{
  "text": "你敢信吗?Github上跟Whisper相关的开源项目光星星过千的就有十几个,每个都说自己更快更强,但我全跑了一遍,真正能打的没几个。\n先说一个前提,我选Whisper的核心原因只有一个,多语言之耻。中文、英文、日文、韩文都得能识别。如果只做中文,我根本不会选Whisper,翻译S2它不香吗?但多语言这个需求目前只有Whisper能扛。\n先说几个不适合实时场景的。Whisper Fintune用LoRA微调,训练成本低,还做了三端部署,工程完整度很高。\n但它是离线识别,先录完再转,流式识别用不了。Whisper GX速度快70倍,但峰值性能依赖TPU,普通GPU上优势没那么多。\nBuzz是桌面App,不适合后端部署。\n再说几个功能聚焦,但场景窄的。Faster Whisper用C-Translate 2加速,速度是原版4倍,内存更低,支持8位量化。\n行业里用得非常广,但它只是推理引擎,不提供流式输出,你得自己组合其他工具。\nYT Whisper只做YouTube字幕,场景太窄。Whisper Transparent只做时间戳,没有加速和部署。\n实时转录那一批呢?Whisper Mic场景最贴合,麦克风输入实时转录,但只支持英文。\nWhisper Real Time和Whisper Live思路类似,都是边录边识别,但都没有加速优化,中文延迟偏高。\nWhisper Realization倒是厉害,能识别谁在什么时候说了什么,但串了四五个模型,太重了。\n然后是两个重量级选手,Whisper X,综合评价最高。\nLarge V3做70倍实时速度,不到8GB显存就能跑。\n关键是支持单词级时间戳,每个字精确对应到音频时间位置,做流式展示和字幕对齐,这个能力太重要了,短期方案它是最稳的。\n另一个是Distil Whisper,把Large V3蒸馏成小模型,速度快六倍,体积小一半,错误率只涨不到1%,性价比太炸了。\n但知名问题是,只发布了英文模型,其他语言得自己蒸馏。\n门槛是有的,但走通了长期收益最大。\n所以方向很清晰,多语言需求选Whisper没毛病,但具体用哪个项目得看你的场景。\n短期上Whisper X最稳,长期自蒸馏Distil Whisper收益最大。\n选项目不是选最好的,是选最合适的。",
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      "text": "你敢信吗?Github上跟Whisper相关的开源项目光星星过千的就有十几个,每个都说自己更快更强,但我全跑了一遍,真正能打的没几个。",
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      "source": "whisper"
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    {
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      "text": "先说一个前提,我选Whisper的核心原因只有一个,多语言之耻。中文、英文、日文、韩文都得能识别。如果只做中文,我根本不会选Whisper,翻译S2它不香吗?但多语言这个需求目前只有Whisper能扛。",
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      "text": "先说几个不适合实时场景的。Whisper Fintune用LoRA微调,训练成本低,还做了三端部署,工程完整度很高。",
      "tokens": [
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      "source": "whisper",
      "text_original": "先说几个不适合实时场景的。Whisper Finiton用LORAV条,训练成本低,还做了三端部署,工程完整度很高。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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      "text": "但它是离线识别,先录完再转,流式识别用不了。Whisper GX速度快70倍,但峰值性能依赖TPU,普通GPU上优势没那么多。",
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      "source": "whisper",
      "text_original": "但它是离线识别,先录完再转,流续识别用不了。Whisper GX速度快70倍,但峰值性能依赖TPU,普通GPU上优势没那么多。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
      "id": 4,
      "seek": 2516,
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      "end": 45.74,
      "text": "Buzz是桌面App,不适合后端部署。",
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      "source": "whisper"
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    {
      "id": 5,
      "seek": 2516,
      "start": 46.84,
      "end": 55.0,
      "text": "再说几个功能聚焦,但场景窄的。Faster Whisper用C-Translate 2加速,速度是原版4倍,内存更低,支持8位量化。",
      "tokens": [
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    {
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      "seek": 5516,
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      "text": "行业里用得非常广,但它只是推理引擎,不提供流式输出,你得自己组合其他工具。",
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      "source": "whisper",
      "text_original": "行业里用得非常广,但它只是推理引擎,不提供流适输出,你得自己组合其他工具。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
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      "seek": 5516,
      "start": 60.879999999999995,
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      "text": "YT Whisper只做YouTube字幕,场景太窄。Whisper Transparent只做时间戳,没有加速和部署。",
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    {
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      "text": "实时转录那一批呢?Whisper Mic场景最贴合,麦克风输入实时转录,但只支持英文。",
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      "text": "Whisper Real Time和Whisper Live思路类似,都是边录边识别,但都没有加速优化,中文延迟偏高。",
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      "text": "Whisper Realization倒是厉害,能识别谁在什么时候说了什么,但串了四五个模型,太重了。",
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      "text": "然后是两个重量级选手,Whisper X,综合评价最高。",
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      "text": "Large V3做70倍实时速度,不到8GB显存就能跑。",
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      "text_original": "Large VR做70倍实时速度,不到8GB显存就能跑。",
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      "text": "关键是支持单词级时间戳,每个字精确对应到音频时间位置,做流式展示和字幕对齐,这个能力太重要了,短期方案它是最稳的。",
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      "text_original": "关键是支持单词及时间戳,每个字精确对应到音频时间位置,做流逝展示和字幕对齐,这个能力太重要了,短期方案它是最稳的。",
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      "text": "另一个是Distil Whisper,把Large V3蒸馏成小模型,速度快六倍,体积小一半,错误率只涨不到1%,性价比太炸了。",
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      "text_original": "另一个是Distil Whisper,把Large VR蒸留成小模型,速度快六倍,体积小一半,错误率只涨不到1%,性价比太炸了。",
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    {
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      "text": "但知名问题是,只发布了英文模型,其他语言得自己蒸馏。",
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      "text_original": "但知名问题是,只发布了英文模型,其他语言得自己蒸留。",
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    {
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      "text": "门槛是有的,但走通了长期收益最大。",
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      "text": "所以方向很清晰,多语言需求选Whisper没毛病,但具体用哪个项目得看你的场景。",
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      "text": "短期上Whisper X最稳,长期自蒸馏Distil Whisper收益最大。",
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      "text": "选项目不是选最好的,是选最合适的。",
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