{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:QHLBWBFSGS5TYTJLOTWEOFPJV6","short_pith_number":"pith:QHLBWBFS","canonical_record":{"source":{"id":"2110.01900","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-05T09:34:44Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"0cd3c23c074ea850f38f32f94ada545b081beebc9dda7e6c6b9c475ee18ac902","abstract_canon_sha256":"bc5a347dcefff3371f20afc48ca667b703f69462c606205bc01f36fbfcb3fc2a"},"schema_version":"1.0"},"canonical_sha256":"81d61b04b234bb3c4d2b74ec4715e9afba83e9655671a4c150a28ca1927220a5","source":{"kind":"arxiv","id":"2110.01900","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.01900","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"arxiv_version","alias_value":"2110.01900v4","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.01900","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_12","alias_value":"QHLBWBFSGS5T","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_16","alias_value":"QHLBWBFSGS5TYTJL","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_8","alias_value":"QHLBWBFS","created_at":"2026-07-05T04:18:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:QHLBWBFSGS5TYTJLOTWEOFPJV6","target":"record","payload":{"canonical_record":{"source":{"id":"2110.01900","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-05T09:34:44Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"0cd3c23c074ea850f38f32f94ada545b081beebc9dda7e6c6b9c475ee18ac902","abstract_canon_sha256":"bc5a347dcefff3371f20afc48ca667b703f69462c606205bc01f36fbfcb3fc2a"},"schema_version":"1.0"},"canonical_sha256":"81d61b04b234bb3c4d2b74ec4715e9afba83e9655671a4c150a28ca1927220a5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:18:28.381133Z","signature_b64":"WYapdevqXMZoG9hQtIyHRzNrnoZjEkbSW4HwEcIok8iJa84h+P21OnqrMBSOzmG2/keSoIkXZkk8mixH9BtKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81d61b04b234bb3c4d2b74ec4715e9afba83e9655671a4c150a28ca1927220a5","last_reissued_at":"2026-07-05T04:18:28.380653Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:18:28.380653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.01900","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:18:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qmBAPauTELiayLRLPCrlbabodaYJvCaoHaT4WGvO5we/mqpT2QgQmDtc36aCB51kFzyPrk4SjOGMunSwZEl5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:15:16.508240Z"},"content_sha256":"c4dc7b43215fdbb7d5d4d9bd039c3813d9be1b82d91845b8d767b4352cdfed7d","schema_version":"1.0","event_id":"sha256:c4dc7b43215fdbb7d5d4d9bd039c3813d9be1b82d91845b8d767b4352cdfed7d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:QHLBWBFSGS5TYTJLOTWEOFPJV6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.CL","authors_text":"Heng-Jui Chang, Hung-yi Lee, Shu-wen Yang","submitted_at":"2021-10-05T09:34:44Z","abstract_excerpt":"Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while ret"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.01900","kind":"arxiv","version":4},"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/2110.01900/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":"2026-07-05T04:18:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YUtkYsZhRaZHGx+NqsecuIRUFCq8lhfcnvByu7qRyk63dSMaDWe7lxsIscQYFGYBbMkPRwwNXWw8nteyaFkqAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:15:16.508626Z"},"content_sha256":"ea97456495d139ffdd555c900b098ca3c34530f63d1776f89bba193d240ea345","schema_version":"1.0","event_id":"sha256:ea97456495d139ffdd555c900b098ca3c34530f63d1776f89bba193d240ea345"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QHLBWBFSGS5TYTJLOTWEOFPJV6/bundle.json","state_url":"https://pith.science/pith/QHLBWBFSGS5TYTJLOTWEOFPJV6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QHLBWBFSGS5TYTJLOTWEOFPJV6/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":"2026-07-06T17:15:16Z","links":{"resolver":"https://pith.science/pith/QHLBWBFSGS5TYTJLOTWEOFPJV6","bundle":"https://pith.science/pith/QHLBWBFSGS5TYTJLOTWEOFPJV6/bundle.json","state":"https://pith.science/pith/QHLBWBFSGS5TYTJLOTWEOFPJV6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QHLBWBFSGS5TYTJLOTWEOFPJV6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:QHLBWBFSGS5TYTJLOTWEOFPJV6","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":"bc5a347dcefff3371f20afc48ca667b703f69462c606205bc01f36fbfcb3fc2a","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-05T09:34:44Z","title_canon_sha256":"0cd3c23c074ea850f38f32f94ada545b081beebc9dda7e6c6b9c475ee18ac902"},"schema_version":"1.0","source":{"id":"2110.01900","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.01900","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"arxiv_version","alias_value":"2110.01900v4","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.01900","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_12","alias_value":"QHLBWBFSGS5T","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_16","alias_value":"QHLBWBFSGS5TYTJL","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_8","alias_value":"QHLBWBFS","created_at":"2026-07-05T04:18:28Z"}],"graph_snapshots":[{"event_id":"sha256:ea97456495d139ffdd555c900b098ca3c34530f63d1776f89bba193d240ea345","target":"graph","created_at":"2026-07-05T04:18:28Z","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/2110.01900/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while ret","authors_text":"Heng-Jui Chang, Hung-yi Lee, Shu-wen Yang","cross_cats":["eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-05T09:34:44Z","title":"DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.01900","kind":"arxiv","version":4},"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:c4dc7b43215fdbb7d5d4d9bd039c3813d9be1b82d91845b8d767b4352cdfed7d","target":"record","created_at":"2026-07-05T04:18:28Z","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":"bc5a347dcefff3371f20afc48ca667b703f69462c606205bc01f36fbfcb3fc2a","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-05T09:34:44Z","title_canon_sha256":"0cd3c23c074ea850f38f32f94ada545b081beebc9dda7e6c6b9c475ee18ac902"},"schema_version":"1.0","source":{"id":"2110.01900","kind":"arxiv","version":4}},"canonical_sha256":"81d61b04b234bb3c4d2b74ec4715e9afba83e9655671a4c150a28ca1927220a5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81d61b04b234bb3c4d2b74ec4715e9afba83e9655671a4c150a28ca1927220a5","first_computed_at":"2026-07-05T04:18:28.380653Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:18:28.380653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WYapdevqXMZoG9hQtIyHRzNrnoZjEkbSW4HwEcIok8iJa84h+P21OnqrMBSOzmG2/keSoIkXZkk8mixH9BtKDg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:18:28.381133Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.01900","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c4dc7b43215fdbb7d5d4d9bd039c3813d9be1b82d91845b8d767b4352cdfed7d","sha256:ea97456495d139ffdd555c900b098ca3c34530f63d1776f89bba193d240ea345"],"state_sha256":"2f59f8da4f5b9f8ab42ad9f7e12e6f9b6c3b33d000034729ce675af940cfcc7f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mgFaXKcKE9D71mLJwfsDv6KogNYwAlyDHpnS3lhD/GlqJK5kjqjE/J9oEj1Ieefof0I5mJzuvhOUhi6msEPsDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T17:15:16.510647Z","bundle_sha256":"856c7b729466c1e1641df78120f224c311d571664e9fa1c46dc0d68ce04b360d"}}