{
  "text": "大家好,最近一個名為Dark Bloom的項目,熱度驟增。它可以把許多閒置的Mac設備連成一個算力網絡。有人就把一台M5Mac 128GB內存的Mac進入了Dark Bloom,24小時內處理了13000多個請求,賺到了4.11美元,並且體現成功。至於電費,大約賺到收入的5%以及磨損其實都構不成問題。也就是說,在你桌上的Mac不用的時候,它可以替別人跑AI,順帶賺點錢。\n至少前沿模型的訂閱費就有了。這個由Icon Labs推出的分布式AI推理網絡,機制大概有這幾步。首先,開發者發起請求,Dark Bloom的調度中心負責挑一台合適的Mac,把這個請求加密發過去。Mac本地用MLX運行開運模型,再將結果加密返回。這個項目疊加了Secure Enclave,Secure Boot,還有設備認證,簽名程序,內存隔離等Apple的安全機制。\n嚴格來說,雖然中央調度方仍然存在信任邊界,不是絕對的端了端加密,但它比一般重包算力認真的多。\nDark Bloom的成長速度非常快,今年4月上線,6月介入OpenRouter,7月初單日Token突破10億,7月底重購MLX的連續Batching Prefail速度提升3-4倍,到8月成為了OpenRouter的付費Provider。\n今天最新數據顯示,Dark Bloom已經介入1380台機器,網絡聚合內存123.2TB,AR2大約是22.2萬美元,實現一周翻倍。\n當然它的提升空間還是很大的,比如峰值利用率為15%,說明願意賣算力的機器比較多,而真實付費需求還沒有完全跟上。\n另外,收益分佈其實並不均勻,比如前面說的那台M5Max,它一天是賺了4.11美元,同時還有另一台M324GB的Mac,是在8個小時內一個任務都沒有接到的。\n所以也許調度它更偏向高內存,認證狀態好的機器。\n再者,它的延遲、穩定性還有知識模型數量等,勢必會繼續完善。\n上周末,剛看到這個項目的時候,想起幾年前有研究過分佈式計算,Distributed Computing,而YouTuber Matthew Burman就這樣找到,\nDart Bloom稱為數據中心殺手。\n他說,\nJust imagine we have a million of these nodes set up,\nThat is major inference power that anybody can tap into at a fraction of the price of going to an open AI or an anthropic,\nand it's completely distributed,it's completely built by regular people。\n當推理需求暴漲,而開源模型、MOE和電話技術等,又正好把一部分能力壓到了Mac、工作站還有AIPC這類邊緣設備上,\n那麼原本散落在千家萬戶的沉睡算力,就有了被組織成公共基礎設施的可能。\n數據中心仍會守住訓練和高并發推理,但那些隱私敏感突發的常委的成本敏感的推理需求,完全可以流向一張有可驗證邊緣設備組成的網路。\n最後介紹一下DarkGloom的核心開發者Gajesh,他7歲開始寫代碼,10歲給創業公司做聊天機器人,\n13歲創建了一個基於區塊鏈的加密貨幣理財管理工具,資產管理規模一度達到700萬美元,15歲加入EigenLabs,\n今年Gajesh干滿18歲,致力於打造開放網絡,加速開放智能。\n這期節目就到這裡,Thanks for listening!\n我是Kyoli,Thanks for listening!",
  "language": "zh",
  "duration": 195.999625,
  "segments": [
    {
      "id": 0,
      "seek": 0,
      "start": 0.0,
      "end": 29.560000000000002,
      "text": "大家好,最近一個名為Dark Bloom的項目,熱度驟增。它可以把許多閒置的Mac設備連成一個算力網絡。有人就把一台M5Mac 128GB內存的Mac進入了Dark Bloom,24小時內處理了13000多個請求,賺到了4.11美元,並且體現成功。至於電費,大約賺到收入的5%以及磨損其實都構不成問題。也就是說,在你桌上的Mac不用的時候,它可以替別人跑AI,順帶賺點錢。",
      "tokens": [
        50365,
        15248,
        11,
        46777,
        8990,
        15940,
        6344,
        35,
        809,
        25927,
        1546,
        8313,
        227,
        11386,
        11,
        32745,
        13127,
        24023,
        253,
        24228,
        252,
        1543,
        11284,
        6723,
        16075,
        40914,
        6392,
        5196,
        240,
        34719,
        1546,
        34355,
        39035,
        20854,
        36252,
        11336,
        8990,
        19497,
        13486,
        26352,
        6948,
        94,
        1543,
        45820,
        3111,
        16075,
        2257,
        15433,
        44,
        20,
        34355,
        29810,
        8769,
        28472,
        39781,
        1546,
        34355,
        18214,
        14028,
        2289,
        35,
        809,
        25927,
        11,
        7911,
        46997,
        28472,
        39289,
        13876,
        2289,
        7668,
        1360,
        6392,
        3338,
        16302,
        32718,
        11,
        13352,
        118,
        21381,
        19,
        13,
        5348,
        39433,
        11,
        35934,
        20334,
        23987,
        9581,
        11336,
        32311,
        1543,
        20844,
        19488,
        20545,
        50071,
        11,
        3582,
        30889,
        13352,
        118,
        4511,
        18681,
        14028,
        1546,
        20,
        4,
        40282,
        163,
        13033,
        22220,
        235,
        14139,
        7182,
        43362,
        1960,
        11336,
        17197,
        1543,
        6404,
        26222,
        11,
        3581,
        2166,
        162,
        5475,
        5708,
        1546,
        34355,
        24384,
        20643,
        11,
        11284,
        6723,
        9531,
        123,
        16158,
        4035,
        32585,
        48698,
        11,
        8313,
        228,
        32379,
        13352,
        118,
        8216,
