Upload croissant.json with huggingface_hub

croissant.json

@@ -17,6 +17,7 @@
       "@type": "@vocab"
     },
     "dct": "http://purl.org/dc/terms/",
+    "equivalentProperty": "cr:equivalentProperty",
     "examples": {
       "@id": "cr:examples",
       "@type": "@json"
@@ -39,6 +40,7 @@
     "regex": "cr:regex",
     "repeated": "cr:repeated",
     "replace": "cr:replace",
+    "samplingRate": "cr:samplingRate",
     "sc": "https://schema.org/",
     "separator": "cr:separator",
     "source": "cr:source",
@@ -69,6 +71,15 @@
   },
   "isLiveDataset": false,
   "distribution": [
+    {
+      "@type": "cr:FileObject",
+      "@id": "vipbench-bundle",
+      "name": "vipbench-bundle",
+      "description": "VIPBench dataset bundle on Hugging Face. The bundle is the dataset git repository; FileObjects and FileSets below are paths within it.",
+      "contentUrl": "https://huggingface.co/datasets/sendfuze/vipbench",
+      "encodingFormat": "git+https",
+      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER"
+    },
     {
       "@type": "cr:FileObject",
       "@id": "speakers-csv",
@@ -76,7 +87,10 @@
       "description": "Per-speaker metadata for the 100 celebrity speakers.",
       "contentUrl": "data/speakers.csv",
       "encodingFormat": "text/csv",
-      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER"
+      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER",
+      "containedIn": {
+        "@id": "vipbench-bundle"
+      }
     },
     {
       "@type": "cr:FileObject",
@@ -85,7 +99,10 @@
       "description": "Per-pair aggregates for the 9,800 voice pairs.",
       "contentUrl": "data/stimuli.csv",
       "encodingFormat": "text/csv",
-      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER"
+      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER",
+      "containedIn": {
+        "@id": "vipbench-bundle"
+      }
     },
     {
       "@type": "cr:FileObject",
@@ -94,7 +111,10 @@
       "description": "Per-judgment records (124,876 rows).",
       "contentUrl": "data/participant_responses.csv",
       "encodingFormat": "text/csv",
-      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER"
+      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER",
+      "containedIn": {
+        "@id": "vipbench-bundle"
+      }
     },
     {
       "@type": "cr:FileObject",
@@ -103,7 +123,10 @@
       "description": "Type 6 morph trajectory metadata (8,100 rows).",
       "contentUrl": "data/stimuli_interpol.csv",
       "encodingFormat": "text/csv",
-      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER"
+      "sha256": "TO_BE_COMPUTED_BY_PUBLISHER",
+      "containedIn": {
+        "@id": "vipbench-bundle"
+      }
     },
     {
       "@type": "cr:FileSet",
@@ -164,8 +187,12 @@
           "description": "Speaker identifier (e.g., F01, M07).",
           "dataType": "sc:Text",
           "source": {
-            "fileObject": {"@id": "speakers-csv"},
-            "extract": {"column": "id"}
+            "fileObject": {
+              "@id": "speakers-csv"
+            },
+            "extract": {
+              "column": "id"
+            }
           }
         },
         {
@@ -175,8 +202,12 @@
           "description": "Speaker name (public figure).",
           "dataType": "sc:Text",
           "source": {
-            "fileObject": {"@id": "speakers-csv"},
-            "extract": {"column": "name"}
+            "fileObject": {
+              "@id": "speakers-csv"
+            },
+            "extract": {
+              "column": "name"
+            }
           }
         },
         {
@@ -186,8 +217,12 @@
           "description": "Sociophonetic group (1=New York City English, 2=Southern American English, 3=African American English, 4=Latino English, 5=Asian American English).",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "speakers-csv"},
-            "extract": {"column": "group"}
+            "fileObject": {
+              "@id": "speakers-csv"
+            },
+            "extract": {
+              "column": "group"
+            }
           }
         },
         {
@@ -197,8 +232,12 @@
           "description": "Speaker gender (1=male, 2=female).",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "speakers-csv"},
-            "extract": {"column": "gender"}
+            "fileObject": {
+              "@id": "speakers-csv"
+            },
+            "extract": {
+              "column": "gender"
+            }
           }
         },
         {
@@ -208,8 +247,12 @@
