[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-c09281106c1de2ef-ibm-granite-speech-4-1-3-asr-models-for-accuracy-f-summary":3,"summaries-facets-categories":211,"summary-related-c09281106c1de2ef-ibm-granite-speech-4-1-3-asr-models-for-accuracy-f-summary":3796},{"id":4,"title":5,"ai":6,"body":13,"categories":164,"created_at":166,"date_modified":166,"description":158,"extension":167,"faq":166,"featured":168,"kicker_label":166,"meta":169,"navigation":193,"path":194,"published_at":195,"question":166,"scraped_at":196,"seo":197,"sitemap":198,"source_id":199,"source_name":186,"source_type":200,"source_url":201,"stem":202,"tags":203,"thumbnail_url":166,"tldr":208,"tweet":166,"unknown_tags":209,"__hash__":210},"summaries\u002Fsummaries\u002Fc09281106c1de2ef-ibm-granite-speech-4-1-3-asr-models-for-accuracy-f-summary.md","IBM Granite Speech 4.1: 3 ASR Models for Accuracy, Features, Speed",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6601,1943,19579,0.00178485,{"type":14,"value":15,"toc":157},"minimark",[16,21,25,28,31,123,127,130,134,154],[17,18,20],"h2",{"id":19},"select-granite-41-variant-by-your-asr-bottleneck","Select Granite 4.1 Variant by Your ASR Bottleneck",[22,23,24],"p",{},"IBM's Granite Speech 4.1 releases three ~2B parameter models optimized for edge deployment, each targeting a specific constraint: accuracy, structured output, or throughput. Use the base model (ibm\u002Fgranite-speech-4.1-2b) for top accuracy—it leads the Hugging Face Open ASR Leaderboard with 5.33% word error rate (WER) across diverse datasets, translating to ~95% word accuracy in real-world scenarios. Its real-time factor (RTF) reaches 231, processing 4 minutes of audio per second of compute (e.g., 1-hour audio in 16 seconds). Supports 7 languages (English, French, German, Spanish, Portuguese, Japanese) for transcription, bidirectional speech-to-text translation, punctuation, truecasing, and keyword biasing—pass domain-specific terms like names or acronyms in the prompt to boost recognition.",[22,26,27],{},"Switch to the Plus variant (ibm\u002Fgranite-speech-4.1-2b-plus) for speaker-attributed ASR (diarization) and word-level timestamps. It labels speakers (e.g., Speaker 1, Speaker 2) for podcasts or meetings, with timestamp accuracy outperforming Whisper-X and customized Whisper models. Incremental decoding lets you prefix prior transcripts for seamless long-audio chunking with overlap, maintaining consistent speaker IDs. Trade-offs: WER rises slightly, drops to 5 languages (no Japanese), no translation or keyword biasing.",[22,29,30],{},"For bulk processing, pick the NAR model (ibm\u002Fgranite-speech-4.1-2b-nar)—non-autoregressive design skips sequential token generation, achieving RTF 1820 batched on H100 (1-hour audio in 2 seconds). No diarization, timestamps, translation, or biasing, but WER stays competitive.",[32,33,34,59],"table",{},[35,36,37],"thead",{},[38,39,40,44,47,50,53,56],"tr",{},[41,42,43],"th",{},"Model",[41,45,46],{},"Key Strengths",[41,48,49],{},"WER",[41,51,52],{},"RTF",[41,54,55],{},"Languages",[41,57,58],{},"Features",[60,61,62,83,103],"tbody",{},[38,63,64,68,71,74,77,80],{},[65,66,67],"td",{},"Base",[65,69,70],{},"Accuracy",[65,72,73],{},"5.33%",[65,75,76],{},"231",[65,78,79],{},"7",[65,81,82],{},"Translation, keyword bias",[38,84,85,88,91,94,97,100],{},[65,86,87],{},"Plus",[65,89,90],{},"Diarization, timestamps",[65,92,93],{},"Higher",[65,95,96],{},"Lower",[65,98,99],{},"5",[65,101,102],{},"Incremental decode",[38,104,105,108,111,114,117,120],{},[65,106,107],{},"NAR",[65,109,110],{},"Throughput",[65,112,113],{},"Competitive",[65,115,116],{},"1820 (H100)",[65,118,119],{},"?",[65,121,122],{},"Raw transcripts",[17,124,126],{"id":125},"non-autoregressive-transcript-editing-beats-sequential-decoding","Non-Autoregressive Transcript Editing Beats Sequential Decoding",[22,128,129],{},"Standard ASR like Whisper or Parakeet uses autoregressive transformers, generating tokens sequentially—each depends on priors, bottlenecking GPUs with tiny forward passes. NAR fixes this via NLE (Non-autoregressive LLM-based editing): a cheap CTC encoder drafts a bidirectional-attention transcript, then an LLM edits it (copy, insert, delete, replace). This parallelizes decoding without losing conditioning, improving on one-shot