        29483,
        1543,
        51843
      ],
      "temperature": 0,
      "avg_logprob": -0.15426332072207802,
      "compression_ratio": 1.2078651685393258,
      "no_speech_prob": 8.688429316283663e-12,
      "source": "whisper"
    },
    {
      "id": 1,
      "seek": 2956,
      "start": 29.56,
      "end": 54.56,
      "text": "至少前沿模型的訂閱費就有了。這個由Icon Labs推出的分布式AI推理網絡,機制大概有這幾步。首先,開發者發起請求,Dark Bloom的調度中心負責挑一台合適的Mac,把這個請求加密發過去。Mac本地用MLX運行開運模型,再將結果加密返回。這個項目疊加了Secure Enclave,Secure Boot,還有設備認證,簽名程序,內存隔離等Apple的安全機制。",
      "tokens": [
        50365,
        20844,
        15686,
        8945,
        3308,
        123,
        41908,
        39823,
        1546,
        30215,
        50071,
        3111,
        2412,
        2289,
        1543,
        6287,
        23786,
        40,
        1671,
        40047,
        33597,
        7781,
        1546,
        6627,
        34688,
        27584,
        48698,
        33597,
        13876,
        26352,
        6948,
        94,
        11,
        17543,
        25491,
        32044,
        2412,
        2664,
        23575,
        31429,
        1543,
        36490,
        11,
        8949,
        14637,
        12444,
        14637,
        9147,
        16302,
        32718,
        11,
        35,
        809,
        25927,
        1546,
        32943,
        13127,
        5975,
        7945,
        11561,
        254,
        11561,
        105,
        49067,
        2257,
        15433,
        14245,
        3330,
        102,
        1546,
        34355,
        11,
        16075,
        6287,
        16302,
        32718,
        9990,
        47838,
        14637,
        36910,
        1543,
        34355,
        8802,
        10928,
        9254,
        12683,
        55,
        32772,
        8082,
        8949,
        32772,
        41908,
        39823,
        11,
        8623,
        34168,
        35181,
        9990,
        47838,
        3316,
        242,
        8350,
        1543,
        6287,
        8313,
        227,
        11386,
        18140,
        232,
        9990,
        2289,
        29511,
        540,
        29584,
        27995,
        11,
        29511,
        540,
        37263,
        11,
        15569,
        39035,
        20854,
        22041,
        11823,
        231,
        11,
        24579,
        121,
        15940,
        29649,
        6346,
        237,
        11,
        28472,
        39781,
        165,
        1206,
        32662,
        10187,
        9132,
        306,
        1546,
        45466,
        17543,
        25491,
        1543,
        51615
      ],
      "temperature": 0,
      "avg_logprob": -0.12914863586425782,
      "compression_ratio": 1.186246418338109,
      "no_speech_prob": 1.4848459267691183e-11,
      "source": "whisper"
    },
    {
      "id": 2,
      "seek": 5456,
      "start": 54.56,
      "end": 62.56,
      "text": "嚴格來說,雖然中央調度方仍然存在信任邊界,不是絕對的端了端加密,但它比一般重包算力認真的多。",
      "tokens": [
        50365,
        29593,
        112,
        30921,
        36979,
        11,
        6306,
        244,
        5823,
        5975,
        1787,
        106,
        32943,
        13127,
        9249,
        1550,
        235,
        5823,
        39781,
        3581,
        17665,
        26443,
        14901,
        21185,
        11,
        7296,
        6948,
        243,
        2855,
        1546,
        11957,
        107,
        2289,
        11957,
        107,
        9990,
        47838,
        11,
        8395,
        11284,
        11706,
        2257,
        49640,
        12624,
        23305,
        19497,
        13486,
        22041,
        8034,
        6392,
        1543,
        50765
      ],
      "temperature": 0,
      "avg_logprob": -0.10623514118479259,
      "compression_ratio": 1.085987261146497,
      "no_speech_prob": 1.669749755128791e-11,
      "source": "whisper"
    },
    {
      "id": 3,
      "seek": 5456,
