状态状况现状现况*:* 发展是其中的一部分 更清晰. 资料来源来源: GithubMS .com/scottgalMS K2卢西德拉格

何处适合:视频拼音器是 管弦管 会 议 会 排 排 会 会 会 议 会 会总 会 会 更清晰 将五条管道合并成统一的视频分析引擎

• DocSummamer 缩写器 - 文档 * @(_实体提取 ,}知识图表=)
• 图像合成器 @-图像 @ (22- 波浪视觉智能@,_ CLIP 嵌入,}(OCRQZ)) @
• 音频合成器 @-音响@(声波剖析 ,喇叭喇叭
• 视频合成器 @(_这篇文章@)}#-}影片 @(orchestrates 全部三个{,}都添加了枪声*/scene结构 *)}
• 数据合成器 - 數據(schema inferenceMS K2 剖析_)}

都跟着一样 降低的RAG模式“:” 提取信号,一旦“,” 存储证据, 用捆绑的 LLM 输入来合成

在CLIP嵌入框中处理两个-小时的电影框架 -%by}-#Frame 需要数小时和几百美元来计算

VideoSummarizer 以三个密钥优化来解析此选项 @ :

1. 感知性散列体衰变 在昂贵的ML (40%前跳过视觉类似的框架
2. 批量 CLIP 嵌入 @ - process {% 1} 每个 GPU 通道的图像, 而不是一个 * MS K2x creedup_ )}
3. 管道成分 “-”链条图像缩写器,用于键盘“,”“音频拼写器”,用于表达语句‘,’+NER NSK3’_Named 实体识别)}(实体名称){,}DocSummarizer 用于文本分析>, GrapRAG

@: @ a\ 2- @ hour movie program process 在@~10-15 @ minutes @ MS K3} 不是时间 @ MPK4 CLIP 用于 本地语义嵌入Name视觉LMS是可选的,仅用于升级分析

与 图像合成器 和 音频合成器由统一的视频分析管道组成

核心洞察力 视频是图片“+”音频 “+”文本“MSSK2”处理每个有专用工具的域@,}将结果合并到一致场景.

• 第一个进程结构 (cuts,I-framesMS K3}音频段)
• 解压缩跨-}模式信号一次 @(_embeddings@,rought\ ,}实体)

名词@:

• 射击 @-摄像头在 FFmpeg现场探测的切口之间拍摄@(}#MSK 1_structural},}来自FFampeg scenection
• 场景 @-_BARBAR_来自 Groupsó)} (semantic,MSk0}相毗連的射擊群組构成一整體單元 *{MSK 1}sementitic@MSK 2來自集團MS K3
• 证据 - (start_time, end_time) + 信号 @+#指针@+}来源

使用的密钥 ML 模型@:

• CLIP - OpenAI' 反用語言@-Imaage pre @-trainingMS K4生成了#512-维嵌入程式,以编码视觉语义。
• 耳语 - OpenAI's 语音识别模式@; 将音频记录为带有时间戳的文字
• BERT-纳 @- 命名为实体识别@; 从文字中提取人们#, 组织}%, 位置
• NONX 运行时间 -cross @-platform ML 发酵@;}在没有框架锁定的情况下运行 CPU/GPU 模型 *-in

本篇文章涵盖 @:

• 视频成像管弦乐队如何搭配五条输油管道 @ (ImagiageSummarizer @,音频成像机@,NER,DocSummerizer},GMS K5
• 从正常化到证据生成的波浪
• 能力系统:Lazy 模型下载@,GPU检测 @,反应性路由
• 用于键盘嵌入的批量 CLIP 优化@ (3-5 @x faster @ MS K1}
• 缩放框@)
• 多@ - @ 信号场景群集 @ MS K1_ 编队 * +} 抄本 @ MPK3 剪切类型 @ I+ 时间 @ O)
• 从记录誊本中提取实体的NER集成
• 时空原子 限制时间估计率@,}%,}和后压
• 输出@:片段 @ ,_ shools @ MPK2}正本\ ,}作为RAG证据的文本轨迹

相关条款:

• 减少的RAG 核心模式
• 受限制的模糊模式 - 基础模式
• 减少RAG执行量
  • DocSummamer 缩写器 - 具有实体提取功能的文件RAG
  • 图像合成器 @-图像RAG 带有 @MS K1波直观情报
  • 音频合成器 音频法证鉴定
  • 影片缩写器 @ ( @ this article @) @ - 视频情报管弦乐队

问题#:视频昂贵

基准基准下面用AMD 9950QX MS K2Core )+ @/QNIVIA ANSK5}{(16GB)+}/{96GBRA MASK10+NMEQ,}MPK12p H.264,}(口哨基地)_.计算的数字 -of @-磁力效应-=YTET -伊甸园字幕组

一部典型的电影包含着:

• ~170,000_框架 @(2_BARBAR_ 时间在#24_FPS_)>
• ~2-小时音频 @(_speech},}音乐 , 效果 )
• 多文本层 @(_Sub标题@,}incredit*\ ,}在-}屏幕文字=)}

稻草人的方法 @ ( @ nobody does this,}但是它设定了缩放Q):@

• CLIP 按框架嵌入@ : @ @ MS K1 @ ms @ I× @ * 170,000} @ MPK4# # 9.4+#小时
• 每个框架的视野LLM= : MS K1 NSK2 I170,000 MPK4 # 94+#小时 闪烁的视觉API球场 马斯克1号

即便有键盘提取@( @say @,}500-1000_Brame{),}(即使按键框架提取%S'>秒的CLIP连环直导数 <.\Q}

传统方法@"#Extrap keyrames@,_发送到VisionLLM%,}希望最佳的"*

问题:_此刻度计算在冗余框架上 @(}%many keybrames 是视觉相似的 <),}处理它们时序@(}GPU 在框中空闲 ),}♪错过音频=/ text 信号 完全{.