predictions. Result: massive speedups without huge WER hits, ideal for hundreds of hours of raw audio.",[17,131,133],{"id":132},"run-locally-with-transformers-chunking-and-fine-tuning-tips","Run Locally with Transformers: Chunking and Fine-Tuning Tips",[22,135,136,137,141,142,145,146,149,150,153],{},"Load via Hugging Face Transformers: ",[138,139,140],"code",{},"processor = AutoProcessor.from_pretrained(model_id); model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id)",". Use ",[138,143,144],{},"generate()"," with custom prompts for diarization (",[138,147,148],{},"\u003C|startoftranscript|>\u003C|en|>\u003C|transcribe|>\u003C|speaker_attributed_asr|>",") or keywords (",[138,151,152],{},"\u003C|startoftranscript|>\u003C|en|>\u003C|transcribe|>\u003C|keywords|>[\"term1\", \"term2\"]\u003C|endkeywords|>","). Requires Flash Attention for NAR (compile for CUDA 13+; issues on T4 Colab GPUs).",[22,155,156],{},"For long audio (e.g., 4-hour podcasts): chunk with overlap, prefix prior text for continuity. Fine-tune on domain data like court transcripts or podcasts using prior Granite notebooks—train on host-specific accents for better WER. Test RTF varies by hardware (RTX 6000 Blackwell hits good speeds but below H100 claims without batching). Build local agents to query via API for cloud-free transcription.",{"title":158,"searchDepth":159,"depth":159,"links":160},"",2,[161,162,163],{"id":19,"depth":159,"text":20},{"id":125,"depth":159,"text":126},{"id":132,"depth":159,"text":133},[165],"AI & LLMs",null,"md",false,{"content_references":170,"triage":188},[171,177,181,184],{"type":172,"title":173,"publisher":174,"url":175,"context":176},"report","Granite 4.1 AI Foundation Models","IBM Research","https:\u002F\u002Fresearch.ibm.com\u002Fblog\u002Fgranite-4-1-ai-foundation-models","cited",{"type":178,"title":179,"context":180},"paper","NLE: Non-autoregressive LLM-based ASR by Transcript Editing","mentioned",{"type":182,"title":183,"context":180},"other","Granite Speech Model Github",{"type":182,"title":185,"author":186,"url":187,"context":180},"llm-tutorials","Sam Witteveen","https:\u002F\u002Fgithub.com\u002Fsamwit\u002Fllm-tutorials",{"relevance":189,"novelty":159,"quality":190,"actionability":189,"composite":191,"reasoning":192},3,4,3.05,"Category: AI & LLMs. The article discusses IBM's Granite Speech 4.1 models, which are relevant to AI-powered product builders interested in speech recognition technology. While it provides some technical details, it lacks actionable insights on how to implement these models in real-world applications.",true,"\u002Fsummaries\u002Fc09281106c1de2ef-ibm-granite-speech-4-1-3-asr-models-for-accuracy-f-summary","2026-05-07 13:40:02","2026-05-07 16:37:55",{"title":5,"description":158},{"loc":194},"a46a387d67c4fcca","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Tymq54Mn8SU","summaries\u002Fc09281106c1de2ef-ibm-granite-speech-4-1-3-asr-models-for-accuracy-f-summary",[204,205,206,207],"ai-tools","python","open-source","ai-llms","IBM's 2B Granite Speech 4.1 suite offers three trade-offs: base leads Open ASR Leaderboard (WER 5.33, RTF 231), Plus adds diarization\u002Ftimestamps, NAR hits RTF 1820 on H100 via transcript editing.",[207],"dF2uBIfxfrOwqTG70jiimSEB82hxedQZTBWqe0_5R-8",[212,215,218,220,223,226,228,230,232,234,236,238,241,243,245,247,249,251,253,255,257,259,262,265,267,269,272,274,276,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2298,2300,2302,2304,2306,2308,2310,2312,2314,2316,2318,2320,2322,2324,2326,2328,2330,2332,2334,2336,2338,2340,2342,2344,2346,2348,2350,2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Code Leak Reveals Sloppy Code and Risks",{"provider":7,"model":8,"input_tokens":3801,"output_tokens":3802,"processing_time_ms":3803,"cost_usd":3804},5902,1452,16294,0.00143915,{"type":14,"value":3806,"toc":3835},[3807,3811,3814,3818,3821,3825,3828,3832],[17,3808,3810],{"id":3809},"accidental-npm-publish-exposes-500k-lines-of-code","Accidental NPM Publish Exposes 500K Lines of Code",[22,3812,3813],{},"Anthropic's Claude Code—touted as a solved coding tool—leaked its entire 500,000-line codebase across 1,900 files via source maps on NPM. Source maps unminify production JavaScript, revealing original variable names and logic. This stemmed from an unaddressed GitHub