      "start": 62.56,
      "end": 77.56,
      "text": "Dark Bloom的成長速度非常快,今年4月上線,6月介入OpenRouter,7月初單日Token突破10億,7月底重購MLX的連續Batching Prefail速度提升3-4倍,到8月成為了OpenRouter的付費Provider。",
      "tokens": [
        50765,
        35,
        809,
        25927,
        1546,
        11336,
        15353,
        31217,
        13127,
        14392,
        10251,
        11,
        38937,
        19,
        6939,
        5708,
        31699,
        11,
        21,
        6939,
        30312,
        14028,
        45569,
        49,
        23985,
        11,
        22,
        6939,
        28727,
        24227,
        6890,
        51,
        8406,
        40859,
        39560,
        3279,
        25459,
        11,
        22,
        6939,
        22816,
        12624,
        13352,
        120,
        12683,
        55,
        1546,
        36252,
        29583,
        33,
        29569,
        6001,
        69,
        864,
        31217,
        13127,
        20949,
        41670,
        18,
        12,
        19,
        35477,
        11,
        4511,
        23,
        6939,
        11336,
        6344,
        2289,
        45569,
        49,
        23985,
        1546,
        41469,
        50071,
        12681,
        85,
        1438,
        1543,
        51515
      ],
      "temperature": 0,
      "avg_logprob": -0.10623514118479259,
      "compression_ratio": 1.085987261146497,
      "no_speech_prob": 1.669749755128791e-11,
      "source": "whisper"
    },
    {
      "id": 4,
      "seek": 7756,
      "start": 77.56,
      "end": 88.56,
      "text": "今天最新數據顯示,Dark Bloom已經介入1380台機器,網絡聚合內存123.2TB,AR2大約是22.2萬美元,實現一周翻倍。",
      "tokens": [
        50365,
        12074,
        8661,
        12560,
        30622,
        36841,
        46605,
        25696,
        11,
        35,
        809,
        25927,
        25338,
        30312,
        14028,
        7668,
        4702,
        15433,
        17543,
        34386,
        11,
        26352,
        6948,
        94,
        8171,
        248,
        14245,
        28472,
        39781,
        4762,
        18,
        13,
        17,
        51,
        33,
        11,
        1899,
        17,
        3582,
        30889,
        1541,
        7490,
        13,
        17,
        14099,
        39433,
        11,
        10376,
        9581,
        2257,
        37390,
        42716,
        35477,
        1543,
        50915
      ],
      "temperature": 0,
      "avg_logprob": -0.08500791045854676,
      "compression_ratio": 1.0629629629629629,
      "no_speech_prob": 1.8627736506071635e-11,
      "source": "whisper"
    },
    {
      "id": 5,
      "seek": 7756,
      "start": 88.56,
      "end": 97.56,
      "text": "當然它的提升空間還是很大的,比如峰值利用率為15%,說明願意賣算力的機器比較多,而真實付費需求還沒有完全跟上。",
      "tokens": [
        50915,
        21707,
        45224,
        20949,
        41670,
        23322,
        11016,
        25583,
        4563,
        39156,
        11,
        36757,
        26555,
        108,
        40242,
        23700,
        9254,
        44866,
        6344,
        5211,
        8923,
        4622,
        11100,
        20692,
        9042,
        13352,
        96,
        19497,
        13486,
        1546,
        17543,
        34386,
        25174,
        6392,
        11,
        11070,
        6303,
        10376,
        41469,
        50071,
        32535,
        32718,
        7824,
        6963,
        37100,
        9678,
        5708,
        1543,
        51365
      ],
      "temperature": 0,
      "avg_logprob": -0.08500791045854676,
      "compression_ratio": 1.0629629629629629,
      "no_speech_prob": 1.8627736506071635e-11,
      "source": "whisper"
    },
    {
      "id": 6,
      "seek": 9756,
      "start": 97.56,
      "end": 108.56,
      "text": "另外,收益分佈其實並不均勻,比如前面說的那台M5Max,它一天是賺了4.11美元,同時還有另一台M324GB的Mac,是在8個小時內一個任務都沒有接到的。",
      "tokens": [
        50365,
        26202,
        11,
        18681,
