[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-ai-engineers-profile-data-i-o-before-models-summary":3,"summaries-facets-categories":109,"summary-related-ai-engineers-profile-data-i-o-before-models-summary":3702},{"id":4,"title":5,"ai":6,"body":13,"categories":88,"created_at":90,"date_modified":90,"description":41,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":93,"navigation":94,"path":95,"published_at":96,"question":90,"scraped_at":90,"seo":97,"sitemap":98,"source_id":99,"source_name":100,"source_type":101,"source_url":102,"stem":103,"tags":104,"thumbnail_url":90,"tldr":106,"tweet":90,"unknown_tags":107,"__hash__":108},"summaries\u002Fsummaries\u002Fai-engineers-profile-data-i-o-before-models-summary.md","AI Engineers: Profile Data\u002FI\u002FO Before Models",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",3633,903,9538,0.00071115,{"type":14,"value":15,"toc":84},"minimark",[16,21,25,29,32,35,77,80],[17,18,20],"h2",{"id":19},"scale-demands-robust-python-beyond-models","Scale Demands Robust Python Beyond Models",[22,23,24],"p",{},"AI engineering requires Python code that handles scale, data volumes, and long-term reliability, not just functional scripts. Engineers often waste time (and GPU credits) on model tweaks when issues stem from elsewhere, turning debugging into archaeology after initial successes like training models or pip-installing libraries.",[17,26,28],{"id":27},"true-bottlenecks-hide-in-data-pipelines","True Bottlenecks Hide in Data Pipelines",[22,30,31],{},"Obsessing over model architecture misses the point: 80–90% of time is spent on data loading, preprocessing, I\u002FO operations, and glue code. Slow training loops rarely need model changes—profile the full stack first.",[22,33,34],{},"Example profiling code reveals data loading costs:",[36,37,42],"pre",{"className":38,"code":39,"language":40,"meta":41,"style":41},"language-python shiki shiki-themes github-light github-dark","import time\nstart = time.time()\n# simulate data loading\ndata = [i for i in range(10_000_000)]\nprint(f\"Time taken: {time.time() - start:.2f}s\")\n","python","",[43,44,45,53,59,65,71],"code",{"__ignoreMap":41},[46,47,50],"span",{"class":48,"line":49},"line",1,[46,51,52],{},"import time\n",[46,54,56],{"class":48,"line":55},2,[46,57,58],{},"start = time.time()\n",[46,60,62],{"class":48,"line":61},3,[46,63,64],{},"# simulate data loading\n",[46,66,68],{"class":48,"line":67},4,[46,69,70],{},"data = [i for i in range(10_000_000)]\n",[46,72,74],{"class":48,"line":73},5,[46,75,76],{},"print(f\"Time taken: {time.time() - start:.2f}s\")\n",[22,78,79],{},"This demonstrates how non-model operations dominate runtime, forcing a shift from model-centric fixes to holistic optimization.",[81,82,83],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":41,"searchDepth":55,"depth":55,"links":85},[86,87],{"id":19,"depth":55,"text":20},{"id":27,"depth":55,"text":28},[89],"Software Engineering",null,"md",false,{},true,"\u002Fsummaries\u002Fai-engineers-profile-data-i-o-before-models-summary","2026-04-08 21:21:17",{"title":5,"description":41},{"loc":95},"38de45bd32930456","Python in Plain English","article","https:\u002F\u002Funknown","summaries\u002Fai-engineers-profile-data-i-o-before-models-summary",[40,105],"ai-llms","80-90% of AI engineering time goes to data loading, preprocessing, and I\u002FO—not models. Profile everything else first to find real bottlenecks.",[105],"-PtXDIqYr6sji4lvGzxXF_vJSWJqWfZyHxjXaw87bXU",[110,113,116,119,122,125,127,129,131,133,135,137,140,142,144,146,148,150,152,154,156,158,161,164,166,168,170,172,174,177,179,181,183,185,187,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,226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RAG Pipeline: ChromaDB + Embeddings + Anthropic",{"provider":7,"model":8,"input_tokens":3707,"output_tokens":3708,"processing_time_ms":3709,"cost_usd":3710},3613,720,7647,0.00105995,{"type":14,"value":3712,"toc":3727},[3713,3717,3720,3724],[17,3714,3716],{"id":3715},"grasp-rag-by-building-and-running-it","Grasp