issue in their acquired JS runtime (Bun): a frontend dev server served source maps in production, reported 3 weeks prior, dismissed as duplicate, and ignored despite follow-ups. Impact: Public access to internals invites reverse-engineering, with researchers already spotting exploits. Previously, Anthropic DMCA'd similar leaks and enforces ToS violations harshly, so avoid downloading or republishing to dodge legal trouble—GPL licenses won't protect you, as they train on open code anyway.",[17,3815,3817],{"id":3816},"hardcoded-hacks-over-ai-sophistication","Hardcoded Hacks Over AI Sophistication",[22,3819,3820],{},"Despite wielding advanced LLMs, Claude Code resorts to 2005-era tricks. Sentiment analysis scans prompts for profanity like 'dumbass', 'piss', 'damn it', or 'this sucks' via a hardcoded regex whitelist—forgoing model-based detection for simplicity. Skills like 'cyber risk instructions' are handcrafted strings by the safety team, embedded client-side with comments warning devs not to edit without approval from David or Kyla. 'Don't blow your cover' mode hides Anthropic employee usage in public repos: no 'Claude Code' mentions, AI attributions, or co-authored lines. These expose rushed, non-scalable engineering that prioritizes speed over robustness, confirming ChatGPT's 'staff-level spaghetti' critique.",[17,3822,3824],{"id":3823},"gamified-features-signal-misdirected-priorities","Gamified Features Signal Misdirected Priorities",[22,3826,3827],{},"Claude Code embeds a terminal Tamagotchi\u002FPokémon-style buddy system, planned for April 1-7 release (possibly ongoing). Collect 'legendary' pets like Cosmos Hail or Nebu Lynx with 'shiny' rarities—evoking NFTs more than productivity tools. This elder-millennial bait diverts from core utility, highlighting AI labs' gimmickry over substance. Client-side secrets amplify risks: 'claude mcp get name' command dumps MCP server URLs, headers, OAuth hints, env vars, and stdin\u002Fstdout server details—leaking AWS\u002FGemini credentials if present. Kro (likely a dep) can't escalate beyond prod takedowns, but over 6 months, expect targeted exploits from this 'vibe-coded' base.",[17,3829,3831],{"id":3830},"tos-hypocrisy-threatens-builders","ToS Hypocrisy Threatens Builders",[22,3833,3834],{},"Anthropic's ToS bans using Claude for 'competing products'—vaguely covering always-on bots, remote planning, memory caching, or multi-agent orchestration, all features they're building. Success risks lawsuits, as they've historically abused clauses against users while training on their GPL'd code (85-95% recallable from weights). Leaks like a Claude-generated PR to open-source itself underscore irony. Builders: Weigh this against lock-in; leaks erode trust, amplifying supply-chain vulnerabilities (e.g., Axios-style attacks) and turning users into 'safety liabilities'.",{"title":158,"searchDepth":159,"depth":159,"links":3836},[3837,3838,3839,3840],{"id":3809,"depth":159,"text":3810},{"id":3816,"depth":159,"text":3817},{"id":3823,"depth":159,"text":3824},{"id":3830,"depth":159,"text":3831},[240],"Having trouble finding the right developer for your team? Get a 7-day free trial + $1,500 off with The Prime’s discount. https:\u002F\u002Ftrm.sh\u002Fg2i\nAttending AIE Miami in April? Use code Prime50Off - https:\u002F\u002Ftrm.sh\u002FAIE\n\n### Sources\n- https:\u002F\u002Fx.com\u002Fwesbos\u002Fstatus\u002F2038961138130432382\n- https:\u002F\u002Fgithub.com\u002FKuberwastaken\u002Fclaude-code?tab=readme-ov-file#buddy---a-tamagotchi-inside-your-terminal\n- https:\u002F\u002Fx.com\u002Fpaoloanzn\u002Fstatus\u002F2038944622039413224\n- https:\u002F\u002Fgithub.com\u002FKuberwastaken\u002Fclaude-code?tab=readme-ov-file#the-system-prompt-architecture\n- https:\u002F\u002Fgitlawb.com\u002Fnode\u002Frepos\u002Fz6MkgKkb\u002Finstructkr-claude-code\n- https:\u002F\u002Fgithub.com\u002FKuberwastaken\u002Fclaude-code?tab=readme-ov-file#undercover-mode---do-not-blow-your-cover\n- https:\u002F\u002Fx.com\u002FYuchenj_UW\u002Fstatus\u002F2038996920845430815\n- https:\u002F\u002Fgithub.com\u002Fgithub\u002Fdmca\u002Fblob\u002Fmaster\u002F2025\u002F03\u002F2025-03-10-anthropic.md\n- https:\u002F\u002Fx.com\u002FGergelyOrosz\u002Fstatus\u002F2038985760175505491\n\nhttps:\u002F\u002Ftwitch.tv\u002FThePrimeagen - I Stream on Twitch\n\nhttps:\u002F\u002Ftwitter.com\u002Fterminaldotshop - Want to order coffee over SSH?