           "description": "Speaker age bracket (1=under 45, 2=55 or older).",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "speakers-csv"},
-            "extract": {"column": "age"}
+            "fileObject": {
+              "@id": "speakers-csv"
+            },
+            "extract": {
+              "column": "age"
+            }
           }
         }
       ]
@@ -227,8 +270,12 @@
           "description": "Stimulus identifier (matches the audio basename for the comparison clip).",
           "dataType": "sc:Text",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "id"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "id"
+            }
           }
         },
         {
@@ -238,8 +285,12 @@
           "description": "Stimulus type (1-6). See docs/stimulus_types.md.",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "stimuli_type"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "stimuli_type"
+            }
           }
         },
         {
@@ -249,10 +300,18 @@
           "description": "Reference speaker ID (joins to speakers/id).",
           "dataType": "sc:Text",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "reference"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "reference"
+            }
           },
-          "references": {"field": {"@id": "speakers/id"}}
+          "references": {
+            "field": {
+              "@id": "speakers/id"
+            }
+          }
         },
         {
           "@type": "cr:Field",
@@ -261,8 +320,12 @@
           "description": "Comparison speaker ID for non-Type-6 pairs.",
           "dataType": "sc:Text",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "comparison"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "comparison"
+            }
           }
         },
         {
@@ -272,8 +335,12 @@
           "description": "Whether the comparison clip is an AI voice clone (1) or natural recording (0).",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "voice_clone"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "voice_clone"
+            }
           }
         },
         {
@@ -283,8 +350,12 @@
           "description": "Metadata-label same/different (1=same speaker by metadata, 0=different).",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "correct_answer"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "correct_answer"
+            }
           }
         },
         {
@@ -294,8 +365,12 @@
           "description": "For Type 6 morphs, the interpolation scale (0=reference voice, 100=other voice). 100 for non-morph pairs.",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "scale"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "scale"
+            }
           }
         },
         {
@@ -305,8 +380,12 @@
           "description": "Number of listener judgments collected for this pair.",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "num_response"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "num_response"
+            }
           }
         },
         {
@@ -316,8 +395,12 @@
           "description": "Number of listeners who judged the pair as the same speaker.",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "same_vote"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "same_vote"
+            }
           }
         },
         {
@@ -327,8 +410,12 @@
           "description": "Number of listeners who judged the pair as different speakers.",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "stimuli-csv"},
-            "extract": {"column": "diff_vote"}
+            "fileObject": {
+              "@id": "stimuli-csv"
+            },
+            "extract": {
+              "column": "diff_vote"
+            }
           }
         }
       ]
@@ -346,8 +433,12 @@
           "description": "Pseudonymized listener identifier.",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "responses-csv"},
-            "extract": {"column": "user_id"}
+            "fileObject": {
+              "@id": "responses-csv"
+            },
+            "extract": {
+              "column": "user_id"
+            }
           }
         },
         {
@@ -357,10 +448,18 @@
           "description": "Stimulus identifier (joins to stimuli/id).",
           "dataType": "sc:Text",
           "source": {
-            "fileObject": {"@id": "responses-csv"},
-            "extract": {"column": "stimuli_id"}
+            "fileObject": {
+              "@id": "responses-csv"
+            },
+            "extract": {
+              "column": "stimuli_id"
+            }
           },