解决方案: 多-阶段过滤 ,批量处理,和管道构成*MS K4

视频放大器结构Name

视频放大器执行工具 减少的RAG 3- 阶段缩减MSC1

flowchart TB
    subgraph Input["Video File (.mp4, .mkv, etc.)"]
        VF[Video File]
    end

    subgraph Stage1["Stage 1: Structural Analysis"]
        N[NormalizeWave<br/>FFprobe metadata]
        SD[ShotDetectionWave<br/>Scene cuts via FFmpeg]
        KE[KeyframeExtractionWave<br/>I-frame + dedup]
        AE[AudioExtractionWave<br/>FFmpeg audio extract]
    end

    subgraph Stage2["Stage 2: Content Extraction"]
        IS[ImageSummarizer<br/>CLIP, OCR, Vision]
        AS[AudioSummarizer<br/>Whisper, Diarization]
        NER[NER Service<br/>Entity extraction]
        DS[DocSummarizer<br/>Text analysis if long]
        GR[GraphRAG<br/>Entity relationships]
    end

    subgraph Stage3["Stage 3: Scene Assembly"]
        SC[SceneClusteringWave<br/>Multi-signal clustering]
        EV[EvidenceGenerationWave<br/>RAG chunks]
    end

    VF --> N
    N --> SD --> KE
    N --> AE
    KE --> IS
    AE --> AS
    IS --> NER
    AS --> NER
    NER --> DS
    NER --> GR
    IS --> SC
    DS --> SC
    GR --> EV
    SC --> EV

    style Stage1 stroke:#22c55e,stroke-width:2px
    style Stage2 stroke:#3b82f6,stroke-width:2px
    style Stage3 stroke:#8b5cf6,stroke-width:2px

产生的证据

在跳入执行前 @,这里 @ '你得到的@(}输出 schema):

每件艺术品包括 来源出处:源波,处理时间戳MS K2信任得分.这些文物是 初级基本检索单位 无法直接获取原始文件@. @%

信号@- @Aware波管道

视频合成器使用 a 基于 signal-基波结构 每一波都明确声明其信号合同

public interface ISignalAwareVideoWave
{
    /// <summary>Signals this wave requires before it can run.</summary>

    IReadOnlyList<string> RequiredSignals { get; }

    /// <summary>Signals this wave can optionally use if available.</summary>

    IReadOnlyList<string> OptionalSignals { get; }

    /// <summary>Signals this wave emits on successful completion.</summary>

    IReadOnlyList<string> EmittedSignals { get; }

    /// <summary>Cache keys this wave produces for downstream waves.</summary>

    IReadOnlyList<string> CacheEmits { get; }

    /// <summary>Cache keys this wave consumes from upstream waves.</summary>

    IReadOnlyList<string> CacheUses { get; }
}

启用此功能 动态波协调:

• 如果缺少所需的信号, 波会自动跳过
• 运行时依赖性解析@ ( @ no hardcoded order * )
• 部分重新运行是用信号键 < ( cache > 复制的{ )\ }
• 用户界面进步颗粒度=:}每波都独立发出进展

@ 16-+# 已跳出管道

Keyframe 提取作为7颗粒波进行,以更好地平行和缓存效率@:

注::

• 图像分析网用途 keyframes.deduplicated @(not full@-res):OCR 运行在缩略图MS K3 视觉字幕使用全=-res, 如果能力通过路由#.}提供的话
• “3-7”是用于缓存效率的“"”键盘提取“MS K2” 子“MSSK3”管线“(7”颗粒波@).}

@2-#小时电影总数@: ~10-15#分钟 @ ( @vs_.}没有优化的时数 @MS K4 @

{\fn黑体\fs22\bord1\shad0\3aHBE\4aH00\fscx67\fscy66\2cHFFFFFF\3cH808080}好的-

信号被定义为一致性的常数@: @%

public static class VideoSignals
{
    // NormalizeWave signals
    public const string VideoDuration = "video.duration";
    public const string VideoFps = "video.fps";
    public const string VideoNormalized = "video.normalized";

    // Shot detection signals
    public const string ShotsDetected = "shots.detected";
    public const string ShotsCount = "shots.count";

    // Keyframe signals
    public const string IframesDetected = "keyframes.iframes_detected";
    public const string KeyframesSelected = "keyframes.selected";
    public const string KeyframesDeduplicated = "keyframes.deduplicated";
    public const string KeyframesExtracted = "keyframes.extracted";