        5419,
        232,
        6627,
        1593,
        230,
        14139,
        35934,
        1960,
        14872,
        229,
        7978,
        119,
        11,
        36757,
        8945,
        8833,
        4622,
        1546,
        4184,
        15433,
        44,
        20,
        36025,
        11,
        11284,
        2257,
        6135,
        1541,
        13352,
        118,
        2289,
        19,
        13,
        5348,
        39433,
        11,
        13089,
        6611,
        15569,
        22762,
        2257,
        15433,
        44,
        18,
        7911,
        8769,
        1546,
        34355,
        11,
        1541,
        3581,
        23,
        3338,
        46997,
        28472,
        8990,
        26443,
        39517,
        7182,
        6963,
        14468,
        4511,
        1546,
        1543,
        50915
      ],
      "temperature": 0,
      "avg_logprob": -0.16759314007229276,
      "compression_ratio": 1.1371158392434988,
      "no_speech_prob": 1.8087035341673996e-11,
      "source": "whisper"
    },
    {
      "id": 7,
      "seek": 9756,
      "start": 108.56,
      "end": 112.56,
      "text": "所以也許調度它更偏向高內存,認證狀態好的機器。",
      "tokens": [
        50915,
        7239,
        6404,
        40914,
        32943,
        13127,
        11284,
        19002,
        7437,
        237,
        24282,
        12979,
        28472,
        39781,
        11,
        22041,
        11823,
        231,
        18637,
        222,
        45248,
        20715,
        17543,
        34386,
        1543,
        51115
      ],
      "temperature": 0,
      "avg_logprob": -0.16759314007229276,
      "compression_ratio": 1.1371158392434988,
      "no_speech_prob": 1.8087035341673996e-11,
      "source": "whisper"
    },
    {
      "id": 8,
      "seek": 9756,
      "start": 112.56,
      "end": 118.56,
      "text": "再者,它的延遲、穩定性還有知識模型數量等,勢必會繼續完善。",
      "tokens": [
        51115,
        8623,
        12444,
        11,
        45224,
        17527,
        114,
        3330,
        110,
        1231,
        13740,
        102,
        12088,
        21686,
        15569,
        6498,
        43143,
        41908,
        39823,
        30622,
        26748,
        10187,
        11,
        7978,
        95,
        28531,
        6236,
        38459,
        14128,
        5234,
        226,
        1543,
        51415
      ],
      "temperature": 0,
      "avg_logprob": -0.16759314007229276,
      "compression_ratio": 1.1371158392434988,
      "no_speech_prob": 1.8087035341673996e-11,
      "source": "whisper"
    },
    {
      "id": 9,
      "seek": 9756,
      "start": 118.56,
      "end": 126.56,
      "text": "上周末,剛看到這個項目的時候,想起幾年前有研究過分佈式計算,Distributed Computing,而YouTuber Matthew Burman就這樣找到,",
      "tokens": [
        51415,
        5708,
        37390,
        1474,
        104,
        11,
        16940,
        18032,
        6287,
        8313,
        227,
        11386,
        20643,
        11,
        7093,
        9147,
        23575,
        49947,
        2412,
        23230,
        242,
        44704,
        8816,
        6627,
        1593,
        230,
        27584,
        30114,
        19497,
        11,
        35,
        468,
        2024,
        4866,
        37804,
        278,
        11,
        11070,
        3223,
        51,
        10261,
        12434,
        7031,
        1601,
        3111,
        8377,
        25085,
        4511,
        11,
        51815
      ],
      "temperature": 0,
      "avg_logprob": -0.16759314007229276,
      "compression_ratio": 1.1371158392434988,
      "no_speech_prob": 1.8087035341673996e-11,
      "source": "whisper"
    },
    {
      "id": 10,
      "seek": 12656,
      "start": 126.56,
      "end": 129.56,
      "text": "Dart Bloom稱為數據中心殺手。",
      "tokens": [
        50365,
        35,
        446,
        25927,
        49606,
        6344,
        30622,
        36841,