\nssh terminal.shop\n\nBecome Backend Dev: https:\u002F\u002Fboot.dev\u002Fprime\n(plus i make courses for them)\n\nThis is also the best way to support me is to support yourself becoming a better backend engineer.  \n\nGreat News?  Want me to research and create video????: https:\u002F\u002Fwww.reddit.com\u002Fr\u002FThePrimeagen\n\nKinesis Advantage 360: https:\u002F\u002Fbit.ly\u002FPrime-Kinesis",{},"\u002Fsummaries\u002F1315617d984805fc-claude-code-leak-reveals-sloppy-code-and-risks-summary","2026-04-01 03:20:11","2026-04-03 21:18:26",{"title":3799,"description":3842},{"loc":3844},"1315617d984805fc","The PrimeTime","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GdgRpiQRsis","summaries\u002F1315617d984805fc-claude-code-leak-reveals-sloppy-code-and-risks-summary",[204,206,207],"Anthropic accidentally published full Claude Code source maps on NPM, exposing hardcoded sentiment detection via profanity lists, security flaws like credential leaks, and ToS hypocrisy on code usage.",[207],"th1RQ4u6qfGGsmd9bm8UbgLe4XSV3raNZbBbBdYC1pg",{"id":3859,"title":3860,"ai":3861,"body":3866,"categories":4009,"created_at":166,"date_modified":166,"description":158,"extension":167,"faq":166,"featured":168,"kicker_label":166,"meta":4010,"navigation":193,"path":4035,"published_at":4036,"question":166,"scraped_at":4037,"seo":4038,"sitemap":4039,"source_id":4040,"source_name":4041,"source_type":200,"source_url":4042,"stem":4043,"tags":4044,"thumbnail_url":166,"tldr":4046,"tweet":166,"unknown_tags":4047,"__hash__":4048},"summaries\u002Fsummaries\u002F00328a14a70095c4-build-vibevoice-speech-pipelines-in-colab-summary.md","Build VibeVoice Speech Pipelines in Colab",{"provider":7,"model":8,"input_tokens":3862,"output_tokens":3863,"processing_time_ms":3864,"cost_usd":3865},9212,2845,29040,0.00324225,{"type":14,"value":3867,"toc":4003},[3868,3872,3906,3940,3944,3960,3967,3971,3974,3981,3985,4000],[17,3869,3871],{"id":3870},"setup-vibevoice-environment-for-instant-asr-and-tts","Setup VibeVoice Environment for Instant ASR and TTS",[22,3873,3874,3875,3878,3879,3885,3886,3889,3890,3893,3894,3897,3898,3901,3902,3905],{},"Install via ",[138,3876,3877],{},"!pip install git+https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftransformers.git"," plus torch, gradio, and clone ",[3880,3881,3882],"a",{"href":3882,"rel":3883},"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FVibeVoice",[3884],"nofollow",". Restart runtime after editable install ",[138,3887,3888],{},"-e \u002Fcontent\u002FVibeVoice",". Load 7B ASR (",[138,3891,3892],{},"microsoft\u002FVibeVoice-ASR-HF",", ~14GB download, float16 on auto device) and 0.5B TTS (",[138,3895,3896],{},"microsoft\u002FVibeVoice-Realtime-0.5B",", set DDPM steps to 20). Use ",[138,3899,3900],{},"AutoProcessor"," for ASR inputs and ",[138,3903,3904],{},"VibeVoiceTextTokenizerFast"," for TTS. This enables 50+ languages, single-pass 60min transcription, and ~300ms streaming latency from ultra-low 7.5Hz tokenizers combining LLM context with diffusion audio gen.",[22,3907,3908,3909,3912,3913,3916,3917,3920,3921,3924,3925,3928,3929,3931,3932,3935,3936,3939],{},"Key ",[138,3910,3911],{},"transcribe(audio_path, context=None)"," wraps ",[138,3914,3915],{},"apply_transcription_request"," then ",[138,3918,3919],{},"generate"," and ",[138,3922,3923],{},"decode"," (formats: 'parsed', 'transcription_only'). For TTS, ",[138,3926,3927],{},"synthesize(text, voice=\"Grace\", cfg_scale=3.0, steps=20)"," uses ",[138,3930,3919],{}," with ",[138,3933,3934],{},"return_speech=True",", ",[138,3937,3938],{},"speaker_name",", outputs 24kHz numpy audio—save via soundfile.",[17,3941,3943],{"id":3942},"unlock-asr-precision-with-speakers-context-and-batches","Unlock ASR Precision with Speakers, Context, and Batches",[22,3945,3946,3947,3951,3952,3955,3956,3959],{},"Achieve speaker diarization on podcasts: parsed output yields list of dicts with 'Speaker', 'Start\u002FEnd' timestamps (s), 'Content'—e.g., ",[3948,3949,3950],"span",{},"Speaker 1"," 0.00s-5.23s: \"Hello...