-          "references": {"field": {"@id": "stimuli/id"}}
+          "references": {
+            "field": {
+              "@id": "stimuli/id"
+            }
+          }
         },
         {
           "@type": "cr:Field",
@@ -369,8 +468,12 @@
           "description": "Stimulus type (1-6).",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "responses-csv"},
-            "extract": {"column": "stimuli_type"}
+            "fileObject": {
+              "@id": "responses-csv"
+            },
+            "extract": {
+              "column": "stimuli_type"
+            }
           }
         },
         {
@@ -380,8 +483,12 @@
           "description": "Listener's binary same/different judgment (1=same, 0=different).",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "responses-csv"},
-            "extract": {"column": "answer"}
+            "fileObject": {
+              "@id": "responses-csv"
+            },
+            "extract": {
+              "column": "answer"
+            }
           }
         },
         {
@@ -391,8 +498,12 @@
           "description": "Whether the listener's answer matches the metadata label.",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "responses-csv"},
-            "extract": {"column": "correct"}
+            "fileObject": {
+              "@id": "responses-csv"
+            },
+            "extract": {
+              "column": "correct"
+            }
           }
         },
         {
@@ -402,8 +513,12 @@
           "description": "Listener-recognition flag (1=listener identified the reference speaker).",
           "dataType": "sc:Integer",
           "source": {
-            "fileObject": {"@id": "responses-csv"},
-            "extract": {"column": "know_speaker"}
+            "fileObject": {
+              "@id": "responses-csv"
+            },
+            "extract": {
+              "column": "know_speaker"
+            }
           }
         }
       ]
@@ -419,158 +534,61 @@
     {
       "@id": "https://huggingface.co/datasets/sendfuze/vipbench",
       "prov:label": "VIPBench per-speaker source clips (R + A-E variants)",
-      "sc:license": "https://creativecommons.org/licenses/by-nc/4.0/",
-      "sc:description": "For each of 100 US celebrity speakers: one reference clip (R) plus 5 additional source clips (variants A through E). All clips are excerpts of publicly available interview and podcast recordings, curated by the dataset authors under an IRB-approved research protocol. Reference (R) clips are released in data/audio/reference/. Voice clones (stimulus types 3 and 5) are seeded from one of the speaker's A through E source clips; voice morphs (stimulus type 6) interpolate between A through E source clips of two within-group speakers. No aggregated external dataset URI is claimed.",
-      "prov:wasAttributedTo": {
-        "@type": "prov:Agent",
-        "prov:label": "Anonymous (NeurIPS 2026 double-blind review)",
-        "sc:description": "Dataset authors; identity withheld for review."
-      }
+      "description": "For each of 100 US celebrity speakers: one reference clip (R) plus 5 additional source clips (variants A through E). All clips are excerpts of publicly available interview and podcast recordings, curated by the dataset authors under an IRB-approved research protocol. Reference (R) clips are released in data/audio/reference/. Voice clones (stimulus types 3 and 5) are seeded from one of the speaker's A through E source clips; voice morphs (stimulus type 6) interpolate between A through E source clips of two within-group speakers. No aggregated external dataset URI is claimed. Attribution: Anonymous (NeurIPS 2026 double-blind review). Dataset authors; identity withheld for review.",
+      "license": "https://creativecommons.org/licenses/by-nc/4.0/"
     },
     {
       "@id": "https://www.cartesia.ai",
       "prov:label": "Cartesia text-to-speech system",
-      "sc:description": "State-of-the-art commercial TTS system used to synthesize the voice clones (stimulus types 3 and 5) and voice morphs (stimulus type 6). Seed inputs to the synthesis system are the per-speaker source clips described above.",
-      "prov:wasAttributedTo": {
-        "@type": "prov:Agent",
-        "@id": "https://www.cartesia.ai",
-        "prov:label": "Cartesia"
-      }
+      "description": "State-of-the-art commercial TTS system used to synthesize the voice clones (stimulus types 3 and 5) and voice morphs (stimulus type 6). Seed inputs to the synthesis system are the per-speaker source clips described above. Attribution: Cartesia."