        5975,
        7945,
        45528,
        11389,
        1543,
        50515
      ],
      "temperature": 0,
      "avg_logprob": -0.19474613132761487,
      "compression_ratio": 1.1805929919137466,
      "no_speech_prob": 4.447453516576161e-11,
      "source": "whisper"
    },
    {
      "id": 11,
      "seek": 12656,
      "start": 129.56,
      "end": 130.56,
      "text": "他說,",
      "tokens": [
        50515,
        31592,
        11,
        50565
      ],
      "temperature": 0,
      "avg_logprob": -0.19474613132761487,
      "compression_ratio": 1.1805929919137466,
      "no_speech_prob": 4.447453516576161e-11,
      "source": "whisper"
    },
    {
      "id": 12,
      "seek": 12656,
      "start": 130.56,
      "end": 133.56,
      "text": "Just imagine we have a million of these nodes set up,",
      "tokens": [
        50565,
        12794,
        3811,
        321,
        362,
        257,
        2459,
        295,
        613,
        13891,
        992,
        493,
        11,
        50715
      ],
      "temperature": 0,
      "avg_logprob": -0.19474613132761487,
      "compression_ratio": 1.1805929919137466,
      "no_speech_prob": 4.447453516576161e-11,
      "source": "whisper"
    },
    {
      "id": 13,
      "seek": 12656,
      "start": 133.56,
      "end": 140.56,
      "text": "That is major inference power that anybody can tap into at a fraction of the price of going to an open AI or an anthropic,",
      "tokens": [
        50715,
        6390,
        307,
        2563,
        38253,
        1347,
        300,
        4472,
        393,
        5119,
        666,
        412,
        257,
        14135,
        295,
        264,
        3218,
        295,
        516,
        281,
        364,
        1269,
        7318,
        420,
        364,
        22727,
        299,
        11,
        51065
      ],
      "temperature": 0,
      "avg_logprob": -0.19474613132761487,
      "compression_ratio": 1.1805929919137466,
      "no_speech_prob": 4.447453516576161e-11,
      "source": "whisper"
    },
    {
      "id": 14,
      "seek": 12656,
      "start": 140.56,
      "end": 144.56,
      "text": "and it's completely distributed,it's completely built by regular people。",
      "tokens": [
        51065,
        474,
        309,
        311,
        2584,
        12631,
        11,
        270,
        311,
        2584,
        3094,
        538,
        3890,
        561,
        1543,
        51265
      ],
      "temperature": 0,
      "avg_logprob": -0.19474613132761487,
      "compression_ratio": 1.1805929919137466,
      "no_speech_prob": 4.447453516576161e-11,
      "source": "whisper"
    },
    {
      "id": 15,
      "seek": 12656,
      "start": 144.56,
      "end": 153.56,
      "text": "當推理需求暴漲,而開源模型、MOE和電話技術等,又正好把一部分能力壓到了Mac、工作站還有AIPC這類邊緣設備上,",
      "tokens": [
        51265,
        13118,
        33597,
        13876,
        32535,
        32718,
        23826,
        112,
        14065,
        110,
        11,
        11070,
        8949,
        47402,
        41908,
        39823,
        1231,
        18976,
        36,
        12565,
        20545,
        11103,
        32502,
        9890,
        241,
        10187,
        11,
        17047,
        15789,
        2131,
        16075,
        2257,
        32174,
        8225,
        13486,
        45964,
        21381,
        34355,
        1231,
        41315,
        34155,
        15569,
        32,
        9139,
        34,
        2664,
        40092,
        14901,
        12608,
        96,
        39035,
        20854,
        5708,
        11,
        51715
      ],
      "temperature": 0,
      "avg_logprob": -0.19474613132761487,
      "compression_ratio": 1.1805929919137466,
      "no_speech_prob": 4.447453516576161e-11,
      "source": "whisper"
    },
    {
      "id": 16,
      "seek": 15356,
      "start": 153.56,
      "end": 159.56,
      "text": "那麼原本散落在千家萬戶的沉睡算力,就有了被組織成公共基礎設施的可能。",
