[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-f783931b642bec27-vibevoice-asr-60-min-asr-with-speakers-timestamps-summary":3,"summaries-facets-categories":142,"summary-related-f783931b642bec27-vibevoice-asr-60-min-asr-with-speakers-timestamps-summary":7047},{"id":4,"title":5,"ai":6,"body":13,"categories":97,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":101,"navigation":125,"path":126,"published_at":98,"question":98,"scraped_at":127,"seo":128,"sitemap":129,"source_id":130,"source_name":131,"source_type":132,"source_url":133,"stem":134,"tags":135,"thumbnail_url":98,"tldr":139,"tweet":98,"unknown_tags":140,"__hash__":141},"summaries\u002Fsummaries\u002Ff783931b642bec27-vibevoice-asr-60-min-asr-with-speakers-timestamps-summary.md","VibeVoice-ASR: 60-Min ASR with Speakers, Timestamps, Hotwords",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8981,1739,13836,0.00215885,{"type":14,"value":15,"toc":90},"minimark",[16,21,49,56,60,83,87],[17,18,20],"h2",{"id":19},"unified-long-form-transcription-in-single-pass","Unified Long-Form Transcription in Single Pass",[22,23,24,25,29,30,33,34,37,38,29,41,44,45,48],"p",{},"VibeVoice-ASR handles 60-minute audio within 64K tokens without chunking losses, maintaining speaker consistency and semantics. It jointly performs ASR, diarization, and timestamping, outputting JSON-like structures with Start\u002FEnd times, Speaker IDs, and Content. Load via Transformers >=5.3.0: ",[26,27,28],"code",{},"AutoProcessor"," and ",[26,31,32],{},"VibeVoiceAsrForConditionalGeneration.from_pretrained(\"microsoft\u002FVibeVoice-ASR-HF\")",". Use ",[26,35,36],{},"processor.apply_transcription_request(audio)"," for inputs, then ",[26,39,40],{},"model.generate(**inputs)",[26,42,43],{},"processor.decode(generated_ids, return_format=\"parsed\")"," for list of dicts or ",[26,46,47],{},"\"transcription_only\""," for plain text. Example on podcast audio yields segments like {\"Start\":0,\"End\":15.43,\"Speaker\":0,\"Content\":\"Hello everyone...\"}, preserving multi-speaker flow.",[22,50,51,52,55],{},"Custom hotwords via ",[26,53,54],{},"prompt"," parameter fix misrecognitions: on German-accented \"VibeVoice\" audio, without prompt it transcribes \"Revevoices\", but \"About VibeVoice\" prompt corrects to exact match, ideal for names or terms.",[17,57,59],{"id":58},"flexible-inference-and-optimization-techniques","Flexible Inference and Optimization Techniques",[22,61,62,63,66,67,70,71,74,75,78,79,82],{},"Batch process lists of audio\u002Fprompts for efficiency. Adjust ",[26,64,65],{},"tokenizer_chunk_size"," (default 1440000 samples\u002F60s at 24kHz, multiples of 3200 hop length) to fit memory, e.g., 64000 for shorter segments with cached states. Chat templates enable role-based inputs: ",[26,68,69],{},"[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"prompt\"},{\"type\":\"audio\",\"path\":\"url\"}]}]",", processed via ",[26,72,73],{},"apply_chat_template",". Torch.compile speeds up by 2x+ on benchmarks (e.g., batch-4 German audio: ~0.2s uncompiled to ~0.1s compiled). Pipeline mode works but requires custom parsing of raw JSON strings. For training, use ",[26,76,77],{},"model.train()"," with ",[26,80,81],{},"output_labels=True"," in chat templates, computing loss on JSON-like targets.",[17,84,86],{"id":85},"proven-performance-across-benchmarks","Proven Performance Across Benchmarks",[22,88,89],{},"Achieves average 7.77% WER on Open ASR Leaderboard (e.g., 2.20% LibriSpeech clean, 13.17% earnings22, RTF 51.80x real-time). Technical report shows low DER, cpWER, tcpWER on long-form datasets. Supports 50+ languages without ID specification, handling code-switching; distribution chart emphasizes English-heavy training with broad coverage. MIT-licensed, deployable on Foundry or Gradio playground.",{"title":91,"searchDepth":92,"depth":92,"links":93},"",2,[94,95,96],{"id":19,"depth":92,"text":20},{"id":58,"depth":92,"text":59},{"id":85,"depth":92,"text":86},[],null,"md",false,{"content_references":102,"triage":120},[103,108,113,117],{"type":104,"title":105,"url":106,"context":107},"paper","VibeVoice-ASR Technical