Very nice work. Training these from scratch is a big undertaking.
- Did you train the encoder & decoder together or separately? It would be nice to have the encoder representation be compatible with the existing whisper implementation since it would mean you could swap your implementation into models where its used as a component, like in the recent Voxtral model. I'd imagine it also might make training a bit faster as well.
- Did you consider training the turbo model as well?
EPA range tends to be pessimistic for EVs as it assumes you are always traveling at highway speeds. Even small reductions in speeds can make EVs much more efficient since drag is quadratic. A quick google search shows Prius prime owners reporting 4-5.5 miles/kwh, so the 3-6 mile range is entirely plausible.
You can buy stock in a solar "yieldco", which is exactly this. It holds the assets of solar farms and pays out the cashflows. There were a lot circa 2018, I believe Brookfield bought up a bunch, so there may be fewer options now.
It's a very simple change in a vanilla python implementation. The encoder is a set of attention blocks, and the length of the attention can be changed without changing the calculation at all.
Here(https://github.com/openai/whisper/blob/main/whisper/model.py...) is the relevant code in the whisper repo. You'd just need to change the for loop to an enumerate and subsample the context along its length at the point you want. I believe it would be:
for i, block in enumerate(self.blocks):
x = block(x)
if i==4:
x = x[,,::2]
If you are hosting whisper yourself, you can do something slightly more elegant, but with the same effect. You can downsample/pool the context 2:1 (or potentially more) a few layers into the encoder. That allows you to do the equivalent of speeding up audio without worry about potential spectral losses. For whisper large v3, that gets you nearly double throughput in exchange for a relative ~4% WER increase.
Most of this decline isn't driven by a change in current fertility rates, but instead by a persistent trend downward in number of reproductive-aged adults. That was locked in by the fertility rates 20-40 years ago. These things move in the timescale of decades. Even if policy were reasonably successful, it would be a quarter of a century before things stabilized.
- Did you train the encoder & decoder together or separately? It would be nice to have the encoder representation be compatible with the existing whisper implementation since it would mean you could swap your implementation into models where its used as a component, like in the recent Voxtral model. I'd imagine it also might make training a bit faster as well.
- Did you consider training the turbo model as well?