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Tom Hanks 最小的孩子楚門·漢克(1995年生)受 numberphile 影响已经变成斯坦福数学 bachelor 了 :ablobowo:

@drq @killyourfm let's move to actually open platforms instead of building workarounds for broken platforms and hoping for the best?

@lucyli 训练好的英语模型有树莓派/手机用的三十 MB 小模型, 电脑用几百 MB 的大模型准确率更高

@lucyli 感觉开源软件识别率比 ios 语音输入高多了, pip 安装一个 alphacephei.com/vosk/install , 单机运行的代码反应还快, 方便判断单个词的识别成功率, 不用等几秒看☁返回的最终结果. 我最近用这个项目搞的效果: fars.ee/HJGo.mp4

wzhd boosted

@lucyli @nebula_moe@moe.cat 记好螺丝的长短和位置, 曾经一颗长螺丝放错孔把下面的电路板刮花了, 好在还能用🤣

才发现 octodon 把 pawoo 整个屏蔽了 :ablobspin:

@pcjustin 看到 measurement 感觉是在做统计, 统统屏蔽了

@jakob I find the test_microphone.py example a good place to start, sample rate and format are handled, so I don't need to worry about converting codec or getting timing wrong. I think the first time I talked into the microphone, words got recognized one by one with little latency, also the partial result sometimes changes to make it more likely to be a English sentence

@jakob I was surprised to find that DeepSpeech doesn't seem to be using of a good language model, as it frequently produces obscure words and even weird combination of letters. At last, I found vosk from alphacep. It's quite accurate even when used by a non-native speaker :ablobowo:

@FiveYellowMice 是什么软件呢? 📱语音识别的效果经常让我觉得就像 UDP 丢包了一样, 虽然它们不用 udp

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