Report","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2601.18184","cited",{"type":109,"title":110,"url":111,"context":112},"other","GitHub Repo","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FVibeVoice","mentioned",{"type":114,"title":115,"url":116,"context":112},"tool","Live Playground","https:\u002F\u002Faka.ms\u002Fvibevoice-asr",{"type":109,"title":118,"url":119,"context":107},"Open ASR Leaderboard","https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fhf-audio\u002Fopen_asr_leaderboard",{"relevance":121,"novelty":122,"quality":122,"actionability":122,"composite":123,"reasoning":124},5,4,4.35,"Category: AI & LLMs. The article provides a detailed overview of the VibeVoice-ASR tool, which is highly relevant for developers looking to integrate advanced ASR capabilities into their AI products. It includes practical examples of how to implement the tool, making it actionable for the target audience.",true,"\u002Fsummaries\u002Ff783931b642bec27-vibevoice-asr-60-min-asr-with-speakers-timestamps-summary","2026-04-14 14:33:41",{"title":5,"description":91},{"loc":126},"f783931b642bec27","__oneoff__","article","https:\u002F\u002Fhuggingface.co\u002Fmicrosoft\u002FVibeVoice-ASR-HF","summaries\u002Ff783931b642bec27-vibevoice-asr-60-min-asr-with-speakers-timestamps-summary",[136,137,138],"ai-tools","machine-learning","python","Process up to 60 minutes of audio in one pass for structured transcripts (speaker IDs, timestamps, content) across 50+ languages, with custom hotwords boosting accuracy on proper 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Wrap diffuse in torch.autocast('cuda') for half-precision speedup. Higher inference steps (100-200) improve quality; guidance 3-10 tunes adherence. Users extended to prompt interpolation, fp16 models (fix dtype mismatches by upgrading diffusers\u002Ftransformers\u002Fscipy), or pipeline simplifications (pipe(prompt, latents=init, ...)).",{"title":91,"searchDepth":92,"depth":92,"links":7080},[7081,7082,7083],{"id":7060,"depth":92,"text":7061},{"id":7067,"depth":92,"text":7068},{"id":7074,"depth":92,"text":7075},[172],{},"\u002Fsummaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion-summary","2026-04-08 21:21:20",{"title":7050,"description":91},{"loc":7086},"9fd1fce56d7f77a1","Andrej Karpathy Gists","https:\u002F\u002Funknown","summaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion-summary",[138,136,137],"Interpolate random latents with slerp under a fixed prompt to create smooth, hypnotic videos from Stable Diffusion frames (50 inference steps, 7.5 guidance, 200 steps per pair).",[],"H_2GboVk_TSGTVOU3bA07RlIxGLs8Nt03Tcp69M_kQk",{"id":7099,"title":7100,"ai":7101,"body":7107,"categories":7153,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":7154,"navigation":125,"path":7158,"published_at":7159,"question":98,"scraped_at":7160,"seo":7161,"sitemap":7162,"source_id":7163,"source_name":7164,"source_type":132,"source_url":7165,"stem":7166,"tags":7167,"thumbnail_url":98,"tldr":7169,"tweet":98,"unknown_tags":7170,"__hash__":7171},"summaries\u002Fsummaries\u002F93e96619473c5ad7-building-real-time-industrial-digital-twins-with-a-summary.md","Building Real-Time Industrial Digital Twins with AI",{"provider":7,"model":7102,"input_tokens":7103,"output_tokens":7104,"processing_time_ms":7105,"cost_usd":7106},"google\u002Fgemini-3.1-flash-lite",3909,369,2758,0.00153075,{"type":14,"value":7108,"toc":7149},[7109,7113,7116,7120,7123,7146],[17,7110,7112],{"id":7111},"from-observation-to-active-simulation","From Observation to Active Simulation",[22,7114,7115],{},"Traditional industrial digital twins often function as passive dashboards, merely visualizing historical or current machine metrics. To be effective in modern environments, digital twins must transition into active systems that understand, simulate, and predict reality. This requires moving away from static reporting toward architectures capable of processing thousands of sensor events per second to mirror the dynamic state changes of production lines and supply chains.",[17,7117,7119],{"id":7118},"architecting-for-real-time-predictive-intelligence","Architecting