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.
Your measuring wall time, not CPU time. It may be that they are similar, but I'd suspect you aren't loading the worker nodes well. If the savings are from the reduced shuffles & serde, it's probably something you can measure. I'd be curious to see the findings.
I'm not against using simple methods where appropriate. 95% of the companies out there probably do not need frameworks like spark. I think the main argument against them is operational complexity though, not the compute overhead.
When you talk between remote machines, you have to translate to a format that can transmitted and distributed between machines(serialization). You then have to undo at the other end(deserialization). If what you are sending along is just a few floats, that can be very cheap. If you're sending along a large nested dictionary or even a full program, not so much.
Imagine an example where you have two arrays of 1 billion numbers, and you want to add them pairwise. You could use spark to do that by having each "task" be a single addition. But the time it would take to structure and transmit the 1 billion requests will be many multiples of the amount of time it would take to just do the additions.
The difference in throughput for local versus distributed orchestration would mainly come from serdes, networking, switching. Serdes can be substantial. Networking and switching has been aggressively offloaded from CPU through better hardware support.
Individual tasks would definitely have better latency, but I'd suspect the impact on throughput/CPU usage might be muted. Of course at the extremes (very small jobs, very large/complex objects being passed) you'd see big gains.
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- 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?