      "tokens": [
        50365,
        20504,
        19683,
        8802,
        7017,
        96,
        30848,
        3581,
        20787,
        5155,
        14099,
        1486,
        114,
        1546,
        3308,
        231,
        40490,
        19497,
        13486,
        11,
        3111,
        2412,
        2289,
        23238,
        30052,
        23141,
        242,
        11336,
        13545,
        15408,
        26008,
        14971,
        236,
        39035,
        4307,
        121,
        1546,
        16657,
        1543,
        50665
      ],
      "temperature": 0,
      "avg_logprob": -0.10341348057299589,
      "compression_ratio": 1.184873949579832,
      "no_speech_prob": 3.154735153665378e-11,
      "source": "whisper"
    },
    {
      "id": 17,
      "seek": 15356,
      "start": 159.56,
      "end": 170.56,
      "text": "數據中心仍會守住訓練和高并發推理,但那些隱私敏感突發的常委的成本敏感的推理需求,完全可以流向一張有可驗證邊緣設備組成的網路。",
      "tokens": [
        50665,
        30622,
        36841,
        5975,
        7945,
        1550,
        235,
        6236,
        48480,
        21632,
        5396,
        241,
        47027,
        12565,
        12979,
        3509,
        114,
        14637,
        33597,
        13876,
        11,
        8395,
        4184,
        13824,
        10673,
        109,
        20083,
        7017,
        237,
        9709,
        40859,
        14637,
        1546,
        11279,
        44230,
        1546,
        11336,
        8802,
        7017,
        237,
        9709,
        1546,
        33597,
        13876,
        32535,
        32718,
        11,
        37100,
        6723,
        27854,
        24282,
        2257,
        18889,
        2412,
        4429,
        24023,
        245,
        11823,
        231,
        14901,
        12608,
        96,
        39035,
        20854,
        30052,
        11336,
        1546,
        26352,
        24658,
        1543,
        51215
      ],
      "temperature": 0,
      "avg_logprob": -0.10341348057299589,
      "compression_ratio": 1.184873949579832,
      "no_speech_prob": 3.154735153665378e-11,
      "source": "whisper"
    },
    {
      "id": 18,
      "seek": 17056,
      "start": 170.56,
      "end": 177.56,
      "text": "最後介紹一下DarkGloom的核心開發者Gajesh,他7歲開始寫代碼,10歲給創業公司做聊天機器人,",
      "tokens": [
        50365,
        20578,
        30312,
        38193,
        8861,
        35,
        809,
        38,
        752,
        298,
        1546,
        8987,
        116,
        7945,
        8949,
        14637,
        12444,
        38,
        1805,
        14935,
        11,
        5000,
        22,
        26256,
        21017,
        4510,
        104,
        19105,
        16337,
        120,
        11,
        3279,
        26256,
        17798,
        5935,
        113,
        27119,
        13545,
        32981,
        10907,
        40096,
        6135,
        17543,
        34386,
        4035,
        11,
        50715
      ],
      "temperature": 0,
      "avg_logprob": -0.15280311847555225,
      "compression_ratio": 1.1226993865030674,
      "no_speech_prob": 2.7072077218859292e-11,
      "source": "whisper"
    },
    {
      "id": 19,
      "seek": 17056,
      "start": 177.56,
      "end": 186.56,
      "text": "13歲創建了一個基於區塊鏈的加密貨幣理財管理工具,資產管理規模一度達到700萬美元,15歲加入EigenLabs,",
      "tokens": [
        50715,
        7668,
        26256,
        5935,
        113,
        34157,
        2289,
        8990,
        26008,
        19488,
        44526,
        26268,
        46241,
        230,
        1546,
        9990,
        47838,
        11561,
        101,
        3509,
        96,
        13876,
        11561,
        94,
        23131,
        13876,
        23323,
        39806,
        11,
        35380,
        33299,
        23131,
        13876,
        2888,
        237,
        41908,
        2257,
        13127,
        33086,
        4511,
        18197,
