{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/get-mask-from-lengths","entry":"get_mask_from_lengths","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":7,"n_papers_ran":3,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":8,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":1,"unverified":4},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2407.02751","paper":"/paper/emotion-and-intent-joint-understanding-in","title":"Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Dataset","date":"2024-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MC-EIU/MC-EIU","path":"models/networks/ContextEncoder.py","file_url":"https://github.com/MC-EIU/MC-EIU/blob/HEAD/models/networks/ContextEncoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7ac986d61113150b","mcp_get_code":{"code_sha256":"7ac986d61113150b"}},{"arxiv_id":"2403.17694","paper":"/paper/aniportrait-audio-driven-synthesis-of","title":"AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scutzzj/aniportrait","path":"src/audio_models/torch_utils.py","file_url":"https://github.com/scutzzj/aniportrait/blob/HEAD/src/audio_models/torch_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3c0feaae421deaa6","mcp_get_code":{"code_sha256":"3c0feaae421deaa6"}},{"arxiv_id":"2010.11683","paper":"/paper/an-analysis-of-simple-data-augmentation-for","title":"An Analysis of Simple Data Augmentation for Named Entity Recognition","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ariepratama/syntax-driven-da","path":"models.py","file_url":"https://github.com/ariepratama/syntax-driven-da/blob/HEAD/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4c4212144e799a54","mcp_get_code":{"code_sha256":"4c4212144e799a54"}},{"arxiv_id":"2010.04301","paper":"/paper/non-attentive-tacotron-robust-and-1","title":"Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling","date":"2020-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IMDxD/NonAttentiveTacotron","path":"src/model/utils.py","file_url":"https://github.com/IMDxD/NonAttentiveTacotron/blob/HEAD/src/model/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08d6a5b23b6ff882","mcp_get_code":{"code_sha256":"08d6a5b23b6ff882"}},{"arxiv_id":"2006.04558","paper":"/paper/fastspeech-2-fast-and-high-quality-end-to-end","title":"FastSpeech 2: Fast and High-Quality End-to-End Text to Speech","date":"2020-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ndkgit339/fastspeech2-filled_pause_speech_synthesis","path":"model/fastspeech2.py","file_url":"https://github.com/ndkgit339/fastspeech2-filled_pause_speech_synthesis/blob/HEAD/model/fastspeech2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"02b8f4bff1530bdc","mcp_get_code":{"code_sha256":"02b8f4bff1530bdc"}},{"arxiv_id":"2006.04558","paper":"/paper/fastspeech-2-fast-and-high-quality-end-to-end","title":"FastSpeech 2: Fast and High-Quality End-to-End Text to Speech","date":"2020-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KevinMIN95/StyleSpeech","path":"models/VarianceAdaptor.py","file_url":"https://github.com/KevinMIN95/StyleSpeech/blob/HEAD/models/VarianceAdaptor.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3785f1ecaa01e75b","mcp_get_code":{"code_sha256":"3785f1ecaa01e75b"}},{"arxiv_id":"2005.05957","paper":"/paper/flowtron-an-autoregressive-flow-based","title":"Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis","date":"2020-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVIDIA/flowtron","path":"flowtron.py","file_url":"https://github.com/NVIDIA/flowtron/blob/HEAD/flowtron.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"18801918bcbf3ee3","mcp_get_code":{"code_sha256":"18801918bcbf3ee3"}},{"arxiv_id":"1807.03039","paper":"/paper/glow-generative-flow-with-invertible-1x1","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keonlee9420/VAENAR-TTS","path":"model/glow.py","file_url":"https://github.com/keonlee9420/VAENAR-TTS/blob/HEAD/model/glow.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0a48396c7ab55a58","mcp_get_code":{"code_sha256":"0a48396c7ab55a58"}}]}