kenarsa

Unfortunately, we only offer Ubuntu x86_64 at the moment. We are planning to add more platforms (Mac and Windows). I make a note of your request and add it to our todo list. Thank you.

Thanks for the question. You can use our first product: https://github.com/Picovoice/Porcupine. It is a voice-control (wake-word) engine. It allows you to detect multiple keywords (phrases) in the audio stream in real time with no delay and it fully runs on-device. No cloud connection required.

Great question. I believe that someone has already performed this measurement on a variety of could APIs (Google, Amazon. MS. etc.). I remember seeing it on GitHub while ago. I can't find it right now with a simple Google search. But I will spend more time later in the evening and comment here when I have it.

The comments are absolutely correct. Could services (can) do better simply because they have access to more compute resources and also data (what is sent to them can be/is used for training later). There are situations where on-device is preferred due to privacy reasons, latency, cost, or lack of internet connection.

Hello, sorry to interrupt the conversation. I am Alireza. I am the founder of Picovoice (maker of Cheetah).

Thanks for mentioning the quote I heard it from few friends who used to work in Nuance. But didn't know where it came from originally.

We have a very small engineering team who deeply understands machine learning and (embedded) software. I have invented/co-invented few US patents in speech processing/recognition prior to Picovoice. But have no academic background in linguistics. Having worked with computational linguists, they are definitely a solid plus to the team when their skill set is utilized correctly. I appreciate your question and curiosity. We hopefully will provide more information about the team on our website soon. Thank you.

Thanks for the link. I'll be sure to look into it. It is impressive.

A disclaimer is that we use the valid train portion of CV as part of our training set. But it is less than 10% of the train set (in terms of hours). Also, we do not employ an LM mainly because the systems we are targeting do not have enough storage for a strong LM (usually the storage on them maxes out at 64 MB). Cheetah is an end-to-end acoustic model. For later versions, we might be able to add a well-pruned LM for specific domains with limited vocabulary to boost the accuracy with limited storage available.

I fully agree with your points. I am taking notes here as I think we should follow up on a couple of your suggestions. Scaling up to DeepSpeech model size can be a bit tricky as it would require much more compute resources (GPU). But should be quite doable with time and budget.

Thanks again for your comments and suggestions. As you correctly pointed out our main focus is the very low resource (CPU/Memory) embedded systems.

Thanks for the comment. Not currently at the moment. But we are going to provide more information on picovoice.ai in coming days.

Thanks for the comment. Just wanted to quickly clarify that the model is available here: https://github.com/Picovoice/stt-benchmark/tree/master/resou...

That is a good point. Definitely a valid roadmap and something we should consider. Thank you for the suggestion.

If you use the CPU that we used for testing yes. It's an Intel CPU. The detail is in readme. You can do 30 streams per core. If you have 2 cores you can do 60.

We used entire librispeech as part of our training set.

I agree with your point. That is why we open sourced the benchmark so you can verify it yourself. You just need an Ubuntu machine with python 3.6. I appreciate the question.

This is a bit of surprise to me as well. That being said. The dataset is a tough one. I am ESL, but some of the accented examples I have a hard time understanding. Also the recordings are not near field and there is sometime some background noise.

Thanks for comment. We don't train on the dataset being tested on. I am fairly certain the other engines don't as well.