\". Context prompts fix hotwords: German sample mishears without ",[138,3953,3954],{},"context=\"About VibeVoice\"",", correctly IDs \"VibeVoice\" with it. Batch multiple audios: ",[138,3957,3958],{},"apply_transcription_request(audio=[path1,path2], prompt=[ctx1,None])"," generates all at once, decode to list of texts—scales for pipelines without loops.",[22,3961,3962,3963,3966],{},"Trade-offs: Long audio risks OOM; mitigate with ",[138,3964,3965],{},"acoustic_tokenizer_chunk_size=64000"," in generate or bfloat16 dtype. Handles MP3\u002FWAV\u002FFLAC uploads via Colab files.",[17,3968,3970],{"id":3969},"craft-expressive-tts-voices-cfg-and-long-form-scaling","Craft Expressive TTS: Voices, CFG, and Long-Form Scaling",[22,3972,3973],{},"Four presets (Carter, Grace, Emma, Davis) yield distinct styles—compare same text across voices for prosody variety. CFG scale 1-5 controls adherence (3.0 default natural), steps 5-50 trade quality\u002Fspeed (15 fast demo, 25 long-form). Generates 10min+ coherent speech: podcast script (~200 words) to 45s audio at cfg=3.5\u002Fsteps=25. Next-token diffusion ensures pauses, intonation unlike rigid TTS.",[22,3975,3976,3977,3980],{},"Real-time viable: low-param model on CUDA\u002FCPU. Gradio UI exposes text, voice dropdown, sliders for cfg\u002Fsteps—",[138,3978,3979],{},"gr.Interface(fn=tts_gradio)"," launches shareable demo.",[17,3982,3984],{"id":3983},"chain-into-speech-to-speech-pipelines-with-optimizations","Chain into Speech-to-Speech Pipelines with Optimizations",[22,3986,3987,3988,3991,3992,3995,3996,3999],{},"End-to-end: Transcribe input (",[138,3989,3990],{},"transcribe(SAMPLE_GERMAN, context=\"About VibeVoice\")"," → \"Über VibeVoice...\"), append response text, synthesize—yields conversational audio. Optimizations: ",[138,3993,3994],{},"torch.cuda.empty_cache()",", gradient checkpointing, reduce steps to 10 for speed. Download outputs like ",[138,3997,3998],{},"\u002Fcontent\u002Flongform_output.wav",". Responsible use: Research only, disclose AI speech, avoid impersonation.",[22,4001,4002],{},"Outcomes: Powers voice assistants, podcasts, accessibility—batch ASR cuts processing time, TTS enables interactive apps via Gradio.",{"title":158,"searchDepth":159,"depth":159,"links":4004},[4005,4006,4007,4008],{"id":3870,"depth":159,"text":3871},{"id":3942,"depth":159,"text":3943},{"id":3969,"depth":159,"text":3970},{"id":3983,"depth":159,"text":3984},[165],{"content_references":4011,"triage":4031},[4012,4015,4018,4021,4024,4027],{"type":4013,"title":4014,"url":3882,"context":180},"tool","VibeVoice",{"type":4013,"title":4016,"url":4017,"context":180},"VibeVoice-ASR-HF","https:\u002F\u002Fhuggingface.co\u002Fmicrosoft\u002FVibeVoice-ASR-HF",{"type":4013,"title":4019,"url":4020,"context":180},"VibeVoice-Realtime-0.5B","https:\u002F\u002Fhuggingface.co\u002Fmicrosoft\u002FVibeVoice-Realtime-0.5B",{"type":178,"title":4022,"url":4023,"context":180},"VibeVoice ASR Paper","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2601.18184",{"type":178,"title":4025,"url":4026,"context":180},"VibeVoice TTS Paper","https:\u002F\u002Fopenreview.net\u002Fpdf?id=FihSkzyxdv",{"type":182,"title":4028,"url":4029,"context":4030},"Full Tutorial Codes","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fmicrosoft_vibevoice_asr_realtime_tts_speech_to_speech_marktechpost.py","recommended",{"relevance":4032,"novelty":190,"quality":190,"actionability":4032,"composite":4033,"reasoning":4034},5,4.55,"Category: AI & LLMs. The article provides a detailed, hands-on tutorial for building speech pipelines using Microsoft VibeVoice, addressing practical applications for AI-powered products. It includes specific code snippets and setup instructions that developers can directly implement, making it highly actionable.","\u002Fsummaries\u002F00328a14a70095c4-build-vibevoice-speech-pipelines-in-colab-summary","2026-04-13 01:22:15","2026-04-13 17:53:25",{"title":3860,"description":158},{"loc":4035},"00328a14a70095c4","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F12\u002Fa-hands-on-coding-tutorial-for-microsoft-vibevoice-covering-speaker-aware-asr-real-time-tts-and-speech-to-speech-pipelines\u002F","summaries\u002F00328a14a70095c4-build-vibevoice-speech-pipelines-in-colab-summary",[205,204,4045,206],"machine-learning","Run Microsoft VibeVoice's 7B ASR for speaker diarization and context-aware transcription plus 0.5B real-time TTS with 300ms latency using this