    // CLIP embedding signals
    public const string ClipEmbeddingsReady = "clip.embeddings_ready";

    // Scene clustering signals
    public const string ScenesDetected = "scenes.detected";
    public const string SceneCount = "scene.count";

    // Transcription signals
    public const string TranscriptionComplete = "transcription.complete";
}

能力系统@:懒惰模型 @&路由

视频合成器使用 a 以能力为主的架构@-在启动时, : 检测一次 GPU #, 下载模型 lazily}, 路线工作到可用的组件\ MS K3#

型号声明@( @YAML @+}类型 @MS K2#Safe Constants_)

模型的定义如下: models.yaml@.# 代码中没有魔力字符串

# models.yaml (excerpt)
models:
  clip-vit-b32:
    name: "CLIP ViT-B/32"
    download_url: "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/onnx/visual_model.onnx"
    preferred_providers: [CUDAExecutionProvider, DmlExecutionProvider, CPUExecutionProvider]

components:
  ClipEmbeddingWave:
    models: [clip-vit-b32]
    fallback_chain: [ImageAnalysisWave]

// Type-safe constants (no raw strings)
await coordinator.EnsureModelAsync(ModelIds.ClipVitB32);
await coordinator.ActivateWaveAsync(ComponentIds.TranscriptionWave);

// Route with fallback
var route = await coordinator.RouteWorkAsync(new[]
{
    ComponentIds.ClipEmbeddingWave,    // Primary (GPU)
    ComponentIds.ImageAnalysisWave     // Fallback (CPU)
});

管道效率原子

利率限制@,时间估计 @, 和适应性回压保持UI反应,同时尽量扩大通过量:

// Time estimation from actual data
var estimator = CapabilityAtoms.CreateTimeEstimator();
using (estimator.Time("clip_embedding")) { await ProcessAsync(); }
var eta = estimator.GetEstimate("clip_embedding", remaining: 50);
// eta.Estimated, eta.Optimistic, eta.Pessimistic, eta.Confidence

全能力系统 docs*:* 见 Mostlylucid.Summarizer.Core/Capabilities/ 用于 GPU 检测@,信号棒 @/sub},后压控制器 @MS K3和网状地形设计

键优化 *1:\ 感知散装物应用

在使用视觉类似的框架运行昂贵的 CLIP 嵌入器前@ , @ VideoSummarizer 过滤器 hash=(=dHash= )=.

DHash 如何工作

public class KeyframeDeduplicationService
{
    // dHash parameters: 9x8 grayscale = 64 bits
    private const int HashWidth = 9;
    private const int HashHeight = 8;
    private const int DefaultHammingThreshold = 10;

    public async Task<ulong> ComputeDHashAsync(string imagePath, CancellationToken ct)
    {
        using var image = Image.Load<Rgba32>(imagePath);

        // Resize to 9x8 (one extra column for gradient comparison)
        image.Mutate(x => x
            .Resize(HashWidth, HashHeight)
            .Grayscale());

        ulong hash = 0;
        int bit = 0;

        // Compare adjacent pixels horizontally
        for (int y = 0; y < HashHeight; y++)
        {
            for (int x = 0; x < HashWidth - 1; x++)
            {
                var left = image[x, y].R;
                var right = image[x + 1, y].R;

                // Set bit if left pixel is brighter than right
                if (left > right)
                {
                    hash |= (1UL << bit);
                }
                bit++;
            }
        }

        return hash;
    }

    public static int HammingDistance(ulong a, ulong b) =>
        BitOperations.PopCount(a ^ b);
}

示例输出:

Input: 50 keyframe candidates (from codec I-frames)

Deduplication (Hamming threshold 10):
  Frame 0: hash=0x8f3a2c1d → KEEP (first frame)
  Frame 1: hash=0x8f3a2c1e → SKIP (distance=1 from frame 0)
  Frame 2: hash=0x8f3a2c1f → SKIP (distance=2 from frame 0)
  Frame 3: hash=0xc7e1b4a2 → KEEP (distance=28 from frame 0)
  ...

Result: 50 → 30 frames (40% reduction)
Processing saved: ~8 seconds of CLIP inference

为什么这重要?

• *~40% 框架缩减 典型内容
• <1%s 每个框架 用于计算 cLIP ) 的 hash 计算 ( @vs@.} @ MS K2% ]
• 过滤冗余框架 之前 昂贵的 GPU 操作

密钥优化 *2:批次 CLIP 嵌入

而不是在一个时间里处理一个图像@, @ VideoSummerizer 批量 @8}每个 GPU pass @ .

