{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:HHR5FE7SARWFQGATADASY7UXGP","short_pith_number":"pith:HHR5FE7S","canonical_record":{"source":{"id":"1909.11646","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-09-25T17:47:49Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"94d283f9d7b6390ab1ceebceaa5ac78cf3b1fba6bda4ae5dd2f626c0db3cff20","abstract_canon_sha256":"324240ae52f81c8802c0eeacf068e2db87df82f8bc6a349ae99613e2e8a25547"},"schema_version":"1.0"},"canonical_sha256":"39e3d293f2046c58181300c12c7e9733f89765a1a5c026fd6b72a21bab2a1e8c","source":{"kind":"arxiv","id":"1909.11646","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.11646","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"arxiv_version","alias_value":"1909.11646v2","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.11646","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_12","alias_value":"HHR5FE7SARWF","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_16","alias_value":"HHR5FE7SARWFQGAT","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_8","alias_value":"HHR5FE7S","created_at":"2026-07-05T00:07:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:HHR5FE7SARWFQGATADASY7UXGP","target":"record","payload":{"canonical_record":{"source":{"id":"1909.11646","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-09-25T17:47:49Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"94d283f9d7b6390ab1ceebceaa5ac78cf3b1fba6bda4ae5dd2f626c0db3cff20","abstract_canon_sha256":"324240ae52f81c8802c0eeacf068e2db87df82f8bc6a349ae99613e2e8a25547"},"schema_version":"1.0"},"canonical_sha256":"39e3d293f2046c58181300c12c7e9733f89765a1a5c026fd6b72a21bab2a1e8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:07:37.963647Z","signature_b64":"oOKmu/NCsU7leqD1CdXyThZleVZtgvPWvH6dHK/kQXpcTYjWRdxWFz4O7S5CdnU3uHjKFNiXQ3HHCM18+kJEDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39e3d293f2046c58181300c12c7e9733f89765a1a5c026fd6b72a21bab2a1e8c","last_reissued_at":"2026-07-05T00:07:37.963207Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:07:37.963207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.11646","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:07:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"11iZ15qNBsL9F5DGC4kqjWPN5cTmYzhTeDgjb0rcffV0TnJjIbbio3LzzwRy2dT19G/XyMY16AtPZvc/53feBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T15:35:33.393771Z"},"content_sha256":"e4dcb84def987982b3166bab2eed5461fecc4723d44aec63424710d398536f53","schema_version":"1.0","event_id":"sha256:e4dcb84def987982b3166bab2eed5461fecc4723d44aec63424710d398536f53"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:HHR5FE7SARWFQGATADASY7UXGP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High Fidelity Speech Synthesis with Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Aidan Clark, Erich Elsen, Jeff Donahue, Karen Simonyan, Luis C. Cobo, Miko{\\l}aj Bi\\'nkowski, Norman Casagrande, Sander Dieleman","submitted_at":"2019-09-25T17:47:49Z","abstract_excerpt":"Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech. Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators whic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.11646","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.11646/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:07:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hoBMpRiud/7c6ItfXe9y/StKjybp6k/umrgUj2xVOe/hE2wrJxqXoTw+7+uYWmot6xiC95/xYVBKv+GDHRNzDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T15:35:33.394153Z"},"content_sha256":"d5d63e0169e3072174dabce27d6daa21be690571ef1c7b9716d7090dc8eb8930","schema_version":"1.0","event_id":"sha256:d5d63e0169e3072174dabce27d6daa21be690571ef1c7b9716d7090dc8eb8930"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HHR5FE7SARWFQGATADASY7UXGP/bundle.json","state_url":"https://pith.science/pith/HHR5FE7SARWFQGATADASY7UXGP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HHR5FE7SARWFQGATADASY7UXGP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-16T15:35:33Z","links":{"resolver":"https://pith.science/pith/HHR5FE7SARWFQGATADASY7UXGP","bundle":"https://pith.science/pith/HHR5FE7SARWFQGATADASY7UXGP/bundle.json","state":"https://pith.science/pith/HHR5FE7SARWFQGATADASY7UXGP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HHR5FE7SARWFQGATADASY7UXGP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:HHR5FE7SARWFQGATADASY7UXGP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"324240ae52f81c8802c0eeacf068e2db87df82f8bc6a349ae99613e2e8a25547","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-09-25T17:47:49Z","title_canon_sha256":"94d283f9d7b6390ab1ceebceaa5ac78cf3b1fba6bda4ae5dd2f626c0db3cff20"},"schema_version":"1.0","source":{"id":"1909.11646","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.11646","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"arxiv_version","alias_value":"1909.11646v2","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.11646","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_12","alias_value":"HHR5FE7SARWF","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_16","alias_value":"HHR5FE7SARWFQGAT","created_at":"2026-07-05T00:07:37Z"},{"alias_kind":"pith_short_8","alias_value":"HHR5FE7S","created_at":"2026-07-05T00:07:37Z"}],"graph_snapshots":[{"event_id":"sha256:d5d63e0169e3072174dabce27d6daa21be690571ef1c7b9716d7090dc8eb8930","target":"graph","created_at":"2026-07-05T00:07:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.11646/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech. Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators whic","authors_text":"Aidan Clark, Erich Elsen, Jeff Donahue, Karen Simonyan, Luis C. Cobo, Miko{\\l}aj Bi\\'nkowski, Norman Casagrande, Sander Dieleman","cross_cats":["cs.LG","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-09-25T17:47:49Z","title":"High Fidelity Speech Synthesis with Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.11646","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e4dcb84def987982b3166bab2eed5461fecc4723d44aec63424710d398536f53","target":"record","created_at":"2026-07-05T00:07:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"324240ae52f81c8802c0eeacf068e2db87df82f8bc6a349ae99613e2e8a25547","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-09-25T17:47:49Z","title_canon_sha256":"94d283f9d7b6390ab1ceebceaa5ac78cf3b1fba6bda4ae5dd2f626c0db3cff20"},"schema_version":"1.0","source":{"id":"1909.11646","kind":"arxiv","version":2}},"canonical_sha256":"39e3d293f2046c58181300c12c7e9733f89765a1a5c026fd6b72a21bab2a1e8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"39e3d293f2046c58181300c12c7e9733f89765a1a5c026fd6b72a21bab2a1e8c","first_computed_at":"2026-07-05T00:07:37.963207Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:07:37.963207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oOKmu/NCsU7leqD1CdXyThZleVZtgvPWvH6dHK/kQXpcTYjWRdxWFz4O7S5CdnU3uHjKFNiXQ3HHCM18+kJEDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:07:37.963647Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.11646","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4dcb84def987982b3166bab2eed5461fecc4723d44aec63424710d398536f53","sha256:d5d63e0169e3072174dabce27d6daa21be690571ef1c7b9716d7090dc8eb8930"],"state_sha256":"f3fb799946ce9f3395399805f965f171ab701a9ecf7fe194a0bb1bbaca68ad80"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wgYQHoU2tgfmo0ZgVTejE2HE72E61n7AWpGr6e8jixPJ/qWX5ZxyRoDtQrrgfbIBbn++WJimbDWhv2cgD8whCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-16T15:35:33.396719Z","bundle_sha256":"c7740ed0281eb18525958f01983c94d8931ffc9acaaad212ce90a9e97d382e8a"}}