Colab code—handles 60min audio and long-form synthesis.",[],"zmPVL5sYTbsBHWCVZHusK-U8VVYqFL1AzQHx0NUJRz8",{"id":4050,"title":4051,"ai":4052,"body":4057,"categories":4155,"created_at":166,"date_modified":166,"description":158,"extension":167,"faq":166,"featured":168,"kicker_label":166,"meta":4156,"navigation":193,"path":4181,"published_at":166,"question":166,"scraped_at":4182,"seo":4183,"sitemap":4184,"source_id":4185,"source_name":4186,"source_type":200,"source_url":4187,"stem":4188,"tags":4189,"thumbnail_url":166,"tldr":4191,"tweet":166,"unknown_tags":4192,"__hash__":4193},"summaries\u002Fsummaries\u002F5db8bfac0c40dc1f-load-4-bit-awq-llms-in-transformers-for-low-memory-summary.md","Load 4-Bit AWQ LLMs in Transformers for Low-Memory Inference",{"provider":7,"model":8,"input_tokens":4053,"output_tokens":4054,"processing_time_ms":4055,"cost_usd":4056},5033,1989,8133,0.00149425,{"type":14,"value":4058,"toc":4150},[4059,4063,4086,4090,4105,4139,4143],[17,4060,4062],{"id":4061},"load-awq-quantized-models-with-one-line","Load AWQ-Quantized Models with One Line",[22,4064,4065,4066,4069,4070,4073,4074,4077,4078,4081,4082,4085],{},"AWQ (Activation-aware Weight Quantization) compresses LLMs to 4-bit weights while preserving a small set of performance-critical weights in higher precision, minimizing accuracy loss versus full quantization. Identify AWQ models by ",[138,4067,4068],{},"quant_method: \"awq\""," in their config.json. Install autoawq (which pins Transformers to v4.47.1—reinstall Transformers after for compatibility), then load with ",[138,4071,4072],{},"AutoModelForCausalLM.from_pretrained(model_id, quant_method=\"awq\")",". This auto-converts non-quantized weights (e.g., embeddings) to fp16 for speed; override via ",[138,4075,4076],{},"dtype=torch.bfloat16",". Move to GPU with ",[138,4079,4080],{},"device_map=\"auto\""," or CPU otherwise. Add ",[138,4083,4084],{},"attn_implementation=\"flash_attention_2\""," for further acceleration, but it conflicts with fused modules below. Trade-off: AWQ prioritizes salient weights per channel, beating round-to-nearest methods on benchmarks like perplexity and zero-shot tasks.",[17,4087,4089],{"id":4088},"fused-modules-double-prefilldecode-throughput","Fused Modules Double Prefill\u002FDecode Throughput",[22,4091,4092,4093,4096,4097,4100,4101,4104],{},"Fuse AWQ linear layers into single kernels for 2x faster prefill (up to 3184 → 3044 tokens\u002Fs at 1024 length) and decode (31 → 89 tokens\u002Fs at 2048 length) at batch_size=1, using just 4-5.5GB VRAM on Mistral-7B-OpenOrca-AWQ. Native support for Llama\u002FMistral; extend to others manually. Create ",[138,4094,4095],{},"AwqConfig(fuse_max_seq_len=2048, do_fuse=True, version=\"gemm\")","—",[138,4098,4099],{},"fuse_max_seq_len"," covers context + generation (oversize safely). Pass to ",[138,4102,4103],{},"from_pretrained(..., quantization_config=AwqConfig(...))",". Benchmarks show fused wins peak at mid-lengths (e.g., 512: prefill 3184→2848, decode 31→97 tokens\u002Fs), but VRAM rises slightly at long contexts (4GB → 5.57GB at 2048). optimum-benchmark graphs confirm fused generate throughput doubles vs. unfused up to batch=8. Can't combine with FlashAttention2—pick based on your seq_len\u002Fbatch needs.",[32,4106,4107,4123],{},[35,4108,4109],{},[38,4110,4111,4114,4117,4120],{},[41,4112,4113],{},"Prefill Length",[41,4115,4116],{},"Unfused Prefill\u002FDecode (tokens\u002Fs)",[41,4118,4119],{},"Fused Prefill\u002FDecode (tokens\u002Fs)",[41,4121,4122],{},"VRAM Savings",[60,4124,4125],{},[38,4126,4127,4130,4133,4136],{},[65,4128,4129],{},"2048",[65,4131,4132],{},"2927 \u002F 35",[65,4134,4135],{},"2715 \u002F 89",[65,4137,4138],{},"~0.16GB",[17,4140,4142],{"id":4141},"exllamav2-kernels-for-amdextreme-speed","ExLlamaV2 Kernels for AMD\u002FExtreme Speed",[22,4144,4145,4146,4149],{},"For fastest prefill\u002Fdecode, install autoawq with ExLlamaV2 support and set ",[138,4147,4148],{},"AwqConfig(version=\"exllama\")",". These kernels excel on AMD GPUs, outperforming standard AWQ on long contexts. Supports fused modules too. Trade-off: ExLlamaV2 ties you to autoawq ecosystem, less flexible than pure