批处理架构

public class BatchClipEmbeddingService
{
    private const int ClipImageSize = 224;
    private const int DefaultBatchSize = 8; // 8 images per GPU pass

    public async Task<Dictionary<int, float[]>> GenerateBatchEmbeddingsAsync(
        Dictionary<int, string> framePaths,
        IBackpressureController backpressure,
        ITimeEstimator estimator,
        int batchSize = DefaultBatchSize,
        CancellationToken ct = default)
    {
        var session = await GetOrLoadClipModelAsync(ct);
        var results = new ConcurrentDictionary<int, float[]>();

        // Pre-index batch for O(1) lookup
        var batches = framePaths
            .Select((kvp, idx) => (idx, kvp.Key, kvp.Value))
            .Chunk(batchSize)
            .ToList();

        foreach (var batch in batches)
        {
            // Acquire slot from backpressure controller (adaptive concurrency)
            using var slot = await backpressure.AcquireSlotAsync(ct);
            using var timer = estimator.Time("clip_batch");

            // Create batch tensor [batchSize, 3, 224, 224]
            var tensor = new DenseTensor<float>(new[] { batch.Length, 3, ClipImageSize, ClipImageSize });

            // Preprocess images into tensor slots
            for (int i = 0; i < batch.Length; i++)
            {
                var (_, frameIndex, path) = batch[i];
                PreprocessImageToTensor(path, tensor, slotIndex: i);
            }

            // Single GPU pass for entire batch
            var inputs = new List<NamedOnnxValue>
            {
                NamedOnnxValue.CreateFromTensor("input", tensor)
            };

            using var outputResults = session.Run(inputs);
            // Extract embeddings, record latency for backpressure adjustment
        }

        backpressure.RecordLatency(estimator.GetAverageDuration("clip_batch"));
        return new Dictionary<int, float[]>(results);
    }
}

性能比较:*

Input: 30 keyframes (after deduplication)

Serial processing (1 frame at a time):
  30 × 200ms = 6,000ms (6.0 seconds)

Batch processing (8 frames per pass):
  4 batches × 350ms = 1,400ms (1.4 seconds)

Speedup: 4.3x

批量处理为何有效 @:

• GPU 平行法没有被充分利用,
• 批次电压 [8, 3, 224, 224] 使用与单个图像相同的 GPU 内存 *% 1
• 运行时间优化内部的批量作业

密钥优化 *3:管道构成

图像合成器或音频合成器@'}它 连锁链 {\fn黑体\fs22\bord1\shad0\3aHBE\4aH00\fscx67\fscy66\2cHFFFFFF\3cH808080}他们...

Keyframe Sub% -Pipeline:图像合成器集成

键盘提取法被分割成“7” 颗粒波 ( 请参见上方的浪表).}*这里='}*协调模式显示它们是如何连接在一起的:}

// IFrameDetectionWave → KeyframeSelectionWave → ThumbnailExtractionWave
// → KeyframeDeduplicationWave → KeyframeFullResExtractionWave → ClipEmbeddingWave

// ClipEmbeddingWave coordinates with ImageSummarizer
public class ClipEmbeddingWave : IVideoWave, ISignalAwareVideoWave
{
    private readonly BatchClipEmbeddingService _batchClipService;
    private readonly IBackpressureController _backpressure;
    private readonly ITimeEstimator _estimator;

    public IReadOnlyList<string> RequiredSignals => [VideoSignals.KeyframesExtracted];
    public IReadOnlyList<string> EmittedSignals => [VideoSignals.ClipEmbeddingsReady];

    public async Task ProcessAsync(VideoContext context, CancellationToken ct)
    {
        var keyframes = context.GetCached<Dictionary<int, string>>("keyframes.paths");

        // Batch CLIP embedding with backpressure control
        var embeddings = await _batchClipService.GenerateBatchEmbeddingsAsync(
            keyframes, _backpressure, _estimator, batchSize: 8, ct);

        foreach (var (frameIndex, embedding) in embeddings)
            context.KeyframeEmbeddings[frameIndex] = embedding;
    }
}

// ImageAnalysisWave runs ImageSummarizer on deduplicated frames
public class ImageAnalysisWave : IVideoWave, ISignalAwareVideoWave
{
    public IReadOnlyList<string> RequiredSignals => [VideoSignals.KeyframesDeduplicated];

    public async Task ProcessAsync(VideoContext context, CancellationToken ct)
    {
        var keyframePaths = context.GetCached<List<string>>("keyframes.deduplicated_paths");

        foreach (var path in keyframePaths)
        {
            // Run ImageSummarizer for OCR, vision, captions
            var result = await _imageOrchestrator.AnalyzeAsync(path, ct);
            context.SetCached($"image_analysis.{Path.GetFileName(path)}", result);
        }
    }
}

音频合成器集成

音频提取和转录现在是分开的信号 -aware 波 :

// AudioExtractionWave runs first (extracts audio track from video)
public class AudioExtractionWave : IVideoWave, ISignalAwareVideoWave
{
    public IReadOnlyList<string> RequiredSignals => [VideoSignals.VideoNormalized];
    public IReadOnlyList<string> EmittedSignals => ["audio.extracted", "audio.path"];

    public async Task ProcessAsync(VideoContext context, CancellationToken ct)
    {
        var audioPath = await _ffmpegService.ExtractAudioAsync(
            context.VideoPath, context.WorkingDirectory, ct);
        context.SetCached("audio.path", audioPath);
    }
}

// TranscriptionWave depends on audio.extracted signal
public class TranscriptionWave : IVideoWave, ISignalAwareVideoWave
{
    public IReadOnlyList<string> RequiredSignals => ["audio.extracted"];
    public IReadOnlyList<string> EmittedSignals => [
        VideoSignals.TranscriptionComplete,
        "transcription.utterance_count"
    ];

    public async Task ProcessAsync(VideoContext context, CancellationToken ct)
    {
        var audioPath = context.GetCached<string>("audio.path");