     }
   ],
   "prov:wasGeneratedBy": [
     {
       "@type": "prov:Activity",
       "prov:label": "Source-clip collection",
-      "sc:description": "Reference (R) and 5 additional source clips (variants A through E) per speaker were curated from publicly available interview and podcast recordings of 100 US celebrity speakers, stratified across 5 sociophonetic groups (1=NYC English, 2=Southern American English, 3=African American English, 4=Latino English, 5=Asian American English) x 2 genders x 2 age brackets (under 45, 55 or older), 5 speakers per cell. Conducted under IRB approval.",
       "prov:atTime": "2025-2026",
-      "prov:wasAttributedTo": [
-        {
-          "@type": "prov:Agent",
-          "prov:label": "Anonymous (NeurIPS 2026 double-blind review)",
-          "sc:description": "Dataset authors; identity withheld for review."
-        }
-      ]
+      "description": "Reference (R) and 5 additional source clips (variants A through E) per speaker were curated from publicly available interview and podcast recordings of 100 US celebrity speakers, stratified across 5 sociophonetic groups (1=NYC English, 2=Southern American English, 3=African American English, 4=Latino English, 5=Asian American English) x 2 genders x 2 age brackets (under 45, 55 or older), 5 speakers per cell. Conducted under IRB approval. Attribution: Anonymous (NeurIPS 2026 double-blind review) (Dataset authors; identity withheld for review.)."
     },
     {
       "@type": "prov:Activity",
       "prov:label": "Voice-clone synthesis",
-      "sc:description": "Voice clones (stimulus types 3 and 5) generated using Cartesia, a state-of-the-art commercial TTS system, seeded from a natural source clip of the speaker being cloned. Each Type 3 clone shares its seed clip with the comparison clip of the matched Type 2 pair; each Type 5 clone shares its seed clip with the matched Type 4 pair (e.g., 3_F01B is seeded from the same F01B source clip used as the comparison in 2_F01B).",
       "prov:atTime": "2025-2026",
-      "prov:wasAttributedTo": [
-        {
-          "@type": "prov:Agent",
-          "@id": "https://www.cartesia.ai",
-          "prov:label": "Cartesia"
-        }
-      ]
+      "description": "Voice clones (stimulus types 3 and 5) generated using Cartesia, a state-of-the-art commercial TTS system, seeded from a natural source clip of the speaker being cloned. Each Type 3 clone shares its seed clip with the comparison clip of the matched Type 2 pair; each Type 5 clone shares its seed clip with the matched Type 4 pair (e.g., 3_F01B is seeded from the same F01B source clip used as the comparison in 2_F01B). Attribution: Cartesia."
     },
     {
       "@type": "prov:Activity",
       "prov:label": "Voice-morph synthesis",
-      "sc:description": "Voice morphs (stimulus type 6) generated using the voice-morphing feature of the same Cartesia system. For each of the 100 reference speakers, the latent voice representation is interpolated toward each of 4 within-group comparison speakers (matched on sociophonetic group, age group, and gender) at 2 distinct recordings per comparison speaker, sampled at 10 morph scales between 0 and 1, plus 1 anchor at scale 1, yielding 4 x 2 x 10 + 1 = 81 stimuli per reference speaker (8,100 total).",
       "prov:atTime": "2025-2026",
-      "prov:wasAttributedTo": [
-        {
-          "@type": "prov:Agent",
-          "@id": "https://www.cartesia.ai",
-          "prov:label": "Cartesia"
-        }
-      ]
+      "description": "Voice morphs (stimulus type 6) generated using the voice-morphing feature of the same Cartesia system. For each of the 100 reference speakers, the latent voice representation is interpolated toward each of 4 within-group comparison speakers (matched on sociophonetic group, age group, and gender) at 2 distinct recordings per comparison speaker, sampled at 10 morph scales between 0 and 1, plus 1 anchor at scale 1, yielding 4 x 2 x 10 + 1 = 81 stimuli per reference speaker (8,100 total). Attribution: Cartesia."