Transformers.",{"title":158,"searchDepth":159,"depth":159,"links":4151},[4152,4153,4154],{"id":4061,"depth":159,"text":4062},{"id":4088,"depth":159,"text":4089},{"id":4141,"depth":159,"text":4142},[165],{"content_references":4157,"triage":4179},[4158,4161,4164,4167,4170,4173,4176],{"type":178,"title":4159,"url":4160,"context":176},"Activation-aware Weight Quantization (AWQ)","https:\u002F\u002Fhf.co\u002Fpapers\u002F2306.00978",{"type":4013,"title":4162,"url":4163,"context":180},"llm-awq","https:\u002F\u002Fgithub.com\u002Fmit-han-lab\u002Fllm-awq",{"type":4013,"title":4165,"url":4166,"context":180},"autoawq","https:\u002F\u002Fgithub.com\u002Fcasper-hansen\u002FAutoAWQ",{"type":4013,"title":4168,"url":4169,"context":180},"optimum-intel","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Foptimum\u002Fmain\u002Fen\u002Fintel\u002Foptimization_inc",{"type":4013,"title":4171,"url":4172,"context":180},"ExLlamaV2","https:\u002F\u002Fgithub.com\u002Fturboderp\u002Fexllamav2",{"type":4013,"title":4174,"url":4175,"context":180},"optimum-benchmark","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Foptimum-benchmark",{"type":182,"title":4177,"url":4178,"context":4030},"AWQ demo notebook","https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1HzZH89yAXJaZgwJDhQj9LqSBux932BvY#scrollTo=Wwsg6nCwoThm",{"relevance":4032,"novelty":190,"quality":190,"actionability":4032,"composite":4033,"reasoning":4180},"Category: AI & LLMs. The article provides a detailed guide on using AWQ quantization for LLMs, addressing practical implementation steps that are highly relevant for developers looking to optimize AI models. It includes specific code snippets and performance benchmarks, making it immediately actionable for the target audience.","\u002Fsummaries\u002F5db8bfac0c40dc1f-load-4-bit-awq-llms-in-transformers-for-low-memory-summary","2026-04-16 03:08:27",{"title":4051,"description":158},{"loc":4181},"5db8bfac0c40dc1f","__oneoff__","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fquantization\u002Fawq","summaries\u002F5db8bfac0c40dc1f-load-4-bit-awq-llms-in-transformers-for-low-memory-summary",[4190,205,204,207],"llm","AWQ quantizes LLMs to 4-bits by preserving key weights, loadable via autoawq in Transformers; fused modules boost prefill\u002Fdecode speeds 2x with 4-5GB VRAM at batch=1.",[207],"ZBEAaUV8_lmC6G_GK3pmiz6Wts4pRoGNM0JAG8eqc7Q",{"id":4195,"title":4196,"ai":4197,"body":4202,"categories":4236,"created_at":166,"date_modified":166,"description":158,"extension":167,"faq":166,"featured":168,"kicker_label":166,"meta":4237,"navigation":193,"path":4260,"published_at":166,"question":166,"scraped_at":4261,"seo":4262,"sitemap":4263,"source_id":4264,"source_name":4186,"source_type":200,"source_url":4265,"stem":4266,"tags":4267,"thumbnail_url":166,"tldr":4268,"tweet":166,"unknown_tags":4269,"__hash__":4270},"summaries\u002Fsummaries\u002Fb0e284183065467e-glasswing-ai-finds-zero-days-to-secure-critical-so-summary.md","Glasswing: AI Finds Zero-Days to Secure Critical Software",{"provider":7,"model":8,"input_tokens":4198,"output_tokens":4199,"processing_time_ms":4200,"cost_usd":4201},8888,2041,17765,0.00277545,{"type":14,"value":4203,"toc":4231},[4204,4208,4211,4214,4218,4221,4224,4228],[17,4205,4207],{"id":4206},"mythos-previews-superior-vulnerability-detection","Mythos Preview's Superior Vulnerability Detection",[22,4209,4210],{},"Claude Mythos Preview, an unreleased frontier model, autonomously identifies thousands of zero-day vulnerabilities—many critical—in every major operating system and web browser, plus tools like FFmpeg and the Linux kernel. Specific examples include a 27-year-old OpenBSD flaw allowing remote crashes on firewalls (patched), a 16-year-old FFmpeg bug missed by 5 million automated tests, and a chained Linux kernel exploit escalating user access to full control. It outperforms Claude Opus 4.6 on CyberGym (83.1% vs 66.6% vulnerability reproduction) and agentic coding benchmarks like SWE-bench Verified (93.9% vs 80.8%), Terminal-Bench 2.0 (82.0% vs 65.4%), and GPQA Diamond (94.6% vs 91.3%). These capabilities stem from advanced agentic coding, reasoning, and search, enabling it to spot flaws surviving decades of human and automated scrutiny, while developing sophisticated exploits.",[22,4212,4213],{},"To counter proliferation risks—where AI lowers expertise barriers for attackers, potentially amplifying $500B annual global cybercrime