        // Run AudioSummarizer pipeline (Whisper + diarization)
        var audioProfile = await _audioOrchestrator.AnalyzeAsync(audioPath, ct);

        // Extract utterances with speaker info
        var turns = audioProfile.GetValue<List<SpeakerTurn>>("speaker.turns");
        foreach (var turn in turns ?? [])
        {
            context.Utterances.Add(new Utterance
            {
                Id = Guid.NewGuid(),
                Text = turn.Text,
                StartTime = turn.StartSeconds,
                EndTime = turn.EndSeconds,
                SpeakerId = turn.SpeakerId,
                Confidence = turn.Confidence
            });
        }

        // Run NER on full transcript for entity extraction
        var transcript = audioProfile.GetValue<string>("transcription.full_text");
        if (!string.IsNullOrEmpty(transcript))
        {
            var entities = await _nerService.ExtractEntitiesAsync(transcript, ct);
            context.SetCached("transcript_entities", entities);

            // Emit entity signals by type (PER, ORG, LOC, MISC)
            foreach (var group in entities.GroupBy(e => e.Type))
            {
                context.AddSignal($"transcript.entities.{group.Key.ToLowerInvariant()}",
                    group.Select(e => e.Text).Distinct().ToList());
            }
        }
    }
}

NER 整合:命名实体识别

使用 BERT-}以 NER 为基地的 NER {(}ONNXMS K2}从笔录中提取命名实体。

OnnxNer 服务

public class OnnxNerService
{
    // Model: dslim/bert-base-NER (ONNX exported)
    // Entities: PER (Person), ORG (Organization), LOC (Location), MISC (Miscellaneous)

    public async Task<List<EntitySpan>> ExtractEntitiesAsync(string text, CancellationToken ct)
    {
        var entities = new List<EntitySpan>();

        // Chunk long text (BERT max 512 tokens)
        foreach (var chunk in ChunkText(text, maxTokens: 400, overlap: 50))
        {
            // Tokenize with WordPiece
            var tokens = _tokenizer.Tokenize(chunk);

            // Run ONNX inference
            var inputs = PrepareInputs(tokens);
            using var results = _session.Run(inputs);

            // Decode BIO tags
            var predictions = DecodePredictions(results);
            var chunkEntities = ExtractEntitySpans(tokens, predictions);

            entities.AddRange(chunkEntities);
        }

        // Deduplicate entities
        return entities
            .GroupBy(e => (e.Text.ToLowerInvariant(), e.Type))
            .Select(g => g.First())
            .ToList();
    }
}

示例输出:

Transcript: "Today we're speaking with John Smith from Microsoft about
their new AI lab in Seattle. The project, codenamed Phoenix, builds
on research from Stanford University."

Entities extracted:
  PER: John Smith
  ORG: Microsoft, Stanford University
  LOC: Seattle
  MISC: Phoenix

Signals emitted:
  transcript.entities.per = ["John Smith"]
  transcript.entities.org = ["Microsoft", "Stanford University"]
  transcript.entities.loc = ["Seattle"]
  transcript.entities.misc = ["Phoenix"]

为什么NER对视频很重要?

• 启用像 @ " find 视频这样的查询, 提及微软@ MS K1 {
• 与 DocSummerizer 实体图的链接
• 提供结构化元数据,不推断LLM

多@ - @ 信号场景集束

仅使用 CLIP 嵌入器进行现场检测, 效果不错 @' @twork works matter {. Keyframes 在设计上是稀疏的 每场变化 (one@),}但镜头很稠密 □.}但是在 &1881}拍摄 39嵌入仪中

视频合成器使用 a mount -_signal 方针 结合了“4”加权信号,以进行稳健的现场边界探测; “.” 确定性加权评分@,_没有学到结尾@-to%-}@end:{

场景 CloenClustering Wave @ : @ 多@ 多@ MS K1 @ 信号建筑

public class SceneClusteringWave : IVideoWave, ISignalAwareVideoWave
{
    // Signal weights for boundary scoring
    private const double EmbeddingWeight = 0.4;   // CLIP embedding dissimilarity
    private const double TranscriptWeight = 0.3;  // Semantic shift in transcript
    private const double CutTypeWeight = 0.2;     // Fade/dissolve detection
    private const double TemporalWeight = 0.1;    // Time since last scene

    // Temporal constraints
    private const double MinSceneDuration = 15.0;   // Don't split scenes < 15s
    private const double MaxSceneDuration = 300.0;  // Force split at 5 minutes
    private const double TargetSceneDuration = 90.0; // Prefer ~90s scenes

    public IReadOnlyList<string> RequiredSignals => [VideoSignals.ShotsDetected];
    public IReadOnlyList<string> OptionalSignals => [
        VideoSignals.ClipEmbeddingsReady,
        VideoSignals.TranscriptionComplete,
        VideoSignals.KeyframesDeduplicated
    ];
    public IReadOnlyList<string> EmittedSignals => [
        VideoSignals.ScenesDetected,
        "scene.count",
        "scene.avg_duration",
        "scene.clustering_method"
    ];

    private List<(int shotIndex, double score)> ComputeBoundaryScores(VideoContext context)
    {
        var shots = context.Shots.OrderBy(s => s.StartTime).ToList();
        var scores = new List<(int, double)>();