        14099,
        39433,
        11,
        5211,
        26256,
        43878,
        36,
        3213,
        43,
        17243,
        11,
        51165
      ],
      "temperature": 0,
      "avg_logprob": -0.15280311847555225,
      "compression_ratio": 1.1226993865030674,
      "no_speech_prob": 2.7072077218859292e-11,
      "source": "whisper"
    },
    {
      "id": 20,
      "seek": 17056,
      "start": 186.56,
      "end": 191.56,
      "text": "今年Gajesh干滿18歲,致力於打造開放網絡,加速開放智能。",
      "tokens": [
        51165,
        38937,
        38,
        1805,
        14935,
        26111,
        32197,
        6494,
        26256,
        11,
        6784,
        112,
        13486,
        19488,
        12467,
        37583,
        8949,
        12744,
        26352,
        6948,
        94,
        11,
        9990,
        31217,
        8949,
        12744,
        5094,
        118,
        8225,
        1543,
        51415
      ],
      "temperature": 0,
      "avg_logprob": -0.15280311847555225,
      "compression_ratio": 1.1226993865030674,
      "no_speech_prob": 2.7072077218859292e-11,
      "source": "whisper"
    },
    {
      "id": 21,
      "seek": 17056,
      "start": 191.56,
      "end": 193.56,
      "text": "這期節目就到這裡,Thanks for listening!",
      "tokens": [
        51415,
        2664,
        16786,
        42567,
        45918,
        18771,
        11,
        27917,
        337,
        4764,
        0,
        51515
      ],
      "temperature": 0,
      "avg_logprob": -0.15280311847555225,
      "compression_ratio": 1.1226993865030674,
      "no_speech_prob": 2.7072077218859292e-11,
      "source": "whisper"
    },
    {
      "id": 22,
      "seek": 19356,
      "start": 193.56,
      "end": 195.56,
      "text": "我是Kyoli,Thanks for listening!",
      "tokens": [
        50365,
        15914,
        42,
        88,
        9384,
        11,
        27917,
        337,
        4764,
        0,
        50465
      ],
      "temperature": 0,
      "avg_logprob": -1.0108482042948406,
      "compression_ratio": 0.8048780487804879,
      "no_speech_prob": 9.534304457048393e-10,
      "source": "whisper"
    }
  ],
  "transcript_selection": {
    "schema": "videonote.transcript_selection.v1",
    "status": "SELECTED",
    "requested_language": "zh",
    "duration": 195.999625,
    "reference_path": null,
    "reference_kind": null,
    "reference_language": null,
    "selected_source": "whisper_segmented",
    "decision_reason": "source_unavailable_or_untrusted_whisper_passed",
    "whisper_rejected": false,
    "source_profile": {
      "segment_count": 0,
      "duration_coverage": 0.0,
      "usable_full": false,
      "usable_partial": false,
      "hallucination_region_count": 0,
      "hallucination_regions": [],
      "language": {
        "requested": "zh",
        "matched": false
      }
    },
    "whisper_profile": {
      "segment_count": 23,
      "multilingual": false,
      "first_start": 0.0,
      "final_end": 195.56,
      "duration_coverage": 0.997757,
      "maximum_gap_seconds": 0.0,
      "inverted_count": 0,
      "language": {
        "requested": "zh",
        "matched": true,
        "latin": 504,
        "han": 766,
        "kana": 0,
        "hangul": 0
      },
      "hallucination_region_count": 0,
      "hallucination_regions": [],
      "usable_full": true,
      "usable_partial": true
    },
    "comparison": {
      "window_seconds": 30.0,
      "compared_window_count": 0,
      "median_score": 0.0,
      "divergent_window_count": 0,
      "divergent_ratio": 0.0,
      "divergent_windows": []
    },
    "timestamp_alignment": {
      "applied": false,
      "estimated_offset_seconds": 0.0,
      "matched_segment_count": 0,
      "match_scores": []
    },
    "caption_timing_repair": null,
    "filled_source_profile": null,
    "whisper_duration_guard": {
      "media_duration": 196.0,
      "rejected_segment_count": 0,
      "rejected_segments": []
    }
  }
}