     },
     {
       "@type": "prov:Activity",
       "prov:label": "Audio preprocessing",
-      "sc:description": "All audio (reference, comparison, clones, morphs) resampled to 16 kHz mono float32 WAV. Clips trimmed to approximately 6 seconds.",
       "prov:atTime": "2025-2026",
-      "prov:wasAttributedTo": [
-        {
-          "@type": "prov:Agent",
-          "prov:label": "Anonymous (NeurIPS 2026 double-blind review)"
-        }
-      ]
+      "description": "All audio (reference, comparison, clones, morphs) resampled to 16 kHz mono float32 WAV. Clips trimmed to approximately 6 seconds. Attribution: Anonymous (NeurIPS 2026 double-blind review)."
     },
     {
       "@type": "prov:Activity",
       "prov:label": "Human annotation (listening study)",
-      "sc:description": "1,290 English-speaking adult crowdworkers recruited via the Centaur AI platform answered binary same/different identity judgments on 9,800 voice pairs. Each pair received at least 8 judgments (median 10, range 8 to 92). Each trial presented reference + 1-second silence + short beep + comparison; listeners answered (a) binary same/different and (b) an optional speaker-recognition probe. Conducted under IRB approval with informed consent.",
       "prov:atTime": "2025-2026",
-      "prov:wasAttributedTo": [
-        {
-          "@type": "prov:Agent",
-          "@id": "https://centaur.ai",
-          "prov:label": "Centaur AI crowdsourcing platform"
-        },
-        {
-          "@type": "prov:Agent",
-          "prov:label": "1,290 adult English-speaking crowdworkers",
-          "sc:description": "Listener identifiers in data/participant_responses.csv are pseudonymized integers tied to no external account."
-        }
-      ]
+      "description": "1,290 English-speaking adult crowdworkers recruited via the Centaur AI platform answered binary same/different identity judgments on 9,800 voice pairs. Each pair received at least 8 judgments (median 10, range 8 to 92). Each trial presented reference + 1-second silence + short beep + comparison; listeners answered (a) binary same/different and (b) an optional speaker-recognition probe. Conducted under IRB approval with informed consent. Attribution: Centaur AI crowdsourcing platform; 1,290 adult English-speaking crowdworkers (Listener identifiers in data/participant_responses.csv are pseudonymized integers tied to no external account.)."
     },
     {
       "@type": "prov:Activity",
       "prov:label": "Aggregation and reliability analysis",
-      "sc:description": "Per-pair P(same) and same/different vote counts computed by aggregating individual responses (data/stimuli.csv). Spearman-Brown corrected split-half reliability rho_SB = 0.705 computed over 100 random splits of the 1,290-participant pool. Per-listener attention-check qualification flags shipped alongside raw responses (data/participant_responses.csv).",
       "prov:atTime": "2025-2026",
-      "prov:wasAttributedTo": [
-        {
-          "@type": "prov:Agent",
-          "prov:label": "Anonymous (NeurIPS 2026 double-blind review)"
-        }
-      ]
+      "description": "Per-pair P(same) and same/different vote counts computed by aggregating individual responses (data/stimuli.csv). Spearman-Brown corrected split-half reliability rho_SB = 0.705 computed over 100 random splits of the 1,290-participant pool. Per-listener attention-check qualification flags shipped alongside raw responses (data/participant_responses.csv). Attribution: Anonymous (NeurIPS 2026 double-blind review)."