        // Build embedding map with nearest-neighbor interpolation
        var shotEmbeddings = PropagateEmbeddingsToNearbyShots(context, shots);

        // Build transcript windows for semantic shift detection
        var transcriptWindows = BuildTranscriptWindows(context, shots, windowSeconds: 10);

        for (int i = 0; i < shots.Count - 1; i++)
        {
            double score = 0;
            var currentShot = shots[i];
            var nextShot = shots[i + 1];

            // 1. Embedding dissimilarity (40%)
            if (shotEmbeddings.TryGetValue(i, out var currentEmbed) &&
                shotEmbeddings.TryGetValue(i + 1, out var nextEmbed))
            {
                var similarity = CosineSimilarity(currentEmbed, nextEmbed);
                score += (1.0 - similarity) * EmbeddingWeight;
            }

            // 2. Transcript semantic shift (30%)
            if (transcriptWindows.TryGetValue(i, out var currentWords) &&
                transcriptWindows.TryGetValue(i + 1, out var nextWords))
            {
                var overlap = currentWords.Intersect(nextWords).Count();
                var union = currentWords.Union(nextWords).Count();
                var jaccard = union > 0 ? (double)overlap / union : 0;
                score += (1.0 - jaccard) * TranscriptWeight;
            }

            // 3. Cut type signal (20%) - fades/dissolves suggest scene boundaries
            if (currentShot.CutType is "fade" or "dissolve")
            {
                score += CutTypeWeight;
            }

            // 4. Temporal pressure (10%) - encourage splits near target duration
            var timeSinceLastScene = currentShot.EndTime - GetLastSceneBoundary();
            if (timeSinceLastScene > TargetSceneDuration)
            {
                var pressure = Math.Min(1.0, (timeSinceLastScene - TargetSceneDuration) / 60);
                score += pressure * TemporalWeight;
            }

            scores.Add((i, score));
        }

        return scores;
    }
}

关键创新

1. 近邻内嵌入式孕育“: ” 只有“~2% 」 有直接CLIP嵌入的 CLIP}. 新的方法通过时间接近重量,在“MS K3 秒内将嵌入附近的镜头。

2. 标定语义窗口: 建構10- 第二個單字視窗, 透過 Jaccar 距离偵測語句移動 MS K2a 廉價但強健的語言流動代理 *). 低重複 <= 題變化\ .BMQ} BM_25 當可用時可以使用或嵌入漂移

3. 认识切剪类型@:_Fade-to-_Black and unformissions 强力表示场景边界@,}提升边界评分=.{

4. 适应性推进权“:”不是固定的阈值,而是从“25%”和“(”中分数的顶端选择边界。

5. 时间制约因素“:”强制实施最小的“15”场景和部队边界(在 @5-”)

实例::

Input: 1881 shots from a 2-hour movie
       39 keyframes with CLIP embeddings
       2302 utterances from transcript

Boundary scoring per shot:
  Shot 45-46: embedding=0.15, transcript=0.32, cut=0.0, temporal=0.0 → score=0.156
  Shot 46-47: embedding=0.08, transcript=0.12, cut=0.0, temporal=0.0 → score=0.068
  Shot 47-48: embedding=0.35, transcript=0.41, cut=0.2, temporal=0.05 → score=0.388 ← BOUNDARY
  ...

Adaptive threshold (top 25%): 0.25
Natural boundaries found: 45

Output: 47 scenes (avg 2.6 minutes per scene)
  - Min scene: 15.2s
  - Max scene: 298.4s
  - Total coverage: 100%

Signals:
  scenes.detected = true
  scene.count = 47
  scene.avg_duration = 156.3
  scene.clustering_method = "multi_signal_weighted"

视频信号合同

VideoSummarizer 延长了图像合成器和音频合成器的信号合同

public record VideoSignal
{
    public required string Key { get; init; }      // "scene.count", "transcript.entities.per"
    public object? Value { get; init; }
    public double Confidence { get; init; } = 1.0;
    public required string Source { get; init; }   // "SceneClusteringWave"

    // Video-specific: time range
    public double? StartTime { get; init; }
    public double? EndTime { get; init; }

    public DateTime Timestamp { get; init; }
    public Dictionary<string, object>? Metadata { get; init; }
    public List<string>? Tags { get; init; }       // ["visual", "scene"]
}

public static class VideoSignalTags
{
    public const string Visual = "visual";
    public const string Audio = "audio";
    public const string Speech = "speech";
    public const string Ocr = "ocr";
    public const string Motion = "motion";
    public const string Scene = "scene";
    public const string Shot = "shot";
    public const string Metadata = "metadata";
}