for Real-Time Predictive Intelligence",[22,7121,7122],{},"Building a digital twin that 'thinks' requires an infrastructure that integrates streaming data with predictive modeling. The core objective is to shift from reactive monitoring to proactive decision-making. By utilizing Python-based AI pipelines, engineers can ingest high-velocity industrial data to:",[7124,7125,7126,7134,7140],"ul",{},[7127,7128,7129,7133],"li",{},[7130,7131,7132],"strong",{},"Simulate Reality:"," Create virtual representations that adapt to demand fluctuations and operational shifts in real-time.",[7127,7135,7136,7139],{},[7130,7137,7138],{},"Predict Failures:"," Use continuous data streams to identify anomalies before they result in downtime, moving from scheduled maintenance to predictive, condition-based maintenance.",[7127,7141,7142,7145],{},[7130,7143,7144],{},"Enable Autonomous Decisions:"," Empower the digital twin to trigger automated responses or optimize production parameters without human intervention, effectively closing the loop between data ingestion and operational action.",[22,7147,7148],{},"This approach transforms the digital twin from a visual aid into a core component of industrial automation, allowing systems to adapt to complex, shifting operational environments.",{"title":91,"searchDepth":92,"depth":92,"links":7150},[7151,7152],{"id":7111,"depth":92,"text":7112},{"id":7118,"depth":92,"text":7119},[158],{"content_references":7155,"triage":7156},[],{"relevance":121,"novelty":122,"quality":122,"actionability":122,"composite":123,"reasoning":7157},"Category: AI Automation. The article discusses how to build real-time industrial digital twins using AI, which directly addresses the audience's interest in AI-powered product development. It provides actionable insights on integrating streaming data with predictive modeling, making it relevant and practical for engineers and product builders.","\u002Fsummaries\u002F93e96619473c5ad7-building-real-time-industrial-digital-twins-with-a-summary","2026-06-30 10:57:32","2026-06-30 12:57:12",{"title":7100,"description":91},{"loc":7158},"93e96619473c5ad7","Python in Plain English","https:\u002F\u002Fpython.plainenglish.io\u002Fbuilding-industrial-digital-twins-that-think-in-real-time-with-python-and-ai-49bb8668a050?source=rss----78073def27b8---4","summaries\u002F93e96619473c5ad7-building-real-time-industrial-digital-twins-with-a-summary",[138,136,137,7168],"automation","Modern digital twins must move beyond static dashboards to active, predictive systems that simulate and anticipate factory operations using real-time streaming data.",[],"LJpDjCRDU3uH1uBOSaJlJ3768B6Epw08opD4hPrI6fs",{"id":7173,"title":7174,"ai":7175,"body":7180,"categories":7259,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":7260,"navigation":125,"path":7273,"published_at":7274,"question":98,"scraped_at":7274,"seo":7275,"sitemap":7276,"source_id":7277,"source_name":7278,"source_type":132,"source_url":7279,"stem":7280,"tags":7281,"thumbnail_url":98,"tldr":7283,"tweet":98,"unknown_tags":7284,"__hash__":7285},"summaries\u002Fsummaries\u002F1eaf4aab7431c0b6-spatial-graph-neural-networks-for-urban-function-i-summary.md","Spatial Graph Neural Networks for Urban Function Inference",{"provider":7,"model":7102,"input_tokens":7176,"output_tokens":7177,"processing_time_ms":7178,"cost_usd":7179},11411,611,3048,0.00376925,{"type":14,"value":7181,"toc":7254},[7182,7186,7197,7201,7204,7236,7243,7247],[17,7183,7185],{"id":7184},"building-a-spatial-graph-pipeline","Building a Spatial Graph Pipeline",[22,7187,7188,7189,7192,7193,7196],{},"This tutorial demonstrates an end-to-end workflow for urban function inference, where the goal is to classify Points of Interest (POIs) based on their spatial context. The pipeline leverages ",[26,7190,7191],{},"city2graph"," to bridge geospatial data processing with graph-based machine learning. The process begins by collecting real-world POI and street network data from OpenStreetMap (OSM) via ",[26,7194,7195],{},"OSMnx",". To ensure reproducibility and robustness, the workflow includes a synthetic data fallback that generates