} |
IDFON-VOICE-DECISION/1 | {
"dataset": "TheAgenticDataCompany/open-yap-1k",
"revision": "a08b1c80e50159d462ddcbbabfdea5bf7ccfc626",
"conversation_id": "conv_04692967d40e",
"start_ms": 5920,
"end_ms": 6000,
"channels": {
"user": "a.flac",
"agent": "b.flac"
}
} | TheAgenticDataCompany/open-yap-1k@a08b1c80e50159d462ddcbbabfdea5bf7ccfc626:conv_04692967d40e | conv_04692967d40e:f75 | 6 | on_device | nineninesix/tyger@bundle:11020c88b1e319186056c666cf4b74908c9bf8f0b1b0b374c5e6102d813d749d+TygerStepInt8 | {
"rules_revision": "sha256:36fc402e7fbbe22e16fddc3296a348ba24d37f1b46ef2bfd036ba7594a66a1fd",
"frame_ms": 80,
"thresholds": {
"ep_thr": 0.9146,
"ep_deb": 2,
"barge_thr": 0.5,
"barge_deb": 2,
"bc_thr": 0.5,
"bc_deb": 2,
"theta": 0.2,
"bc_refractory": 15
}
} | {
"mode": "speaking",
"prev_state": "SPEAK",
"energy_db": -25.374,
"frames_left": 1
} | {
"turn_state": {
"type": "choice",
"instructions": "Which Tyger floor state is most likely at this frame?",
"criteria": {
"listen": "The user has the floor and the agent is not speaking.",
"hold": "The user is speaking or holding the floor.",
"speak": "The agent has the floor and is spe... | {
"turn_state": {
"type": "choice",
"choice": "speak",
"probabilities": {
"listen": 0.000341,
"hold": 0.000034,
"speak": 0.999227,
"backchannel": 0.000397
},
"confidence": 0.999227
},
"end_of_turn": {
"type": "noul",
"noul": 0.0115
},
"barge_in": {
"type... | {
"id": "not_executed",
"executed": false
} | {
"source": "tyger-rules",
"events": []
} | null | null | {
"heads": [
"state_listen",
"state_hold",
"state_speak",
"state_backchannel",
"hazard",
"yield",
"bc",
"p_end"
],
"values": [
-1.7441,
-4.0435,
6.2375,
-1.5924,
0.004,
0.3833,
0.9899,
0.0115
]
} |
End of preview.
Open Yap 1K turn annotations — Tyger pseudo-labels (v0.1 pilot slice)
Per-frame turn-taking annotations for 20 minutes (four conversations × first 300 s) of the public 8.92-hour sample of The Agentic Data Company's Open Yap 1K dataset. One JSONL record per 80 ms frame carries the teacher's floor state, typed probabilistic decision scores, and thresholded rule proposals for end-of-turn, BARGE (agent yields to a user cut-in), and BACKCHANNEL (agent acknowledges while the user keeps the floor).
These are teacher pseudo-labels, not human ground truth. They are for distillation and schema prototyping; do not report agreement with them as accuracy.
No audio, transcripts, demographics, or speaker crosswalks are included.
Data
data/turn-annotations.jsonl— 14,992 records (38.1 MB; one record per 80 ms frame), schemaIDFON-VOICE-DECISION/1.manifest.json— full provenance: source revision, teacher bundle + rules hashes, thresholds, per-conversation checksums, ontology, and limitations.human-audit-openyap.csv— the 75-item stratified human-audit subset with verdicts (no audio).
Record fields: source_ref (dataset revision, conversation, channels, time
span), ts, state (mode listen/speaking, prev_state, energy),
questions + answers (typed probabilities: turn_state 4-way floor state,
end_of_turn, barge_in, assistant_backchannel), teacher_scores (raw
8-head vector), teacher_proposal (thresholded rule events), action.executed: false (offline replay), policy (rules revision + thresholds). JSON Schema:
https://github.com/donpark/idfon/blob/main/docs/voice-decision-record.schema.json
Load
from datasets import load_dataset
ds = load_dataset("OWNER/open-yap-1k-turn-annotations", split="train")
Label-generation recipe (pinned)
- Teacher:
nineninesix/tyger, bundle manifest11020c88…, stepTygerStepInt8.onnx(L15 encoder ≈ 1.4 GB + int8 step 23.2 MB). - Rules:
sha256:36fc402e…; thresholds ep .9146 / barge .5 / bc .5 (debounce 2, bc refractory 15). - Floor mode per frame derived from agent-channel RMS with a 6-quiet-hop hysteresis (speaking/listen); rules replayed in the stored mode.
- Paired tracks resampled to 16 kHz mono and merged (left = user, right = agent in derived clips); role projection A = user, B = agent.
- Exporter:
scripts/openyap-tyger-labels.pyin donpark/idfon (resume + SHA-256 sidecars; reruns skip only outputs whose config and hash match).
Splits
All four conversations form one pilot split:
conv_f5c0b02f01e2, conv_e11edcca8521, conv_bcff0c2a0be7, conv_04692967d40e.
Any train/eval split must group by conversation_id (speaker-disjoint where
possible); frame-level random splits are not independent.
Human audit (label-noise estimates)
75 stratified items (32 BARGE, 28 BACKCHANNEL, all 9 ENDPOINT, 6 near-miss contrast windows), single auditor listening to 6 s stereo clips, criterion = presence near the proposal frame (late/clipped/rushed realizations count present with a defect note):
| stratum | confirmed | rate |
|---|---|---|
| BARGE (proposal present) | 20/32 | 62.5% |
| BACKCHANNEL (presence) | 15/28 | 53.6% |
| ENDPOINT (proposal present) | 5/9 | 55.6% |
| BARGE_NEAR_MISS (no proposal) | 5/6 | windows contained real un-proposed cut-ins |
BARGE confirmation rises with teacher score (45.5% at .5–.7 → 83.3% at .9+). A single auditor means wide binomial intervals (±17–29 pp at these n).
Limitations
- Tyger was trained on Open Yap; outputs may be in-sample. Distillation target, not independent evaluation.
- Human-human tracks projected into user/agent roles; no live TTS playback or AEC. Open-loop replay leaves agent speech running after a BARGE proposal, so repeated proposals in one span are not independent cut-ins.
- Backchannel realizations in the source audio are often word-like or mumbly and arrive late, clipped, or rushed; the labels mark opportunity frames, not delivery quality.
- BACKCHANNEL here is the agent acknowledgement while the user speaks — the opposite direction from Pipecat BCD's user-backchannel label. Do not equate.
- ENDPOINT evidence is thin (n = 9 audited); classic endpointing remains the fail-open path.
Sizes
- Annotation data: 38.1 MB JSONL (14,992 records).
- Model weights: not published (trained-weight redistribution requires a separate rights review).
Source and license
Source: TheAgenticDataCompany/open-yap-1k, public sample revision
a08b1c80e50159d462ddcbbabfdea5bf7ccfc626, licensed CC BY 4.0.
Attribution: The Agentic Data Company, Open Yap 1K v1.0. Changes: Tyger
teacher pseudo-labels in Jev/System-One record format. The full 1,000-hour
corpus (DUA-only) is not included. Annotations are released under CC BY 4.0
with the same attribution. Model outputs have no separate output restriction
under the teacher's OpenMDW-1.1 license; this does not override the source
data's terms. Do not use these files for speaker identification or voice
cloning.
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