openWakeWord VS CTranslate2

https://github.com/dscripka/openWakeWord

Balancing wake reliability vs false wake activation is a tricky balance. OWW is decent but could certainly be better.

It's used with Home Assistant now so I expect the training data and implementation overall to get significantly better fairly soon.

There's also OpenWakeWord[0]. The models are readily available in tflite and ONNX formats and are impressively "light" in terms of compute requirements and performance.

It should be possible.

[0] - https://github.com/dscripka/openWakeWord

hey, quick question. do you mind if I use your stft function in the speech preprocessing library I've been working on? we've been trying to add support for doing mel spectrograms to build a runner for openwakeword, but progress is pretty slow because I've been soloing something I really don't have the right background for(I've never directly studied or worked with signal processing)

potentially build another runner for open wakeword

here's what's on the pipeline next: - finish mel-spectrogram implementation - publish initial version on crates - move python caching rust side - finish implementing in the precise rust port - potentially build another runner for (open wakeword)[https://github.com/dscripka/openWakeWord] - build an android app that supports user-defined wakewords and has some popular defaults to load. ps not a voice assistant, just the thing that activates the voice assistant.

Just a point of clarification - faster-whisper references it but ctranslate2[0] is what's really doing the magic here.

Ctranslate2 is a sleeper powerhouse project that enables a lot. They should be up front and center and get the credit they deserve.

[0] - https://github.com/OpenNMT/CTranslate2

We'd love to move beyond Nvidia.

The issue (among others) is we achieve the speech recognition performance we do largely thanks to ctranslate2[0]. They've gone on the record saying that they essentially have no interest in ROCm[1].

Of course with open source anything is possible but we see this as being one of several fundamental issues in supporting AMD GPGPU hardware.

[0] - https://github.com/OpenNMT/CTranslate2

[1] - https://github.com/OpenNMT/CTranslate2/issues/1072

> While I agree that it's much more effort to get things working on AMD cards than it is with Nvidia, I was a bit surprised to see this comment mention Whisper being an example of "5-10x as performant".

It easily is. See the benchmarks[0] from faster-whisper which uses Ctranslate2. That's 5x faster than OpenAI reference code on a Tesla V100. Needless to say something like a 4080 easily multiplies that.

> https://www.tomshardware.com/news/whisper-audio-transcriptio... is a good example of Nvidia having no excuses being double the price when it comes to Whisper inference, with 7900XTX being directly comparable with 4080, albeit with higher power draw. To be fair it's not using ROCm but Direct3D 11, but for performance/price arguments sake that detail is not relevant.

With all due respect to the author of the article this is "my first entry into ML" territory. They talk about a 5-10 second delay, my project can do sub 1 second times[1] even with ancient GPUs thanks to Ctranslate2. I don't have an RTX 4080 but if you look at the performance stats for the closest thing (RTX 4090) the performance numbers are positively bonkers - completely untouchable for anything ROCm based. Same goes for the other projects I linked, lmdeploy does over 100 tokens/s in a single session with LLama2 13b on my RTX 4090 and almost 600 tokens/s across eight simultaneous sessions.

> EDIT: Also using CTranslate2 as an example is not great as it's actually a good showcase why ROCm is so far behind CUDA: It's all about adapting the tech and getting the popular libraries to support it. Things usually get implemented in CUDA first and then would need additional effort to add ROCm support that projects with low amount of (possibly hobbyist) maintainers might not have available. There's even an issue in CTranslate2 where they clearly state no-one is working to get ROCm supported in the library. ( https://github.com/OpenNMT/CTranslate2/issues/1072#issuecomm... )

I don't understand what you're saying here. It (along with the other projects I linked) are fantastic examples of just how far behind the ROCm ecosystem is. ROCm isn't even on the radar for most of them as your linked issue highlights.

Things always get implemented in CUDA first (ten years in this space and I've never seen ROCm first) and ROCm users either wait months (minimum) for sub-par performance or never get it at all.

[0] - https://github.com/guillaumekln/faster-whisper#benchmark

[1] - https://heywillow.io/components/willow-inference-server/#ben...

Etc.

Now, what this allows you to do is reuse the attention computed from the previous turns (since the prefix is the same).

In practice, people often have a system prompt before the conversation history, which (as far a I can tell) makes this technique not applicable (the input prefix will change as soon as the conversation history is long enough that we need to start dropping the oldest turns).

In such case, what you could do is to cache at least the system prompt. This is also possible with https://github.com/OpenNMT/CTranslate2/blob/2203ad5c8baf878a...

The original Whisper implementation from OpenAI uses the PyTorch deep learning framework. On the other hand, faster-whisper is implemented using CTranslate2 [1] which is a custom inference engine for Transformer models. So basically it is running the same model but using another backend, which is specifically optimized for inference workloads.

[1] https://github.com/OpenNMT/CTranslate2

FLAN-T5 models generally perform well for their size, but they are encode-decoder models, and they aren't as widely supported for efficient inference. I wanted students to be able to run everything locally on CPU, so I was ideally hoping for something that supported quantization for CPU inference. I explored llama.cpp and GGML, but ultimately landed on ctranslate2 for inference.

You can also check out the CTranslate2 library which supports efficient inference of T5 models, including 8-bit quantization on CPU and GPU. There is a usage example in the documentation.