RAG by Building and Running It",[22,3718,3719],{},"RAG (Retrieval-Augmented Generation) becomes intuitive not from diagrams but from executing code that queries unseen documents—like a paper the model never trained on—and gets accurate answers. Skip CRUD or Hello World; this 50-line pipeline is your essential first Python AI project for day-one production relevance. It demonstrates semantic search retrieving relevant chunks, then feeding them into an LLM via a tuned system prompt for grounded responses.",[17,3721,3723],{"id":3722},"core-mechanics-semantic-search-prompting","Core Mechanics: Semantic Search + Prompting",[22,3725,3726],{},"RAG relies on two elements: (1) semantic search via embeddings (using SentenceTransformers) stored in ChromaDB vector database for fast retrieval of contextually similar document chunks; (2) an effective system prompt that injects retrieved content into the LLM (Anthropic) to generate answers without hallucination. Provide your documents as input, embed them once, query semantically, and output synthesized responses—bypassing the LLM's static training data.",{"title":41,"searchDepth":55,"depth":55,"links":3728},[3729,3730],{"id":3715,"depth":55,"text":3716},{"id":3722,"depth":55,"text":3723},[118],{},"\u002Fsummaries\u002F50-line-rag-pipeline-chromadb-embeddings-anthropic-summary","2026-04-08 21:21:20",{"title":3705,"description":41},{"loc":3733},"6155f44abdae4444","Level Up Coding","summaries\u002F50-line-rag-pipeline-chromadb-embeddings-anthropic-summary",[40,3741,105],"llm","Build a working RAG system in Python using ChromaDB for storage, SentenceTransformers for semantic search embeddings, and Anthropic for generation—answers questions from unseen docs via retrieval + prompting.",[105],"BIJedy9i_JFeNsMJjn7eT_KsRnpCCYu15mqeltQb0d8",{"id":3746,"title":3747,"ai":3748,"body":3753,"categories":3797,"created_at":90,"date_modified":90,"description":41,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":3798,"navigation":94,"path":3799,"published_at":3800,"question":90,"scraped_at":90,"seo":3801,"sitemap":3802,"source_id":3803,"source_name":100,"source_type":101,"source_url":102,"stem":3804,"tags":3805,"thumbnail_url":90,"tldr":3806,"tweet":90,"unknown_tags":3807,"__hash__":3808},"summaries\u002Fsummaries\u002Fpython-shallow-copies-share-nested-mutables-summary.md","Python Shallow Copies Share Nested Mutables",{"provider":7,"model":8,"input_tokens":3749,"output_tokens":3750,"processing_time_ms":3751,"cost_usd":3752},3622,781,6701,0.0010924,{"type":14,"value":3754,"toc":3793},[3755,3759,3766,3780,3784,3787,3790],[17,3756,3758],{"id":3757},"shallow-copies-fail-on-nested-mutables","Shallow Copies Fail on Nested Mutables",[22,3760,3761,3762,3765],{},"Python's list.copy() or slicing (e.g., my_list",[46,3763,3764],{},":",") produces shallow copies: top-level elements are duplicated, but nested mutable objects like lists or dicts are shared references. Modifying a nested item in the copy changes the original, causing silent data corruption during experiments.",[22,3767,3768,3769,3772,3773,3776,3777,3779],{},"Example pitfall: If original_list = [[1,2], ",[46,3770,3771],{},"3,4","], then copy_list = original_list.copy(); copy_list[0]",[46,3774,3775],{},"0"," = 99 also sets original_list[0]",[46,3778,3775],{}," to 99.",[17,3781,3783],{"id":3782},"deepcopy-ensures-independence","Deepcopy Ensures Independence",[22,3785,3786],{},"Use copy.deepcopy() to recursively copy all nested structures, creating fully independent data. This prevents betrayal in iterative workflows where you transform data (remove items, add values) while preserving raw originals for validation and comparison.",[22,3788,3789],{},"Rule for data scientists\u002Fengineers: Never modify raw data—always deepcopy first to safely iterate, validate transformations, and compare