关键信号发出@: @%

-=YTET -伊甸园字幕组=- 翻译: |--------|--------|-------------| | video.duration 以秒计的总持续时间@| | video.resolution “| 正常生活”“|+Width @×QH8 |” | video.fps “|” 正常生活“|” 框架速率“MS K2” | shots.count 侦测到的射击次数 | keyframes.count “| ” 键盘提取除去“|” 后唯一的键盘“MSC2” | keyframes.duplicates_skipped 由 dHash 过滤的 | 框架 | scene.count @| 场景清新 {|} 相合场景片段| | transcript.entities.per @|_TrannprificingWave@|}来自NER的名人 {|} | transcript.entities.org 组织名称 | | transcript.word_count | Transcription Wave @| 抄本中总单词@|

视频 Pipeline:RAG 输出

缩略 VideoPipeline 将视频信号转换为 ContentChunk 对于 RAG 指数化 < :\ }

public class VideoPipeline : PipelineBase
{
    public override string PipelineId => "video";
    public override IReadOnlySet<string> SupportedExtensions => new HashSet<string>
    {
        ".mp4", ".mkv", ".avi", ".mov", ".wmv", ".webm", ".flv", ".m4v", ".mpeg", ".mpg"
    };

    private List<ContentChunk> BuildContentChunks(VideoContext context, string filePath)
    {
        var chunks = new List<ContentChunk>();

        // 1. Scene-based chunks (best for video retrieval)
        foreach (var scene in context.Scenes)
        {
            var sceneText = BuildSceneText(context, scene);
            var embedding = context.GetCached<float[]>($"scene_centroid.{scene.Id}");

            chunks.Add(new ContentChunk
            {
                Text = sceneText,
                ContentType = ContentType.Summary,
                Embedding = embedding,  // Proper vector column, not metadata
                Metadata = new Dictionary<string, object?>
                {
                    ["source"] = "video_scene",
                    ["scene_id"] = scene.Id,
                    ["key_terms"] = scene.KeyTerms,
                    ["speakers"] = scene.SpeakerIds,
                    ["start_time"] = scene.StartTime,
                    ["end_time"] = scene.EndTime
                }
            });
        }

        // 2. Transcript chunks (1-minute windows)
        var transcriptChunks = BuildTranscriptChunks(context, filePath);
        chunks.AddRange(transcriptChunks);

        // 3. Text track chunks (on-screen text/subtitles)
        foreach (var textTrack in context.TextTracks)
        {
            chunks.Add(new ContentChunk
            {
                Text = $"On-screen text: {textTrack.Text}",
                ContentType = ContentType.ImageOcr,
                Metadata = new Dictionary<string, object?>
                {
                    ["source"] = "video_ocr",
                    ["text_type"] = textTrack.TextType.ToString(),
                    ["start_time"] = textTrack.StartTime
                }
            });
        }

        return chunks;
    }

    private string BuildSceneText(VideoContext context, SceneSegment scene)
    {
        var parts = new List<string>();

        if (!string.IsNullOrEmpty(scene.Label))
            parts.Add($"Scene: {scene.Label}");

        parts.Add($"[{FormatTime(scene.StartTime)} - {FormatTime(scene.EndTime)}]");

        if (scene.KeyTerms.Count > 0)
            parts.Add($"Topics: {string.Join(", ", scene.KeyTerms)}");

        // Add utterances in this scene
        var sceneUtterances = context.Utterances
            .Where(u => u.StartTime >= scene.StartTime && u.EndTime <= scene.EndTime)
            .OrderBy(u => u.StartTime);

        if (sceneUtterances.Any())
            parts.Add($"Speech: {string.Join(" ", sceneUtterances.Select(u => u.Text))}");

        return string.Join("\n", parts);
    }
}

电影的示例输出@:

{
  "chunks": [
    {
      "text": "Scene: Opening montage\n[0:00 - 2:34]\nTopics: city, night, traffic\nSpeech: The year is 2049. The world has changed.",
      "contentType": "Summary",
      "metadata": {
        "source": "video_scene",
        "scene_id": "abc123",
        "key_terms": ["city", "night", "traffic"],
        "start_time": 0.0,
        "end_time": 154.0
      }
    },
    {
      "text": "The detective arrived at the crime scene. Forensics had already processed the area.",
      "contentType": "Transcript",
      "metadata": {
        "source": "video_transcript",
        "time_window": "2:34 - 3:34",
        "utterance_count": 4
      }
    },
    {
      "text": "On-screen text: LOS ANGELES 2049",
      "contentType": "ImageOcr",
      "metadata": {
        "source": "video_ocr",
        "text_type": "Title"
      }
    }
  ]
}

性能特点

处理时间 @ (2-_ hour movie@ MS K1} @ I1080p)

-=YTET -伊甸园字幕组=- 翻译: |-------|------|-------| | FFprobe元数据 @| @#~2#s@|} @MS K4# -=YTET -伊甸园字幕组=- 翻译: | 键盘提取@| @ @~30 @s @s|}(I500) |#dHash dedudition |} @~0.5%s | MS K4 NSK5 MSSK6 框架@| | 批量CLIP 嵌入 @| ~60s *|# MS K4框,批量@8{|} |图像放大器 OCR | ~120s @|+50键盘和文本@|# @| @音频提取@|}#~30 @s | @FFmpeg @s|# @|whyworld 发言时间#| -=YTET -伊甸园字幕组=- 翻译: |N NER提取 I|Q MS K2QS |BERT-NER 在笔录@|QZ @| @ 场景群集 @ |# @ @ ~5 @s @s| @s|} @sk4@ -=YTET字幕组=- 翻译: | 共计共计 | 分钟 | |