     },
     {
       "@type": "prov:Activity",
       "prov:label": "Embedding extraction",
-      "sc:description": "Utterance-level embeddings extracted from each of 9,900 audio files using 10 publicly available pretrained models (x-vector, ECAPA-TDNN, RawNet3, TitaNet, resemblyzer, wav2vec 2.0, HuBERT, WavLM, XLS-R, Whisper). For self-supervised models, both final-layer and per-transformer-layer mean-pooled embeddings are released. Extraction scripts are in code/extract_*.py.",
       "prov:atTime": "2025-2026",
-      "prov:wasAttributedTo": [
-        {
-          "@type": "prov:Agent",
-          "prov:label": "Anonymous (NeurIPS 2026 double-blind review)"
-        }
-      ]
-    }
-  ],
-  "rai:dataCollectionRawData": [
-    "Per-speaker source clips: for each of 100 US celebrity speakers, one reference clip (data/audio/reference/<id>R.wav) plus five additional clips labeled A through E (e.g., F01A, F01B, ..., F01E). All source clips are excerpts of publicly available interview and podcast recordings, curated directly by the dataset authors under an IRB-approved research protocol and resampled to 16 kHz mono float32 WAV. The 100 reference (R) clips are released in data/audio/reference/. The A through E source clips for the speakers being cloned or morphed are not redistributed as standalone files in this release; their identifiers appear in stimulus IDs (e.g., '3_F01B' indicates a Type 3 clone of speaker F01 generated from clip F01B). Speaker metadata is in data/speakers.csv. No aggregated external dataset URI is claimed.",
-    "Voice clones (stimulus types 3 and 5; data/audio/comparison/3_*.wav and 5_*.wav): synthetically generated using the Cartesia text-to-speech system (https://www.cartesia.ai), seeded from a natural source clip of the speaker being cloned. The variant letter in the stimulus ID identifies the seed: a Type 3 clone shares its seed clip with the comparison clip of the matched Type 2 pair, and a Type 5 clone shares its seed with the matched Type 4 pair (e.g., '3_F01B' is seeded from the same F01B source clip used as the comparison in '2_F01B'). Type 3 = same-speaker clone (cloned voice paired with the reference of the same speaker). Type 5 = different-speaker clone (cloned voice paired with the reference of a different speaker).",
-    "Voice morphs (stimulus type 6; data/audio/comparison/6_*.wav): synthetically generated using the voice-morphing feature of the same Cartesia system. For each of the 100 reference speakers, the latent voice representation of the reference speaker is interpolated toward each of 4 within-group comparison speakers (matched on sociophonetic group, age group, and gender), at 2 distinct recordings per comparison speaker, with 10 morph scales between 0 and 1 plus 1 anchor at scale 1, yielding 4 x 2 x 10 + 1 = 81 stimuli per reference speaker (8,100 total). Stimulus IDs encode the two endpoint speakers, the seed-recording variants, and the scale (e.g., '6_M05A_M03A_065' = morph between M05 and M03 with seed recordings A from each, at scale 65). Per-stimulus trajectory metadata (endpoint speakers, seed-recording variants, scale) is in data/stimuli_interpol.csv.",
-    "Pretrained model checkpoints used to compute the embeddings in data/embeddings/ are listed in docs/model_table.md and retain their original licenses; see https://huggingface.co/datasets/sendfuze/vipbench/blob/main/docs/model_table.md."
-  ],
-  "rai:provenanceActivities": [
-    {
-      "@type": "rai:ProvenanceActivity",
-      "name": "Collection",
-      "description": "Per-speaker source clips for 100 US celebrity speakers collected from publicly available interview and podcast recordings: one reference clip (R) plus five additional source clips (A through E) per speaker. Speakers are stratified across 5 sociophonetic groups x 2 genders x 2 age brackets (5 speakers per cell). Curated by the dataset authors under an IRB-approved research protocol."
-    },
-    {
-      "@type": "rai:ProvenanceActivity",
-      "name": "Synthetic generation",
-      "description": "Voice clones (stimulus types 3 and 5) generated by Cartesia TTS, seeded from a natural source clip of the speaker being cloned. The seed clip for a Type 3 clone is the same source clip used as the comparison in the matched Type 2 pair; the seed clip for a Type 5 clone is the same source clip used as the comparison in the matched Type 4 pair. Voice morphs (stimulus type 6) generated using the voice-morphing feature of the same Cartesia system: for each of 100 reference speakers, the latent voice representation is interpolated toward each of 4 within-group comparison speakers (matched on sociophonetic group, age group, and gender) at 2 distinct recordings per comparison speaker, sampled at 10 morph scales between 0 and 1 plus 1 anchor at scale 1, yielding 4 x 2 x 10 + 1 = 81 stimuli per reference speaker (8,100 total)."
