{"id":"https://openalex.org/W7164575210","doi":"https://doi.org/10.48550/arxiv.2606.12555","title":"AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation","display_name":"AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164575210","doi":"https://doi.org/10.48550/arxiv.2606.12555"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.12555","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12555","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.12555","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138504278","display_name":"Zeyue Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Zeyue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138551342","display_name":"Lei Ke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ke, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138504299","display_name":"Zhaoyang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhaoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138488215","display_name":"Ruibin Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Ruibin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138526635","display_name":"Liumeng Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Liumeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138527958","display_name":"Yujiu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yujiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138488097","display_name":"Weijia Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Weijia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138545181","display_name":"Xu Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138492260","display_name":"Qifeng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Qifeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138510200","display_name":"Wei Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101893660","display_name":"Yi Guo","orcid":"https://orcid.org/0009-0006-1890-6726"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yike","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2199999988079071,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2199999988079071,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.16030000150203705,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11349","display_name":"Music Technology and Sound Studies","score":0.1023000031709671,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.67330002784729},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45989999175071716},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4366999864578247},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4284999966621399},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4027000069618225},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.38019999861717224},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.37549999356269836},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.3626999855041504},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.3596000075340271}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.774399995803833},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.67330002784729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4927000105381012},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4284999966621399},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4027000069618225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3946000039577484},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2687999904155731},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C133162039","wikidata":"https://www.wikidata.org/wiki/Q1061077","display_name":"Code generation","level":3,"score":0.2644999921321869},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.12555","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12555","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.12555","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12555","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6144696474075317,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Audio":[0],"and":[1,31,48,62,97,146,168,178,206,212],"music":[2],"generation":[3,53,199],"based":[4],"on":[5,78,166],"flexible":[6,201],"multimodal":[7,23,57,92,202],"control":[8],"signals":[9],"is":[10,76,98,195],"a":[11,21,46,70,79,84,116,131,142,153],"widely":[12],"applicable":[13],"topic,":[14],"with":[15,83],"the":[16,33,102,125],"following":[17],"key":[18],"challenges:":[19],"1)":[20],"unified":[22,47],"modeling":[24],"framework,":[25],"2)":[26],"large-scale,":[27,132],"high-quality":[28,120,133],"training":[29,126],"data,":[30],"3)":[32],"prohibitive":[34],"inference":[35],"cost":[36],"of":[37,127,156,197],"multi-step":[38,187],"diffusion":[39],"sampling.":[40],"As":[41],"such,":[42],"we":[43,129],"propose":[44],"AudioX-Turbo,":[45,128],"efficient":[49,205],"framework":[50],"for":[51,94,119],"anything-to-audio":[52],"that":[54,89,159,192],"integrates":[55],"varied":[56],"conditions":[58],"(i.e.,":[59],"text,":[60],"video,":[61],"audio":[63,198],"signals)":[64],"in":[65],"this":[66],"work.":[67],"AudioX-Turbo":[68,105,151],"follows":[69],"teacher-student":[71],"paradigm.":[72],"The":[73,210],"teacher":[74],"AudioX-Base":[75],"built":[77],"Multimodal":[80,85],"Diffusion":[81],"Transformer":[82],"Adaptive":[86],"Fusion":[87],"module":[88],"aligns":[90],"diverse":[91],"inputs":[93],"high-fidelity":[95],"synthesis,":[96],"then":[99],"distilled":[100],"into":[101],"few-step":[103,121],"student":[104],"via":[106],"Distribution":[107],"Matching":[108],"Distillation":[109],"adapted":[110],"to":[111],"flow":[112],"matching,":[113],"complemented":[114],"by":[115],"diffusion-based":[117],"discriminator":[118],"generation.":[122],"To":[123],"support":[124],"construct":[130],"dataset,":[134],"IF-caps-Pro,":[135],"comprising":[136],"approximately":[137,180],"9.2M":[138],"samples":[139],"curated":[140],"through":[141],"two-stage":[143],"data":[144],"collection":[145],"annotation":[147],"pipeline.":[148],"We":[149],"benchmark":[150],"across":[152],"wide":[154],"range":[155],"tasks,":[157],"finding":[158],"our":[160,193],"model":[161],"achieves":[162],"superior":[163],"performance,":[164],"especially":[165],"text-to-audio":[167],"text-to-music":[169],"generation,":[170],"while":[171],"operating":[172],"at":[173,217],"only":[174],"4":[175],"sampling":[176],"steps":[177],"requiring":[179],"25x":[181],"fewer":[182],"function":[183],"evaluations":[184],"(NFE)":[185],"than":[186],"baselines.":[188],"These":[189],"results":[190],"demonstrate":[191],"method":[194],"capable":[196],"under":[200],"control,":[203],"showing":[204],"powerful":[207],"instruction-following":[208],"capabilities.":[209],"code":[211],"datasets":[213],"will":[214],"be":[215],"available":[216],"https://zeyuet.github.io/AudioX-Turbo/.":[218]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-13T00:00:00"}