costs—defenders gain an edge by using the same tools proactively. Partners report it uncovers complex issues prior models missed, accelerating fixes at scale.",[17,4215,4217],{"id":4216},"project-glasswing-enables-industry-wide-defense","Project Glasswing Enables Industry-Wide Defense",[22,4219,4220],{},"Launched with partners including AWS, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks—plus 40+ critical infrastructure orgs—Project Glasswing provides Mythos Preview access for scanning first-party and open-source systems. Focus areas: local vulnerability detection, black-box binary testing, endpoint securing, and penetration testing. Anthropic commits $100M in usage credits (post-preview: $25\u002F$125 per million tokens via Claude API, Bedrock, Vertex AI, Microsoft Foundry) and $4M donations ($2.5M to Alpha-Omega\u002FOpenSSF, $1.5M to Apache). Open-source maintainers apply via Claude for Open Source program.",[22,4222,4223],{},"Partners like Cisco emphasize AI's pace\u002Fscale shift demands new hardening; AWS integrates it into 400T daily network flows; Microsoft notes CTI-REALM gains; CrowdStrike warns of collapsing exploit timelines (months to minutes); Linux Foundation sees it as a 'sidekick' for maintainers lacking teams. Google highlights ecosystem tools like Big Sleep\u002FCodeMender. This collaboration shares learnings to harden shared cyber surfaces before adversarial use.",[17,4225,4227],{"id":4226},"balancing-ai-cyber-risks-with-safeguarded-deployment","Balancing AI Cyber Risks with Safeguarded Deployment",[22,4229,4230],{},"AI cyber skills rival top humans (echoing DARPA's 2016 Cyber Grand Challenge), risking frequent\u002Fdestructive attacks on banking, healthcare, energy, transport, and government without safeguards. Yet optimism prevails: Mythos aids bug-free software creation. Anthropic won't release it publicly but plans safeguards in upcoming Claude Opus for safe, scaled deployment in cybersecurity and beyond. Cryptographic hashes disclosed for unpatched vulns; full details post-fix via Frontier Red Team blog.",{"title":158,"searchDepth":159,"depth":159,"links":4232},[4233,4234,4235],{"id":4206,"depth":159,"text":4207},{"id":4216,"depth":159,"text":4217},{"id":4226,"depth":159,"text":4227},[165],{"content_references":4238,"triage":4258},[4239,4243,4248,4252,4255],{"type":172,"title":4240,"publisher":4241,"url":4242,"context":176},"Estimating Global Yearly Cybercrime Damage Costs","Governance.ai","https:\u002F\u002Fwww.governance.ai\u002Fresearch-paper\u002Festimating-global-yearly-cybercrime-damage-costs",{"type":4244,"title":4245,"publisher":4246,"url":4247,"context":180},"event","DARPA Cyber Grand Challenge","DARPA","https:\u002F\u002Fwww.darpa.mil\u002Fresearch\u002Fprograms\u002Fcyber-grand-challenge",{"type":182,"title":4249,"publisher":4250,"url":4251,"context":180},"Claude Mythos Preview System Card","Anthropic","https:\u002F\u002Fanthropic.com\u002Fclaude-mythos-preview-system-card",{"type":182,"title":4253,"publisher":4250,"url":4254,"context":176},"Frontier Red Team Blog: Mythos Preview","https:\u002F\u002Fred.anthropic.com\u002F2026\u002Fmythos-preview",{"type":182,"title":4256,"url":4257,"context":4030},"Claude for Open Source","https:\u002F\u002Fclaude.com\u002Fcontact-sales\u002Fclaude-for-oss",{"relevance":189,"novelty":189,"quality":190,"actionability":159,"composite":191,"reasoning":4259},"Category: AI & LLMs. The article discusses a new AI model's capabilities in detecting vulnerabilities, which is relevant to AI engineering and security. However, it lacks practical applications or frameworks that the audience can directly implement in their product development.","\u002Fsummaries\u002Fb0e284183065467e-glasswing-ai-finds-zero-days-to-secure-critical-so-summary","2026-04-14 14:30:00",{"title":4196,"description":158},{"loc":4260},"b0e284183065467e","https:\u002F\u002Fwww.anthropic.com\u002Fglasswing","summaries\u002Fb0e284183065467e-glasswing-ai-finds-zero-days-to-secure-critical-so-summary",[4190,204,206,207],"Claude Mythos Preview autonomously detects thousands of high-severity zero-days in every major OS\u002Fbrowser; Project Glasswing shares access with 40+ orgs via $100M credits to prioritize defense over attack.",[207],"0XzmR78IxsYtPCzBGDbznDzwmWHpoupoo2TqGarJ3B4"]