clustered POIs if live OSM data is unavailable.",[17,7198,7200],{"id":7199},"feature-engineering-and-graph-construction","Feature Engineering and Graph Construction",[22,7202,7203],{},"Spatial features are engineered by calculating local POI density and proximity to the nearest street segments. The core of the spatial analysis involves constructing various proximity graph families to represent urban structure, including:",[7124,7205,7206,7211,7216,7221,7226,7231],{},[7127,7207,7208],{},[7130,7209,7210],{},"K-Nearest Neighbors (KNN)",[7127,7212,7213],{},[7130,7214,7215],{},"Delaunay Triangulation",[7127,7217,7218],{},[7130,7219,7220],{},"Gabriel Graphs",[7127,7222,7223],{},[7130,7224,7225],{},"Relative Neighborhood Graphs (RNG)",[7127,7227,7228],{},[7130,7229,7230],{},"Euclidean Minimum Spanning Trees (EMST)",[7127,7232,7233],{},[7130,7234,7235],{},"Waxman Graphs",[22,7237,7238,7239,7242],{},"These topologies are compared to evaluate how different connectivity strategies capture urban relationships. The data is then converted into ",[26,7240,7241],{},"PyTorch Geometric"," formats, supporting both homogeneous graphs (for standard classification) and heterogeneous graphs (to model relationships between different urban function categories).",[17,7244,7246],{"id":7245},"model-training-and-inference","Model Training and Inference",[22,7248,7249,7250,7253],{},"For classification, the tutorial implements a two-layer ",[26,7251,7252],{},"GraphSAGE"," model. The model learns node representations by aggregating features from local graph neighborhoods. The training process uses a 60\u002F20\u002F20 split for training, validation, and testing. Performance is evaluated using accuracy and macro-F1 scores. Finally, the learned embeddings are visualized using PCA, and predictions are mapped back to geographic space, providing a clear view of how the model interprets urban functions based on spatial structure.",{"title":91,"searchDepth":92,"depth":92,"links":7255},[7256,7257,7258],{"id":7184,"depth":92,"text":7185},{"id":7199,"depth":92,"text":7200},{"id":7245,"depth":92,"text":7246},[197],{"content_references":7261,"triage":7269},[7262,7265,7267],{"type":114,"title":7191,"url":7263,"context":7264},"https:\u002F\u002Fgithub.com\u002Fc2g-dev\u002Fcity2graph","recommended",{"type":114,"title":7195,"url":7266,"context":7264},"https:\u002F\u002Fosmnx.readthedocs.io\u002F",{"type":114,"title":7241,"url":7268,"context":7264},"https:\u002F\u002Fwww.pyg.org\u002F",{"relevance":122,"novelty":7270,"quality":122,"actionability":122,"composite":7271,"reasoning":7272},3,3.8,"Category: AI & LLMs. The article provides a practical pipeline for urban function inference using spatial graph neural networks, which directly addresses the audience's need for actionable AI engineering content. It includes specific techniques and tools like `city2graph`, `OSMnx`, and `PyTorch Geometric`, making it relevant for developers looking to implement AI features.","\u002Fsummaries\u002F1eaf4aab7431c0b6-spatial-graph-neural-networks-for-urban-function-i-summary","2026-06-13 12:56:20",{"title":7174,"description":91},{"loc":7273},"1eaf4aab7431c0b6","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F12\u002Fa-coding-implementation-on-spatial-graph-neural-networks-for-urban-function-inference-using-city2graph-osmnx-and-pytorch-geometric\u002F","summaries\u002F1eaf4aab7431c0b6-spatial-graph-neural-networks-for-urban-function-i-summary",[138,137,7282,136],"data-science","A practical pipeline for urban function inference using city2graph, OSMnx, and PyTorch Geometric to classify POIs based on spatial relationships and graph topology.",[],"NgedcdoA-nvXxSX0y0M6IsoB3fqq-EzowtGYVGVd-XQ",{"id":7287,"title":7288,"ai":7289,"body":7294,"categories":7367,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":7368,"navigation":125,"path":7378,"published_at":7379,"question":98,"scraped_at":7379,"seo":7380,"sitemap":7381,"source_id":7382,"source_name":7278,"source_type":132,"source_url":7383,"stem":7384,"tags":7385,"thumbnail_url":98,"tldr":7386,"tweet":98,"unknown_tags":7387,"__hash__":7388},"summaries\u002Fsummaries\u002F216d50b9ddb75827-building-3d-medical-segmentation-pipelines-with-mo-summary.md","Building 