{"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/time-warp","entry":"time_warp","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":5,"n_papers_ran":2,"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":5,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":3},"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":"2310.04673","paper":"/paper/lauragpt-listen-attend-understand-and","title":"LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT","date":"2023-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba-damo-academy/funcodec","path":"funcodec/layers/time_warp.py","file_url":"https://github.com/alibaba-damo-academy/funcodec/blob/HEAD/funcodec/layers/time_warp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a9052885a451088","mcp_get_code":{"code_sha256":"6a9052885a451088"}},{"arxiv_id":"2309.17352","paper":"/paper/improving-audio-captioning-models-with-fine","title":"Improving Audio Captioning Models with Fine-grained Audio Features, Text Embedding Supervision, and LLM Mix-up Augmentation","date":null,"month_inferred_from_arxiv_id":"2023-09","title_source":"archive","repo":"slseanwu/beats-conformer-bart-audio-captioner","path":"model/specaug.py","file_url":"https://github.com/slseanwu/beats-conformer-bart-audio-captioner/blob/HEAD/model/specaug.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":"460bae18834bc026","mcp_get_code":{"code_sha256":"460bae18834bc026"}},{"arxiv_id":"2010.03135","paper":"/paper/representation-learning-for-sequence-data-1","title":"Representation Learning for Sequence Data with Deep Autoencoding Predictive Components","date":"2020-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/DAPC","path":"dapc/spec_augment.py","file_url":"https://github.com/salesforce/DAPC/blob/HEAD/dapc/spec_augment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"362f5ee0833c25ae","mcp_get_code":{"code_sha256":"362f5ee0833c25ae"}},{"arxiv_id":"2005.14327","paper":"/paper/on-the-comparison-of-popular-end-to-end","title":"On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cywang97/StreamingTransformer","path":"espnet/transform/spec_augment.py","file_url":"https://github.com/cywang97/StreamingTransformer/blob/HEAD/espnet/transform/spec_augment.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":"8164158a7a04e80a","mcp_get_code":{"code_sha256":"8164158a7a04e80a"}},{"arxiv_id":"1904.08779","paper":"/paper/specaugment-a-simple-data-augmentation-method","title":"SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition","date":"2019-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DemisEom/SpecAugment","path":"SpecAugment/sparse_image_warp_pytorch.py","file_url":"https://github.com/DemisEom/SpecAugment/blob/HEAD/SpecAugment/sparse_image_warp_pytorch.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":"d94ba36bc01eda60","mcp_get_code":{"code_sha256":"d94ba36bc01eda60"}}]}