original vs. modified without data loss.",[22,3791,3792],{},"Trade-off: Deepcopy is slower and memory-intensive for large\u002Fdeep structures, so use shallow copy when no nested mutables exist.",{"title":41,"searchDepth":55,"depth":55,"links":3794},[3795,3796],{"id":3757,"depth":55,"text":3758},{"id":3782,"depth":55,"text":3783},[89],{},"\u002Fsummaries\u002Fpython-shallow-copies-share-nested-mutables-summary","2026-04-08 21:21:18",{"title":3747,"description":41},{"loc":3799},"63cc641c63227a90","summaries\u002Fpython-shallow-copies-share-nested-mutables-summary",[40],"list.copy() creates shallow copies that share nested mutable objects, so modifying them alters originals—use deepcopy for safe independent copies.",[],"uJ6bu263RNqr5XsOIjO1kIq1mKG75iKSYEXqDNA6ZEY",{"id":3810,"title":3811,"ai":3812,"body":3817,"categories":3843,"created_at":90,"date_modified":90,"description":41,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":3844,"navigation":94,"path":3845,"published_at":3800,"question":90,"scraped_at":90,"seo":3846,"sitemap":3847,"source_id":3848,"source_name":100,"source_type":101,"source_url":102,"stem":3849,"tags":3850,"thumbnail_url":90,"tldr":3851,"tweet":90,"unknown_tags":3852,"__hash__":3853},"summaries\u002Fsummaries\u002Fpython-tops-linkedin-specialize-for-160k-salaries-summary.md","Python Tops LinkedIn: Specialize for $160K Salaries",{"provider":7,"model":8,"input_tokens":3813,"output_tokens":3814,"processing_time_ms":3815,"cost_usd":3816},3692,1295,17049,0.00136325,{"type":14,"value":3818,"toc":3839},[3819,3823,3826,3829,3833,3836],[17,3820,3822],{"id":3821},"pythons-unmatched-job-demand","Python's Unmatched Job Demand",[22,3824,3825],{},"Python has overtaken all languages as LinkedIn's #1 skill, powering 1.19 million active job listings—an industry-wide requirement, not a niche. This shift, from a 1991 hobby project, reflects seismic changes in tech hiring where Python proficiency is now mandatory for most roles.",[22,3827,3828],{},"Average salaries exceed $127K, but the real opportunity lies in the spread: undifferentiated Python developers earn around $80K, while those with precise specializations command $160K for similar workloads.",[17,3830,3832],{"id":3831},"escape-commodity-skills-with-specialization","Escape Commodity Skills with Specialization",[22,3834,3835],{},"Basic Python knowledge is table stakes; the closing window demands credentials and positioning in high-value niches. The article outlines a 2026 playbook—covering dominance data, top-paying specializations, and actionable steps—but emphasizes that generic 'Python developer' roles are vanishing.",[22,3837,3838],{},"To capture value, focus on what separates earners: specific niches (not detailed in intro), credentials that signal expertise, and positioning moves that align skills with market mandates.",{"title":41,"searchDepth":55,"depth":55,"links":3840},[3841,3842],{"id":3821,"depth":55,"text":3822},{"id":3831,"depth":55,"text":3832},[89],{},"\u002Fsummaries\u002Fpython-tops-linkedin-specialize-for-160k-salaries-summary",{"title":3811,"description":41},{"loc":3845},"7fde895a62c4ed5b","summaries\u002Fpython-tops-linkedin-specialize-for-160k-salaries-summary",[40],"Python leads with 1.19M job listings at $127K+ avg pay; basic skills get $80K, specializations unlock $160K roles via targeted niches.",[],"b0wuBuWMVZtYHhjm4wSIkZOErw-w_QWbXtqwc8HQc1s",{"id":3855,"title":3856,"ai":3857,"body":3862,"categories":4007,"created_at":90,"date_modified":90,"description":41,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":4008,"navigation":94,"path":4030,"published_at":4031,"question":90,"scraped_at":4032,"seo":4033,"sitemap":4034,"source_id":4035,"source_name":4025,"source_type":101,"source_url":4036,"stem":4037,"tags":4038,"thumbnail_url":90,"tldr":4041,"tweet":90,"unknown_tags":4042,"__hash__":4043},"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":3858,"output_tokens":3859,"processing_time_ms":3860,"cost_usd":3861},6601,1943,19579,0.00178485,{"type":14,"value":3863,"toc":4002},[3864,3868,3871,3874,3877,3969,3973,3976,3980,3999],[17,3865,3867],{"id":3866},"select-granite-41-variant-by-your-asr-bottleneck","Select