-    },
-    {
-      "@type": "rai:ProvenanceActivity",
-      "name": "Preprocessing",
-      "description": "All audio resampled to 16 kHz mono float32 WAV. Clips trimmed to approximately 6 seconds. Speaker IDs assigned in the form F01-F50, M01-M50."
-    },
-    {
-      "@type": "rai:ProvenanceActivity",
-      "name": "Annotation",
-      "description": "1,290 English-speaking adult crowdworkers recruited via the Centaur AI platform answered binary same/different identity judgments on 9,800 voice pairs (median 10 judgments per pair, range 8 to 92). Each trial presented reference + 1-second silence + beep + comparison; listeners answered (a) binary same/different and (b) optional speaker recognition. Conducted under IRB approval with informed consent."
-    },
-    {
-      "@type": "rai:ProvenanceActivity",
-      "name": "Aggregation",
-      "description": "Per-pair P(same) and same/different vote counts computed by aggregating individual responses (data/stimuli.csv). Per-listener attention-check qualification flags computed and shipped alongside raw responses (data/participant_responses.csv). Spearman-Brown corrected split-half reliability rho_SB = 0.705 computed over 100 random splits of the 1,290-participant pool."
-    },
-    {
-      "@type": "rai:ProvenanceActivity",
-      "name": "Embedding extraction",
-      "description": "Utterance-level embeddings extracted from each of 9,900 audio files using 10 publicly available pretrained models (x-vector, ECAPA-TDNN, RawNet3, TitaNet, resemblyzer, wav2vec 2.0, HuBERT, WavLM, XLS-R, Whisper). For self-supervised models, both the final-layer embedding and per-transformer-layer mean-pooled embeddings are released. Extraction scripts are in code/extract_*.py."
+      "description": "Utterance-level embeddings extracted from each of 9,900 audio files using 10 publicly available pretrained models (x-vector, ECAPA-TDNN, RawNet3, TitaNet, resemblyzer, wav2vec 2.0, HuBERT, WavLM, XLS-R, Whisper). For self-supervised models, both final-layer and per-transformer-layer mean-pooled embeddings are released. Extraction scripts are in code/extract_*.py. Attribution: Anonymous (NeurIPS 2026 double-blind review)."
     }
   ],
+  "rai:dataCollectionRawData": "Per-speaker source clips: for each of 100 US celebrity speakers, one reference clip (data/audio/reference/<id>R.wav) plus five additional clips labeled A through E (e.g., F01A, F01B, ..., F01E). All source clips are excerpts of publicly available interview and podcast recordings, curated directly by the dataset authors under an IRB-approved research protocol and resampled to 16 kHz mono float32 WAV. The 100 reference (R) clips are released in data/audio/reference/. The A through E source clips for the speakers being cloned or morphed are not redistributed as standalone files in this release; their identifiers appear in stimulus IDs (e.g., '3_F01B' indicates a Type 3 clone of speaker F01 generated from clip F01B). Speaker metadata is in data/speakers.csv. No aggregated external dataset URI is claimed. | Voice clones (stimulus types 3 and 5; data/audio/comparison/3_*.wav and 5_*.wav): synthetically generated using the Cartesia text-to-speech system (https://www.cartesia.ai), seeded from a natural source clip of the speaker being cloned. The variant letter in the stimulus ID identifies the seed: a Type 3 clone shares its seed clip with the comparison clip of the matched Type 2 pair, and a Type 5 clone shares its seed with the matched Type 4 pair (e.g., '3_F01B' is seeded from the same F01B source clip used as the comparison in '2_F01B'). Type 3 = same-speaker clone (cloned voice paired with the reference of the same speaker). Type 5 = different-speaker clone (cloned voice paired with the reference of a different speaker). | Voice morphs (stimulus type 6; data/audio/comparison/6_*.wav): synthetically generated using the voice-morphing feature of the same Cartesia system. For each of the 100 reference speakers, the latent voice representation of the reference speaker is interpolated toward each of 4 within-group comparison speakers (matched on sociophonetic