3D Medical Segmentation Pipelines with MONAI",{"provider":7,"model":7102,"input_tokens":7290,"output_tokens":7291,"processing_time_ms":7292,"cost_usd":7293},10639,468,2763,0.00336175,{"type":14,"value":7295,"toc":7362},[7296,7300,7303,7307,7310,7355,7359],[17,7297,7299],{"id":7298},"end-to-end-medical-imaging-pipeline","End-to-End Medical Imaging Pipeline",[22,7301,7302],{},"This workflow utilizes the MONAI framework to process 3D medical CT volumes for binary organ segmentation. The pipeline transforms raw medical data into a format suitable for deep learning by applying specific medical imaging operations: orientation alignment, voxel-spacing normalization, intensity windowing, and foreground cropping.",[17,7304,7306],{"id":7305},"model-training-and-inference-techniques","Model Training and Inference Techniques",[22,7308,7309],{},"To handle the computational demands of 3D volumetric data, the implementation employs several key strategies:",[7124,7311,7312,7318,7331,7337],{},[7127,7313,7314,7317],{},[7130,7315,7316],{},"Patch-based Sampling:"," Instead of processing full volumes, the model trains on smaller, randomly cropped patches (96x96x96) to manage memory constraints.",[7127,7319,7320,7323,7324,29,7327,7330],{},[7130,7321,7322],{},"Mixed Precision Training:"," Uses PyTorch's ",[26,7325,7326],{},"autocast",[26,7328,7329],{},"GradScaler"," to accelerate training and reduce GPU memory usage.",[7127,7332,7333,7336],{},[7130,7334,7335],{},"Sliding-Window Inference:"," During validation and testing, the model performs inference using a sliding-window approach with 50% overlap to ensure seamless segmentation across the entire 3D volume.",[7127,7338,7339,7342,7343,7346,7347,7350,7351,7354],{},[7130,7340,7341],{},"Loss and Optimization:"," The model uses ",[26,7344,7345],{},"DiceCELoss"," to handle class imbalance and an ",[26,7348,7349],{},"AdamW"," optimizer with a ",[26,7352,7353],{},"CosineAnnealingLR"," scheduler for stable convergence.",[17,7356,7358],{"id":7357},"evaluation-and-visualization","Evaluation and Visualization",[22,7360,7361],{},"Model performance is tracked using the Dice metric, which measures the overlap between predicted masks and ground-truth labels. The tutorial includes a final visualization step that compares original CT slices, ground-truth labels, and model predictions, allowing for qualitative assessment of the segmentation accuracy.",{"title":91,"searchDepth":92,"depth":92,"links":7363},[7364,7365,7366],{"id":7298,"depth":92,"text":7299},{"id":7305,"depth":92,"text":7306},{"id":7357,"depth":92,"text":7358},[197],{"content_references":7369,"triage":7376},[7370,7373],{"type":114,"title":7371,"url":7372,"context":7264},"MONAI","https:\u002F\u002Fgithub.com\u002Fproject-monai\u002Fmonai",{"type":7374,"title":7375,"context":112},"dataset","Medical Segmentation Decathlon Task09",{"relevance":122,"novelty":7270,"quality":122,"actionability":122,"composite":7271,"reasoning":7377},"Category: AI & LLMs. The article provides a detailed tutorial on building a 3D medical segmentation pipeline using MONAI, which directly addresses the audience's need for practical applications in AI engineering. It includes specific techniques like patch-based sampling and mixed precision training, making it actionable for developers looking to implement similar solutions.","\u002Fsummaries\u002F216d50b9ddb75827-building-3d-medical-segmentation-pipelines-with-mo-summary","2026-06-12 12:57:08",{"title":7288,"description":91},{"loc":7378},"216d50b9ddb75827","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F12\u002Fa-coding-implementation-on-monai-for-end-to-end-3d-spleen-segmentation-using-unet-on-medical-ct-volumes\u002F","summaries\u002F216d50b9ddb75827-building-3d-medical-segmentation-pipelines-with-mo-summary",[138,137,136,7282],"This tutorial demonstrates an end-to-end 3D spleen segmentation pipeline using MONAI and a 3D UNet, covering data preprocessing, patch-based training, and sliding-window inference.",[],"t4XqjeP5oMHKKDdW-TLwqgDW2F9PaplfpBm9emBo__o"]