没有优化

内存使用

与清晰合成集成

视频合成器登记为 IPipeline 自动路由@: @%

// In Program.cs
builder.Services.AddDocSummarizer(builder.Configuration.GetSection("DocSummarizer"));
builder.Services.AddDocSummarizerImages(builder.Configuration.GetSection("Images"));
builder.Services.AddVideoSummarizer();  // NEW
builder.Services.AddPipelineRegistry(); // Must be last

// Auto-routing by extension
var registry = services.GetRequiredService<IPipelineRegistry>();
var pipeline = registry.FindForFile("movie.mp4");  // Returns VideoPipeline
var result = await pipeline.ProcessAsync("movie.mp4");

支持的扩展@: @%

• .mp4, .mkv, .avi, .mov, .wmv, .webm, .flv, .m4v, .mpeg, .mpg

你得到的

• 场景-级RAG块:集成片段,有笔录{,}关键名词_,喇叭
• 多重-}模式证据: 視覺 K1 Keyframe 嵌入 *, 音效 (speaker diarization *MS K4 文字 MSKO5OCR, 字幕)
• 命名实体@:People,_Organizations\ ,}来自抄本 NER
• 可审计来源每个信号都有源波 , 信任 MS K2 时间戳
• 高效处理@2-@hour 电影时间: @(_nonot#)>

成本多少

• ~1.5GB GPU 内存 适合所有ONNX模型
• ~8-10 @ 分钟处理 每部2- 小时电影
• 磁盘空间 自动清理@)}%
• 复杂程度需要了解波的依附关系

结论结论

视频合成器显示 管道构成 缩放:

1. 专用管道再利用:唐't 重塑图像合成器或音频合成器@.
2. 在昂贵操作前过滤:dHash解码成本
3. 批次 GPU 操作@ : @ @ I8 图像/ 每過程@ MS K2 @ *3-5x crapup
4. 内容前的抽取结构-=YTET -伊甸园字幕组=- 翻译:
5. 懒惰模式管理@ : 下载模型只在需要时@ MS K1 自动检测 GPU
6. 反反应路由@: 现有部件的路线工程@, 优雅地后退

结果是“:+”一部Z2-Qhour电影变成一个结构化的信号分类账,上面写着: “,+Creprises@,+Besties @,}和嵌入式=-QQAready for RAG questions like:}。

• John Smith在讨论微软的"时,
• @"_Show 剪辑,上面有#-}关于凤凰计划的屏幕文字 200)}"
• @"_Find video 类似此场景的视频@"}{(}CLIP 嵌入搜索中=)}

视频“ :” 减少的 RAG 模式Name

Ingestion:  Video → 16 waves → Signals + Evidence (scenes, transcripts, entities)
Storage:    Signals (indexed) + Embeddings (CLIP, voice) + Evidence (chunks)
Query:      Filter (SQL) → Search (BM25 + vector) → Synthesize (LLM, ~5 results)

能力系统@: @%

Startup:    Detect GPU → Load ModelManifest (YAML) → Initialize SignalSink
Activation: Component requests model → Lazy download → Signal "ModelAvailable"
Routing:    Route to best provider → Fallback chain → Backpressure control
Atoms:      Rate limiting + Time estimation + Pipeline balancing

这是 受限制的模糊 缩放时@: @%

• 概率组成部分提出信号:嵌入中 @ MS K1CLIP),OCR假想@,diarization 猜測
• 确定性评分组装结构:加权边界分数,门槛选择MS K2时间限制
• LLM {(}可选})根据证据合成:绑定的上下文 @,可审计来源

法学硕士在 -=YTET -伊甸园字幕组@,_ never raw video #.} 从未有过原始的视频#MSK1

资源资源资源 资源和资源资源资源

清晰的RAG 文件

• 视频成像器库 - 源代码
• NNER 文件 -实体采掘结构

相关图书馆

• 图像合成器 视觉情报管道
• 音频合成器 音频法证管道
• DocSummamer 缩写器 -文件编审中

ONNX模型

• CLIP Vit-B/32 - 视觉嵌入器
• ECAPA-TDNN(坦桑尼亚) @- 发言人嵌入
• BERT-纳 - 命名实体识别
• 耳语 - 语音转录

相关条款

核心模式

• 减少的RAG 核心模式
• 受限制的模糊模式 基础模式

减少RAG执行量

• DocSummamer 缩写器 - 具有实体提取功能的文件RAG
• 图像合成器 @-图像RAG 带有 @MS K1波直观情报
  • 3-Tier OCR管道 OCR升级
• 音频合成器 音频法证鉴定
• 影片缩写器 @ ( @ this article @) @ - 视频情报管弦乐队
• 数据合成器 - 数据剖析和计划推论

系列丛书

下一頁: 多- 模式图 RAG 有清晰的RAGMS K2 将所有五个摘要集合并成一个统一的知识图表, 与连接@.}

所有部件都沿着相同的变数 : 建议的概率元件.