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hello this is daniel povey and today
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we're asking him can we fine-tune asr
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models in cauldi by training it on more
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audio files
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okay
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so fine tuning is a little bit of a weak
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spot in calde and i'll try to explain
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the reasons why so
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firstly fine tuning means that we
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already have a train model
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and we want to train it on some other
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data that maybe better matches our like
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domain
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uh and the general idea
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with
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is that you train using a lower learning
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rate because you don't want to destroy
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the already learned parameters
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now
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unfortunately this generally hasn't
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worked too well in calde
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we have a few recipes that are supposed
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to do this but the
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the performance is just so so
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uh
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later in the video maybe we'll add some
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links to the recipes uh
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i i suspect that one of the reasons it
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doesn't work so well is because our
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normal models with
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tdnns they have
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batch norm layers in them
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and
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i believe what happens
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is that when you train on different type
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of data the batch norm stats like are
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different they don't match
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and this means that right from the start
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the model is performing worse because in
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effect it's uh
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you're normalizing use it using a
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different data distribution than when
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you're originally trained
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now in principle one fix for this would
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be to train the original model with
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without batching on by replacing it with
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normalized layer in keldi which is
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something like layer norm
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but that would require that you return
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the original model in general
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i recommend that people simply train
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from scratch
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using a mixture of the original training
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data and the in-domain data if that's
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possible
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thank you
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bye
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bye