Granite 4.1 Variant by Your ASR Bottleneck",[22,3869,3870],{},"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.",[22,3872,3873],{},"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.",[22,3875,3876],{},"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.",[3878,3879,3880,3905],"table",{},[3881,3882,3883],"thead",{},[3884,3885,3886,3890,3893,3896,3899,3902],"tr",{},[3887,3888,3889],"th",{},"Model",[3887,3891,3892],{},"Key Strengths",[3887,3894,3895],{},"WER",[3887,3897,3898],{},"RTF",[3887,3900,3901],{},"Languages",[3887,3903,3904],{},"Features",[3906,3907,3908,3929,3949],"tbody",{},[3884,3909,3910,3914,3917,3920,3923,3926],{},[3911,3912,3913],"td",{},"Base",[3911,3915,3916],{},"Accuracy",[3911,3918,3919],{},"5.33%",[3911,3921,3922],{},"231",[3911,3924,3925],{},"7",[3911,3927,3928],{},"Translation, keyword bias",[3884,3930,3931,3934,3937,3940,3943,3946],{},[3911,3932,3933],{},"Plus",[3911,3935,3936],{},"Diarization, timestamps",[3911,3938,3939],{},"Higher",[3911,3941,3942],{},"Lower",[3911,3944,3945],{},"5",[3911,3947,3948],{},"Incremental decode",[3884,3950,3951,3954,3957,3960,3963,3966],{},[3911,3952,3953],{},"NAR",[3911,3955,3956],{},"Throughput",[3911,3958,3959],{},"Competitive",[3911,3961,3962],{},"1820 (H100)",[3911,3964,3965],{},"?",[3911,3967,3968],{},"Raw transcripts",[17,3970,3972],{"id":3971},"non-autoregressive-transcript-editing-beats-sequential-decoding","Non-Autoregressive Transcript Editing Beats Sequential Decoding",[22,3974,3975],{},"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.",[17,3977,3979],{"id":3978},"run-locally-with-transformers-chunking-and-fine-tuning-tips","Run Locally with Transformers: Chunking and Fine-Tuning Tips",[22,3981,3982,3983,3986,3987,3990,3991,3994,3995,3998],{},"Load via Hugging Face Transformers: ",[43,3984,3985],{},"processor = AutoProcessor.from_pretrained(model_id); model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id)",". Use ",[43,3988,3989],{},"generate()"," with custom prompts for diarization (",[43,3992,3993],{},"\u003C|startoftranscript|>\u003C|en|>\u003C|transcribe|>\u003C|speaker_attributed_asr|>",") or keywords (",[43,3996,3997],{},"\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).",[22,4000,4001],{},"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":41,"searchDepth":55,"depth":55,"links":4003},[4004,4005,4006],{"id":3866,"depth":55,"text":3867},{"id":3971,"depth":55,"text":3972},{"id":3978,"depth":55,"text":3979},[118],{"content_references":4009,"triage":4027},[4010,4016,4020,4023],{"type":4011,"title":4012,"publisher":4013,"url":4014,"context":4015},"report","Granite 4.1 AI Foundation Models","IBM Research","https:\u002F\u002Fresearch.ibm.com\u002Fblog\u002Fgranite-4-1-ai-foundation-models","cited",{"type":4017,"title":4018,"context":4019},"paper","NLE: Non-autoregressive LLM-based ASR by Transcript Editing","mentioned",{"type":4021,"title":4022,"context":4019},"other","Granite Speech Model Github",{"type":4021,"title":4024,"author":4025,"url":4026,"context":4019},"llm-tutorials","Sam Witteveen","https:\u002F\u002Fgithub.com\u002Fsamwit\u002Fllm-tutorials",{"relevance":61,"novelty":55,"quality":67,"actionability":61,"composite":4028,"reasoning":4029},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.","\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":3856,"description":41},{"loc":4030},"a46a387d67c4fcca","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Tymq54Mn8SU","summaries\u002Fc09281106c1de2ef-ibm-granite-speech-4-1-3-asr-models-for-accuracy-f-summary",[4039,40,4040,105],"ai-tools","open-source","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.",[105],"dF2uBIfxfrOwqTG70jiimSEB82hxedQZTBWqe0_5R-8"]