group, age group, and gender), at 2 distinct recordings per comparison speaker, with 10 morph scales between 0 and 1 plus 1 anchor at scale 1, yielding 4 x 2 x 10 + 1 = 81 stimuli per reference speaker (8,100 total). Stimulus IDs encode the two endpoint speakers, the seed-recording variants, and the scale (e.g., '6_M05A_M03A_065' = morph between M05 and M03 with seed recordings A from each, at scale 65). Per-stimulus trajectory metadata (endpoint speakers, seed-recording variants, scale) is in data/stimuli_interpol.csv. | Pretrained model checkpoints used to compute the embeddings in data/embeddings/ are listed in docs/model_table.md and retain their original licenses; see https://huggingface.co/datasets/sendfuze/vipbench/blob/main/docs/model_table.md.",
+  "rai:provenanceActivities": "[] Per-speaker source clips for 100 US celebrity speakers collected from publicly available interview and podcast recordings: one reference clip (R) plus five additional source clips (A through E) per speaker. Speakers are stratified across 5 sociophonetic groups x 2 genders x 2 age brackets (5 speakers per cell). Curated by the dataset authors under an IRB-approved research protocol. | [] Voice clones (stimulus types 3 and 5) generated by Cartesia TTS, seeded from a natural source clip of the speaker being cloned. The seed clip for a Type 3 clone is the same source clip used as the comparison in the matched Type 2 pair; the seed clip for a Type 5 clone is the same source clip used as the comparison in the matched Type 4 pair. Voice morphs (stimulus type 6) generated using the voice-morphing feature of the same Cartesia system: for each of 100 reference speakers, the latent voice representation is interpolated toward each of 4 within-group comparison speakers (matched on sociophonetic group, age group, and gender) at 2 distinct recordings per comparison speaker, sampled at 10 morph scales between 0 and 1 plus 1 anchor at scale 1, yielding 4 x 2 x 10 + 1 = 81 stimuli per reference speaker (8,100 total). | [] All audio resampled to 16 kHz mono float32 WAV. Clips trimmed to approximately 6 seconds. Speaker IDs assigned in the form F01-F50, M01-M50. | [] 1,290 English-speaking adult crowdworkers recruited via the Centaur AI platform answered binary same/different identity judgments on 9,800 voice pairs (median 10 judgments per pair, range 8 to 92). Each trial presented reference + 1-second silence + beep + comparison; listeners answered (a) binary same/different and (b) optional speaker recognition. Conducted under IRB approval with informed consent. | [] Per-pair P(same) and same/different vote counts computed by aggregating individual responses (data/stimuli.csv). Per-listener attention-check qualification flags computed and shipped alongside raw responses (data/participant_responses.csv). Spearman-Brown corrected split-half reliability rho_SB = 0.705 computed over 100 random splits of the 1,290-participant pool. | [] Utterance-level embeddings extracted from each of 9,900 audio files using 10 publicly available pretrained models (x-vector, ECAPA-TDNN, RawNet3, TitaNet, resemblyzer, wav2vec 2.0, HuBERT, WavLM, XLS-R, Whisper). For self-supervised models, both the final-layer embedding and per-transformer-layer mean-pooled embeddings are released. Extraction scripts are in code/extract_*.py.",
   "rai:dataCollectionMissingValues": "The know_speaker field is missing for some early-trial responses (less than 1% of records). Listeners with fewer than the qualification threshold of attention-check passes are flagged but their responses are still released.",
   "rai:dataCollectionTimeFrame": {
     "@type": "DateTime",
@@ -600,4 +618,4 @@
     "Calibration and uncertainty estimation in speaker verification."
   ],
   "rai:dataBiases": "Speakers are 100 US celebrities; the dataset over-represents US-dialect, professionally-recorded speech. Listeners are English-speaking adult crowdworkers. Generalization to non-English speakers, listeners, or in-the-wild audio conditions is not measured. The 5 sociophonetic groups carry 20 speakers each, limiting subgroup statistical power."
-}
+}